1
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Tang R, Guo S, Wang K, Lin H, Huang L, Mou G. A framework of insole blanking robot based on adaptive edge detection and FSPS-BIT* path planning. Sci Rep 2024; 14:20791. [PMID: 39251697 PMCID: PMC11385949 DOI: 10.1038/s41598-024-71636-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2024] [Accepted: 08/29/2024] [Indexed: 09/11/2024] Open
Abstract
Insole blanking production technology plays a vital role in contemporary machining and manufacturing industries. Existing insole blanking production models have limitations because most robots are required to accurately position the workpiece to a predetermined location, and special auxiliary equipment is usually required to ensure the precise positioning of the robot. In this paper, we present an adaptive blanking robotic system for different lighting environments, which consists of an industrial robot arm, an RGB-D camera configuration, and a customized insole blanking table and mold. We introduce an innovative edge detection framework that utilizes color features and morphological parameters optimized through particle swarm optimization (PSO) techniques to Adaptive recognition of insole edge contours. A path planning framework based on FSPS-BIT* is also introduced, which integrates the BIT* algorithm with the FSPS algorithm for efficient path planning of the robotic arm.
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Affiliation(s)
- Rui Tang
- College of Advanced Manufacturing, Fuzhou University, Jinjiang, 362200, China
| | - Shirong Guo
- Faculty of Engineering, Monash University, Wellington Road, Clayton, VIC, 3800, Australia.
| | - Kunfu Wang
- College of Advanced Manufacturing, Fuzhou University, Jinjiang, 362200, China
| | - Hongdi Lin
- College of Advanced Manufacturing, Fuzhou University, Jinjiang, 362200, China
| | - Lujin Huang
- College of Advanced Manufacturing, Fuzhou University, Jinjiang, 362200, China
| | - Gang Mou
- College of Advanced Manufacturing, Fuzhou University, Jinjiang, 362200, China.
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2
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Szczepanski R, Erwinski K, Tejer M, Daab D. Optimal Path Planning Algorithm with Built-In Velocity Profiling for Collaborative Robot. SENSORS (BASEL, SWITZERLAND) 2024; 24:5332. [PMID: 39205026 PMCID: PMC11359329 DOI: 10.3390/s24165332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/26/2024] [Revised: 08/07/2024] [Accepted: 08/09/2024] [Indexed: 09/04/2024]
Abstract
This paper proposes a method for solving the path planning problem for a collaborative robot. The time-optimal, smooth, collision-free B-spline path is obtained by the application of a nature-inspired optimization algorithm. The proposed approach can be especially useful when moving items that are delicate or contain a liquid in an open container using a robotic arm. The goal of the optimization is to obtain the shortest execution time of the production cycle, taking into account the velocity, velocity and jerk limits, and the derivative continuity of the final trajectory. For this purpose, the velocity profiling algorithm for B-spline paths is proposed. The methodology has been applied to the production cycle optimization of the pick-and-place process using a collaborative robot. In comparison with point-to-point movement and the solution provided by the RRT* algorithm with the same velocity profiling to ensure the same motion limitations, the proposed path planning algorithm decreased the entire production cycle time by 11.28% and 57.5%, respectively. The obtained results have been examined in a simulation with the entire production cycle visualization. Moreover, the smoothness of the movement of the robotic arm has been validated experimentally using a robotic arm.
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Affiliation(s)
- Rafal Szczepanski
- Department of Automatics and Measurement Systems, Institute of Engineering and Technology, Faculty of Physics Astronomy and Informatics, Nicolaus Copernicus University, Wilenska 7, 87-100 Torun, Poland; (K.E.); (M.T.)
| | - Krystian Erwinski
- Department of Automatics and Measurement Systems, Institute of Engineering and Technology, Faculty of Physics Astronomy and Informatics, Nicolaus Copernicus University, Wilenska 7, 87-100 Torun, Poland; (K.E.); (M.T.)
| | - Mateusz Tejer
- Department of Automatics and Measurement Systems, Institute of Engineering and Technology, Faculty of Physics Astronomy and Informatics, Nicolaus Copernicus University, Wilenska 7, 87-100 Torun, Poland; (K.E.); (M.T.)
| | - Dominika Daab
- Department of Geomatics and Cartography, Faculty of Earth Sciences and Spatial Management, Nicolaus Copernicus University, Lwowska 1, 87-100 Torun, Poland;
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3
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Monfaredi R, Concepcion-Gonzalez A, Acosta Julbe J, Fischer E, Hernandez-Herrera G, Cleary K, Oluigbo C. Automatic Path-Planning Techniques for Minimally Invasive Stereotactic Neurosurgical Procedures-A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2024; 24:5238. [PMID: 39204935 PMCID: PMC11359713 DOI: 10.3390/s24165238] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Revised: 08/05/2024] [Accepted: 08/08/2024] [Indexed: 09/04/2024]
Abstract
This review systematically examines the recent research from the past decade on diverse path-planning algorithms tailored for stereotactic neurosurgery applications. Our comprehensive investigation involved a thorough search of scholarly papers from Google Scholar, PubMed, IEEE Xplore, and Scopus, utilizing stringent inclusion and exclusion criteria. The screening and selection process was meticulously conducted by a multidisciplinary team comprising three medical students, robotic experts with specialized knowledge in path-planning techniques and medical robotics, and a board-certified neurosurgeon. Each selected paper was reviewed in detail, and the findings were synthesized and reported in this review. The paper is organized around three different types of intervention tools: straight needles, steerable needles, and concentric tube robots. We provide an in-depth analysis of various path-planning algorithms applicable to both single and multi-target scenarios. Multi-target planning techniques are only discussed for straight tools as there is no published work on multi-target planning for steerable needles and concentric tube robots. Additionally, we discuss the imaging modalities employed, the critical anatomical structures considered during path planning, and the current status of research regarding its translation to clinical human studies. To the best of our knowledge and as a conclusion from this systematic review, this is the first review paper published in the last decade that reports various path-planning techniques for different types of tools for minimally invasive neurosurgical applications. Furthermore, this review outlines future trends and identifies existing technology gaps within the field. By highlighting these aspects, we aim to provide a comprehensive overview that can guide future research and development in path planning for stereotactic neurosurgery, ultimately contributing to the advancement of safer and more effective neurosurgical procedures.
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Affiliation(s)
- Reza Monfaredi
- Sheikh Zayed Institute of Pediatrics Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (E.F.); (K.C.)
- Department of Pediatrics and Radiology, George Washington University, Washington, DC 20037, USA
| | - Alondra Concepcion-Gonzalez
- School of Medicine and Health Sciences, George Washington University School of Medicine, Washington, DC 20052, USA;
| | - Jose Acosta Julbe
- Department of Orthopaedic Surgery & Orthopaedic and Arthritis Center for Outcomes Research, Brigham and Women’s Hospital, Boston, MA 02115, USA;
| | - Elizabeth Fischer
- Sheikh Zayed Institute of Pediatrics Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (E.F.); (K.C.)
| | | | - Kevin Cleary
- Sheikh Zayed Institute of Pediatrics Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (E.F.); (K.C.)
- Department of Pediatrics and Radiology, George Washington University, Washington, DC 20037, USA
| | - Chima Oluigbo
- Sheikh Zayed Institute of Pediatrics Surgical Innovation, Children’s National Hospital, Washington, DC 20010, USA; (E.F.); (K.C.)
- Department of Neurology and Pediatrics, George Washington University School of Medicine, Washington, DC 20052, USA
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4
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Xia T, Chen H. A Survey of Autonomous Vehicle Behaviors: Trajectory Planning Algorithms, Sensed Collision Risks, and User Expectations. SENSORS (BASEL, SWITZERLAND) 2024; 24:4808. [PMID: 39123854 PMCID: PMC11314818 DOI: 10.3390/s24154808] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/01/2024] [Revised: 07/19/2024] [Accepted: 07/22/2024] [Indexed: 08/12/2024]
Abstract
Autonomous vehicles are rapidly advancing and have the potential to revolutionize transportation in the future. This paper primarily focuses on vehicle motion trajectory planning algorithms, examining the methods for estimating collision risks based on sensed environmental information and approaches for achieving user-aligned trajectory planning results. It investigates the different categories of planning algorithms within the scope of local trajectory planning applications for autonomous driving, discussing and differentiating their properties in detail through a review of the recent studies. The risk estimation methods are classified and introduced based on their descriptions of the sensed collision risks in traffic environments and their integration with trajectory planning algorithms. Additionally, various user experience-oriented methods, which utilize human data to enhance the trajectory planning performance and generate human-like trajectories, are explored. The paper provides comparative analyses of these algorithms and methods from different perspectives, revealing the interconnections between these topics. The current challenges and future prospects of the trajectory planning tasks in autonomous vehicles are also discussed.
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Affiliation(s)
| | - Hui Chen
- School of Automotive Studies, Tongji University, Shanghai 201804, China;
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5
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Li X, Li G, Bian Z. Research on Autonomous Vehicle Path Planning Algorithm Based on Improved RRT* Algorithm and Artificial Potential Field Method. SENSORS (BASEL, SWITZERLAND) 2024; 24:3899. [PMID: 38931683 PMCID: PMC11207524 DOI: 10.3390/s24123899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/13/2024] [Accepted: 06/13/2024] [Indexed: 06/28/2024]
Abstract
For the RRT* algorithm, there are problems such as greater randomness, longer time consumption, more redundant nodes, and inability to perform local obstacle avoidance when encountering unknown obstacles in the path planning process of autonomous vehicles. And the artificial potential field method (APF) applied to autonomous vehicles is prone to problems such as local optimality, unreachable targets, and inapplicability to global scenarios. A fusion algorithm combining the improved RRT* algorithm and the improved artificial potential field method is proposed. First of all, for the RRT* algorithm, the concept of the artificial potential field and probability sampling optimization strategy are introduced, and the adaptive step size is designed according to the road curvature. The path post-processing of the planned global path is carried out to reduce the redundant nodes of the generated path, enhance the purpose of sampling, solve the problem where oscillation may occur when expanding near the target point, reduce the randomness of RRT* node sampling, and improve the efficiency of path generation. Secondly, for the artificial potential field method, by designing obstacle avoidance constraints, adding a road boundary repulsion potential field, and optimizing the repulsion function and safety ellipse, the problem of unreachable targets can be solved, unnecessary steering in the path can be reduced, and the safety of the planned path can be improved. In the face of U-shaped obstacles, virtual gravity points are generated to solve the local minimum problem and improve the passing performance of the obstacles. Finally, the fusion algorithm, which combines the improved RRT* algorithm and the improved artificial potential field method, is designed. The former first plans the global path, extracts the path node as the temporary target point of the latter, guides the vehicle to drive, and avoids local obstacles through the improved artificial potential field method when encountered with unknown obstacles, and then smooths the path planned by the fusion algorithm, making the path satisfy the vehicle kinematic constraints. The simulation results in the different road scenes show that the method proposed in this paper can quickly plan a smooth path that is more stable, more accurate, and suitable for vehicle driving.
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Affiliation(s)
| | - Gang Li
- School of Automobile and Traffic Engineering, Liaoning University of Technology, Jinzhou 121001, China; (X.L.); (Z.B.)
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6
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Xu T. Recent advances in Rapidly-exploring random tree: A review. Heliyon 2024; 10:e32451. [PMID: 38961991 PMCID: PMC11219357 DOI: 10.1016/j.heliyon.2024.e32451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2024] [Revised: 05/15/2024] [Accepted: 06/04/2024] [Indexed: 07/05/2024] Open
Abstract
Path planning is an crucial research area in robotics. Compared to other path planning algorithms, the Rapidly-exploring Random Tree (RRT) algorithm possesses both search and random sampling properties, and thus has more potential to generate high-quality paths that can balance the global optimum and local optimum. This paper reviews the research on RRT-based improved algorithms from 2021 to 2023, including theoretical improvements and application implementations. At the theoretical level, branching strategy improvement, sampling strategy improvement, post-processing improvement, and model-driven RRT are highlighted, at the application level, application scenarios of RRT under welding robots, assembly robots, search and rescue robots, surgical robots, free-floating space robots, and inspection robots are detailed, and finally, many challenges faced by RRT at both the theoretical and application levels are summarized. This review suggests that although RRT-based improved algorithms has advantages in large-scale scenarios, real-time performance, and uncertain environments, and some strategies that are difficult to be quantitatively described can be designed based on model-driven RRT, RRT-based improved algorithms still suffer from the problems of difficult to design the hyper-parameters and weak generalization, and in the practical application level, the reliability and accuracy of the hardware such as controllers, actuators, sensors, communication, power supply and data acquisition efficiency all pose challenges to the long-term stability of RRT in large-scale unstructured scenarios. As a part of autonomous robots, the upper limit of RRT path planning performance also depends on the robot localization and scene modeling performance, and there are still architectural and strategic choices in multi-robot collaboration, in addition to the ethics and morality that has to be faced. To address the above issues, I believe that multi-type robot collaboration, human-robot collaboration, real-time path planning, self-tuning of hyper-parameters, task- or application-scene oriented algorithms and hardware design, and path planning in highly dynamic environments are future trends.
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Affiliation(s)
- Tong Xu
- School of Information Technology, Jiangsu Open University, Nanjing, 210000, China
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7
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Hua J, Su Y, Xin D, Guo W. A High-Precision Hand-Eye Coordination Localization Method under Convex Relaxation Optimization. SENSORS (BASEL, SWITZERLAND) 2024; 24:3830. [PMID: 38931614 PMCID: PMC11207387 DOI: 10.3390/s24123830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/06/2024] [Accepted: 06/11/2024] [Indexed: 06/28/2024]
Abstract
Traditional switching operations require on-site work, and the high voltage generated by arc discharges can pose a risk of injury to the operator. Therefore, a combination of visual servo and robot control is used to localize the switching operation and construct hand-eye calibration equations. The solution to the hand-eye calibration equations is coupled with the rotation matrix and translation vectors, and it depends on the initial value determination. This article presents a convex relaxation global optimization hand-eye calibration algorithm based on dual quaternions. Firstly, the problem model is simplified using the mathematical tools of dual quaternions, and then the linear matrix inequality convex optimization method is used to obtain a rotation matrix with higher accuracy. Afterwards, the calibration equations of the translation vectors are rewritten, and a new objective function is established to solve the coupling influence between them, maintaining positioning precision at approximately 2.9 mm. Considering the impact of noise on the calibration process, Gaussian noise is added to the solutions of the rotation matrix and translation vector to make the data more closely resemble the real scene in order to evaluate the performance of different hand-eye calibration algorithms. Eventually, an experiment comparing different hand-eye calibration methods proves that the proposed algorithm is better than other hand-eye calibration algorithms in terms of calibration accuracy, robustness to noise, and stability, satisfying the accuracy requirements of switching operations.
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Affiliation(s)
- Jin Hua
- School of Electronic Information Engineering, Xi’an Technological University, Xi’an 710021, China; (Y.S.); (D.X.); (W.G.)
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8
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Kotsinis D, Bechlioulis CP. Decentralized Navigation with Optimality for Multiple Holonomic Agents in Simply Connected Workspaces. SENSORS (BASEL, SWITZERLAND) 2024; 24:3134. [PMID: 38793989 PMCID: PMC11125295 DOI: 10.3390/s24103134] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/31/2024] [Revised: 04/30/2024] [Accepted: 05/13/2024] [Indexed: 05/26/2024]
Abstract
Multi-agent systems are utilized more often in the research community and industry, as they can complete tasks faster and more efficiently than single-agent systems. Therefore, in this paper, we are going to present an optimal approach to the multi-agent navigation problem in simply connected workspaces. The task involves each agent reaching its destination starting from an initial position and following an optimal collision-free trajectory. To achieve this, we design a decentralized control protocol, defined by a navigation function, where each agent is equipped with a navigation controller that resolves imminent safety conflicts with the others, as well as the workspace boundary, without requesting knowledge about the goal position of the other agents. Our approach is rendered sub-optimal, since each agent owns a predetermined optimal policy calculated by a novel off-policy iterative method. We use this method because the computational complexity of learning-based methods needed to calculate the global optimal solution becomes unrealistic as the number of agents increases. To achieve our goal, we examine how much the yielded sub-optimal trajectory deviates from the optimal one and how much time the multi-agent system needs to accomplish its task as we increase the number of agents. Finally, we compare our method results with a discrete centralized policy method, also known as a Multi-Agent Poli-RRT* algorithm, to demonstrate the validity of our method when it is attached to other research algorithms.
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Affiliation(s)
- Dimitrios Kotsinis
- Division of Systems and Automatic Control, Department of Electrical and Computer Engineering, University of Patras, Rio, 26504 Patras, Greece;
- Athena Research Center, Robotics Institute, Artemidos 6 & Epidavrou, 15125 Maroussi, Greece
| | - Charalampos P. Bechlioulis
- Division of Systems and Automatic Control, Department of Electrical and Computer Engineering, University of Patras, Rio, 26504 Patras, Greece;
- Athena Research Center, Robotics Institute, Artemidos 6 & Epidavrou, 15125 Maroussi, Greece
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9
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Chu L, Wang Y, Li S, Guo Z, Du W, Li J, Jiang Z. Intelligent Vehicle Path Planning Based on Optimized A* Algorithm. SENSORS (BASEL, SWITZERLAND) 2024; 24:3149. [PMID: 38794003 PMCID: PMC11125652 DOI: 10.3390/s24103149] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2024] [Revised: 05/12/2024] [Accepted: 05/12/2024] [Indexed: 05/26/2024]
Abstract
With the rapid development of the intelligent driving technology, achieving accurate path planning for unmanned vehicles has become increasingly crucial. However, path planning algorithms face challenges when dealing with complex and ever-changing road conditions. In this paper, aiming at improving the accuracy and robustness of the generated path, a global programming algorithm based on optimization is proposed, while maintaining the efficiency of the traditional A* algorithm. Firstly, turning penalty function and obstacle raster coefficient are integrated into the search cost function to increase the adaptability and directionality of the search path to the map. Secondly, an efficient search strategy is proposed to solve the problem that trajectories will pass through sparse obstacles while reducing spatial complexity. Thirdly, a redundant node elimination strategy based on discrete smoothing optimization effectively reduces the total length of control points and paths, and greatly reduces the difficulty of subsequent trajectory optimization. Finally, the simulation results, based on real map rasterization, highlight the advanced performance of the path planning and the comparison among the baselines and the proposed strategy showcases that the optimized A* algorithm significantly enhances the security and rationality of the planned path. Notably, it reduces the number of traversed nodes by 84%, the total turning angle by 39%, and shortens the overall path length to a certain extent.
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Affiliation(s)
| | | | | | | | | | | | - Zewei Jiang
- State Key Laboratory of Automotive Simulation and Control, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China; (L.C.); (Y.W.); (S.L.); (Z.G.); (W.D.); (J.L.)
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10
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Ye L, Li J, Li P. Improving path planning for mobile robots in complex orchard environments: the continuous bidirectional Quick-RRT* algorithm. FRONTIERS IN PLANT SCIENCE 2024; 15:1337638. [PMID: 38803601 PMCID: PMC11128624 DOI: 10.3389/fpls.2024.1337638] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 04/22/2024] [Indexed: 05/29/2024]
Abstract
Efficient obstacle-avoidance path planning is critical for orchards with numerous irregular obstacles. This paper presents a continuous bidirectional Quick-RRT* (CBQ-RRT*) algorithm based on the bidirectional RRT (Bi-RRT) and Quick-RRT* algorithms and proposes an expansion cost function that evaluates path smoothness and length to overcome the limitations of the Quick-RRT* algorithm for non-holonomic mobile robot applications. To improve the zigzag between dual trees caused by the dual-tree expansion of the Bi-RRT algorithm, CBQ-RRT* proposes the CreateConnectNode optimization method, which effectively solves the path smoothness problem at the junction of dual trees. Simulations conducted on the ROS platform showed that the CBQ-RRT* outperformed the unidirectional Quick-RRT* in terms of efficiency for various orchard layouts and terrain conditions. Compared to Bi-RRT*, CBQ-RRT* reduced the average path length and maximum heading angle by 8.5% and 21.7%, respectively. In addition, field tests confirmed the superior performance of the CBQ-RRT*, as evidenced by an average maximum path lateral error of 0.334 m, a significant improvement over Bi-RRT* and Quick-RRT*. These improvements demonstrate the effectiveness of the CBQ-RRT* in complex orchard environments.
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Affiliation(s)
- Lei Ye
- School of Intelligent Engineering, Shaoguan University, Shaoguan, China
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11
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Osmani K, Schulz D. Comprehensive Investigation of Unmanned Aerial Vehicles (UAVs): An In-Depth Analysis of Avionics Systems. SENSORS (BASEL, SWITZERLAND) 2024; 24:3064. [PMID: 38793917 PMCID: PMC11125140 DOI: 10.3390/s24103064] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 05/01/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024]
Abstract
The evolving technologies regarding Unmanned Aerial Vehicles (UAVs) have led to their extended applicability in diverse domains, including surveillance, commerce, military, and smart electric grid monitoring. Modern UAV avionics enable precise aircraft operations through autonomous navigation, obstacle identification, and collision prevention. The structures of avionics are generally complex, and thorough hierarchies and intricate connections exist in between. For a comprehensive understanding of a UAV design, this paper aims to assess and critically review the purpose-classified electronics hardware inside UAVs, each with the corresponding performance metrics thoroughly analyzed. This review includes an exploration of different algorithms used for data processing, flight control, surveillance, navigation, protection, and communication. Consequently, this paper enriches the knowledge base of UAVs, offering an informative background on various UAV design processes, particularly those related to electric smart grid applications. As a future work recommendation, an actual relevant project is openly discussed.
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Affiliation(s)
| | - Detlef Schulz
- Department of Electrical Engineering, Helmut Schmidt University, 22043 Hamburg, Germany;
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12
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Baek C, Cho K. Deep Learning-Enhanced Sampling-Based Path Planning for LTL Mission Specifications. SENSORS (BASEL, SWITZERLAND) 2024; 24:2998. [PMID: 38793854 PMCID: PMC11125380 DOI: 10.3390/s24102998] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2024] [Revised: 05/02/2024] [Accepted: 05/03/2024] [Indexed: 05/26/2024]
Abstract
The presented paper introduces a novel path planning algorithm designed for generating low-cost trajectories that fulfill mission requirements expressed in Linear Temporal Logic (LTL). The proposed algorithm is particularly effective in environments where cost functions encompass the entire configuration space. A core contribution of this paper is the presentation of a refined approach to sampling-based path planning algorithms that aligns with the specified mission objectives. This enhancement is achieved through a multi-layered framework approach, enabling a simplified discrete abstraction without relying on mesh decomposition. This abstraction is especially beneficial in complex or high-dimensional environments where mesh decomposition is challenging. The discrete abstraction effectively guides the sampling process, influencing the selection of vertices for extension and target points for steering in each iteration. To further improve efficiency, the algorithm incorporates a deep learning-based extension, utilizing training data to accurately model the optimal trajectory distribution between two points. The effectiveness of the proposed method is demonstrated through simulated tests, which highlight its ability to identify low-cost trajectories that meet specific mission criteria. Comparative analyses also confirm the superiority of the proposed method compared to existing methods.
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Affiliation(s)
| | - Kyunghoon Cho
- Department of Information and Telecommunication Engineering, Incheon National University, Incheon 22012, Republic of Korea;
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13
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Karwowski J, Szynkiewicz W, Niewiadomska-Szynkiewicz E. Bridging Requirements, Planning, and Evaluation: A Review of Social Robot Navigation. SENSORS (BASEL, SWITZERLAND) 2024; 24:2794. [PMID: 38732900 PMCID: PMC11086376 DOI: 10.3390/s24092794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Revised: 04/21/2024] [Accepted: 04/24/2024] [Indexed: 05/13/2024]
Abstract
Navigation lies at the core of social robotics, enabling robots to navigate and interact seamlessly in human environments. The primary focus of human-aware robot navigation is minimizing discomfort among surrounding humans. Our review explores user studies, examining factors that cause human discomfort, to perform the grounding of social robot navigation requirements and to form a taxonomy of elementary necessities that should be implemented by comprehensive algorithms. This survey also discusses human-aware navigation from an algorithmic perspective, reviewing the perception and motion planning methods integral to social navigation. Additionally, the review investigates different types of studies and tools facilitating the evaluation of social robot navigation approaches, namely datasets, simulators, and benchmarks. Our survey also identifies the main challenges of human-aware navigation, highlighting the essential future work perspectives. This work stands out from other review papers, as it not only investigates the variety of methods for implementing human awareness in robot control systems but also classifies the approaches according to the grounded requirements regarded in their objectives.
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Affiliation(s)
| | | | - Ewa Niewiadomska-Szynkiewicz
- Institute of Control and Computation Engineering, Warsaw University of Technology, 00-665 Warsaw, Poland; (J.K.); (W.S.)
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14
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Fan H, Huang J, Huang X, Zhu H, Su H. BI-RRT*: An improved path planning algorithm for secure and trustworthy mobile robots systems. Heliyon 2024; 10:e26403. [PMID: 38455527 PMCID: PMC10918008 DOI: 10.1016/j.heliyon.2024.e26403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 02/01/2024] [Accepted: 02/13/2024] [Indexed: 03/09/2024] Open
Abstract
The optimal RRT in elliptic space sampling (Informed-RRT*) is an extension of RRT that provides asymptotic optimality, however, it experiences gradual progress and close to obstacles. In the paper, we propose a novel path planning algorithm guided bidirectional Informed-RRT* (BI-RRT*), that introduces extension range, dual-direction exploration, and refinement in trajectory design. The growth range refers to maintaining an additional area from the obstacle to enhance the dependability of the path through preventing impacts. Bidirectional search is a search strategy using both start and target points for a initial solution. Smoothing improves path robustness by using cubic spline. Furthermore, simulation tests for the BI-RRT* algorithm are executed, and the efficacy of the suggested algorithm is confirmed through its application in a robot operating system (ROS). Simulations and experimental tests verify that the proposed algorithm improves the path planning capability. We emphasize the importance of safety, privacy, and reliability in the deployment of AI systems. Our algorithm ensures that the planned paths maintain a safe distance from obstacles, reducing the risk of collisions. Additionally, we prioritize privacy by adhering to data protection regulations and implementing secure communication protocols within the AI system. Moreover, we have applied rigorous testing and validation processes to enhance the reliability of our algorithm, ensuring consistent and accurate path planning outcomes.
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Affiliation(s)
- Honghui Fan
- School of Computer Engineering, Jiangsu University of Technology, ChangZhou, JiangSu, China
| | - Jiahe Huang
- School of Mechanical Engineering, Jiangsu University of Technology, ChangZhou, JiangSu, China
| | - Xianzhen Huang
- School of Mechanical Engineering, Jiangsu University of Technology, ChangZhou, JiangSu, China
| | - Hongjin Zhu
- School of Computer Engineering, Jiangsu University of Technology, ChangZhou, JiangSu, China
| | - Huachang Su
- School of Computer Science, Nanjing University of Information Science and Technology, NanJing, JiangSu, China
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15
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Hoeller D, Rudin N, Sako D, Hutter M. ANYmal parkour: Learning agile navigation for quadrupedal robots. Sci Robot 2024; 9:eadi7566. [PMID: 38478592 DOI: 10.1126/scirobotics.adi7566] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 02/16/2024] [Indexed: 10/11/2024]
Abstract
Performing agile navigation with four-legged robots is a challenging task because of the highly dynamic motions, contacts with various parts of the robot, and the limited field of view of the perception sensors. Here, we propose a fully learned approach to training such robots and conquer scenarios that are reminiscent of parkour challenges. The method involves training advanced locomotion skills for several types of obstacles, such as walking, jumping, climbing, and crouching, and then using a high-level policy to select and control those skills across the terrain. Thanks to our hierarchical formulation, the navigation policy is aware of the capabilities of each skill, and it will adapt its behavior depending on the scenario at hand. In addition, a perception module was trained to reconstruct obstacles from highly occluded and noisy sensory data and endows the pipeline with scene understanding. Compared with previous attempts, our method can plan a path for challenging scenarios without expert demonstration, offline computation, a priori knowledge of the environment, or taking contacts explicitly into account. Although these modules were trained from simulated data only, our real-world experiments demonstrate successful transfer on hardware, where the robot navigated and crossed consecutive challenging obstacles with speeds of up to 2 meters per second.
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Affiliation(s)
- David Hoeller
- Robotic Systems Lab, ETH Zurich, Zurich, Switzerland
- NVIDIA, Zurich, Switzerland
| | - Nikita Rudin
- Robotic Systems Lab, ETH Zurich, Zurich, Switzerland
- NVIDIA, Zurich, Switzerland
| | - Dhionis Sako
- Robotic Systems Lab, ETH Zurich, Zurich, Switzerland
| | - Marco Hutter
- Robotic Systems Lab, ETH Zurich, Zurich, Switzerland
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16
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George BM, Arya S, G S S, Bharadwaj K, N S V. Robotic Archwire Bending in Orthodontics: A Review of the Literature. Cureus 2024; 16:e56611. [PMID: 38646270 PMCID: PMC11032650 DOI: 10.7759/cureus.56611] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2024] [Accepted: 03/19/2024] [Indexed: 04/23/2024] Open
Abstract
Malocclusion is a widespread oral health issue that adversely affects individuals' health and well-being. Currently, fixed orthodontics is considered the most efficient treatment for correcting malocclusion, with archwire bending playing a key role in orthodontic treatment. Traditionally, orthodontists manually performed archwire bending using various handheld pliers and other mechanical tools, requiring a significant amount of time, precision, and specialized training yet being unable to guarantee appliance accuracy. The process of shaping orthodontic wire is challenging due to its high stiffness and superelasticity, resulting in a time-consuming, laborious process that is prone to human errors. With advancements in orthodontics, traditional methods have taken a backseat, making way for innovative technologies that provide more accurate and personalized treatment options. The continuous efforts to enhance treatment efficiency, accuracy, efficacy, and patient experience have led to the integration of robotics into various orthodontic procedures. The use of robotics in archwire bending represents a breakthrough in orthodontics, offering unparalleled precision, consistency, and efficiency. This technology reduces treatment time and patient discomfort, overcoming the limitations of manual bending and enhancing orthodontic treatment overall. Hence, the present study aims to review the literature on robotic archwire bending in orthodontics, including their drawbacks and their impact on orthodontic treatment.
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Affiliation(s)
- Babitha Merin George
- Orthodontics and Dentofacial Orthopaedics, RajaRajeswari Dental College & Hospital, Bengaluru, IND
| | - Siddarth Arya
- Orthodontics and Dentofacial Orthopaedics, RajaRajeswari Dental College & Hospital, Bengaluru, IND
| | - Shwetha G S
- Orthodontics and Dentofacial Orthopaedics, RajaRajeswari Dental College & Hospital, Bengaluru, IND
| | - Keerthana Bharadwaj
- Orthodontics and Dentofacial Orthopaedics, RajaRajeswari Dental College & Hospital, Bengaluru, IND
| | - Vaishnavi N S
- Orthodontics and Dentofacial Orthopaedics, RajaRajeswari Dental College & Hospital, Bengaluru, IND
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17
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Sayar E, Gao X, Hu Y, Chen G, Knoll A. Toward coordinated planning and hierarchical optimization control for highly redundant mobile manipulator. ISA TRANSACTIONS 2024; 146:16-28. [PMID: 38228436 DOI: 10.1016/j.isatra.2024.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Revised: 12/12/2023] [Accepted: 01/05/2024] [Indexed: 01/18/2024]
Abstract
This paper represents a constraint planning and optimization control scheme for a highly redundant mobile manipulator considering a complex indoor environment. Compared with the traditional optimization solution of a redundant manipulator, infinity norm and slack variable are additionally introduced and leveraged by the optimization algorithm. The former takes into account the joint limits effectively by considering individual joint velocities and the latter relaxes the equality constraint by decreasing the infeasible solution area. By using derived kinematic equations, the tracking control problem is expressed as an optimization problem and converted into a new quadratic programming (QP) problem. To address the optimization problem, the two-timescale recurrent neural networks optimization scheme is proposed and tested with a 9 DOFs nonholonomic mobile-based manipulator. Additionally, the BI2RRT∗ path-planning algorithm incorporates path planning in the complex environment where different obstacles are positioned. To test and evaluate the proposed optimization scheme, both predefined and generated paths are tested in the Neurorobotics Platform (NRP) 2which is open access and open source integrative simulation framework powered by Gazebo and developed by our team.
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Affiliation(s)
- Erdi Sayar
- School of Computation, Information and Technology, Technical University of Munich, Munich, 85748, Germany.
| | - Xiang Gao
- School of Computation, Information and Technology, Technical University of Munich, Munich, 85748, Germany.
| | - Yingbai Hu
- School of Computation, Information and Technology, Technical University of Munich, Munich, 85748, Germany; Multi-Scale Medical Robotics Centre, The Chinese University of Hong Kong, Hong Kong, China; Chow Yuk Ho Technology Centre for Innovative Medicine, The Chinese University of Hong Kong, Hong Kong, China.
| | - Guang Chen
- School of Computation, Information and Technology, Technical University of Munich, Munich, 85748, Germany; School of Automotive Engineering and the Department of Computer Science, Tongji University, Shanghai, China.
| | - Alois Knoll
- School of Computation, Information and Technology, Technical University of Munich, Munich, 85748, Germany.
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18
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Wang H, Zhou X, Li J, Yang Z, Cao L. Improved RRT* Algorithm for Disinfecting Robot Path Planning. SENSORS (BASEL, SWITZERLAND) 2024; 24:1520. [PMID: 38475056 DOI: 10.3390/s24051520] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2023] [Revised: 02/13/2024] [Accepted: 02/20/2024] [Indexed: 03/14/2024]
Abstract
In this paper, an improved APF-GFARRT* (artificial potential field-guided fuzzy adaptive rapidly exploring random trees) algorithm based on APF (artificial potential field) guided sampling and fuzzy adaptive expansion is proposed to solve the problems of weak orientation and low search success rate when randomly expanding nodes using the RRT (rapidly exploring random trees) algorithm for disinfecting robots in the dense environment of disinfection operation. Considering the inherent randomness of tree growth in the RRT* algorithm, a combination of APF with RRT* is introduced to enhance the purposefulness of the sampling process. In addition, in the context of RRT* facing dense and restricted environments such as narrow passages, adaptive step-size adjustment is implemented using fuzzy control. It accelerates the algorithm's convergence and improves search efficiency in a specific area. The proposed algorithm is validated and analyzed in a specialized environment designed in MATLAB, and comparisons are made with existing path planning algorithms, including RRT, RRT*, and APF-RRT*. Experimental results show the excellent exploration speed of the improved algorithm, reducing the average initial path search time by about 46.52% compared to the other three algorithms. In addition, the improved algorithm exhibits faster convergence, significantly reducing the average iteration count and the average final path cost by about 10.01%. The algorithm's enhanced adaptability in unique environments is particularly noteworthy, increasing the chances of successfully finding paths and generating more rational and smoother paths than other algorithms. Experimental results validate the proposed algorithm as a practical and feasible solution for similar problems.
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Affiliation(s)
- Haotian Wang
- Mechanical Engineering College, Beihua University, Jilin 132021, China
| | - Xiaolong Zhou
- Mechanical Engineering College, Beihua University, Jilin 132021, China
| | - Jianyong Li
- Mechanical Engineering College, Beihua University, Jilin 132021, China
| | - Zhilun Yang
- Mechanical Engineering College, Beihua University, Jilin 132021, China
| | - Linlin Cao
- Mechanical Engineering College, Beihua University, Jilin 132021, China
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19
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Zhou Q, Feng H, Liu Y. Multigene and Improved Anti-Collision RRT* Algorithms for Unmanned Aerial Vehicle Task Allocation and Route Planning in an Urban Air Mobility Scenario. Biomimetics (Basel) 2024; 9:125. [PMID: 38534810 DOI: 10.3390/biomimetics9030125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Revised: 02/14/2024] [Accepted: 02/19/2024] [Indexed: 03/28/2024] Open
Abstract
Compared to terrestrial transportation systems, the expansion of urban traffic into airspace can not only mitigate traffic congestion, but also foster establish eco-friendly transportation networks. Additionally, unmanned aerial vehicle (UAV) task allocation and trajectory planning are essential research topics for an Urban Air Mobility (UAM) scenario. However, heterogeneous tasks, temporary flight restriction zones, physical buildings, and environment prerequisites put forward challenges for the research. In this paper, multigene and improved anti-collision RRT* (IAC-RRT*) algorithms are proposed to address the challenge of task allocation and path planning problems in UAM scenarios by tailoring the chance of crossover and mutation. It is proved that multigene and IAC-RRT* algorithms can effectively minimize energy consumption and tasks' completion duration of UAVs. Simulation results demonstrate that the strategy of this work surpasses traditional optimization algorithms, i.e., RRT algorithm and gene algorithm, in terms of numerical stability and convergence speed.
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Affiliation(s)
- Qiang Zhou
- School of Electronic and Information Engineering, Beihang University, 37 XueYuan Road, Haidian District, Beijing 100191, China
| | - Houze Feng
- School of Electronic and Information Engineering, Beihang University, 37 XueYuan Road, Haidian District, Beijing 100191, China
| | - Yueyang Liu
- School of Electronic and Information Engineering, Beihang University, 37 XueYuan Road, Haidian District, Beijing 100191, China
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20
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Yun WJ, Shin M, Mohaisen D, Lee K, Kim J. Hierarchical Deep Reinforcement Learning-Based Propofol Infusion Assistant Framework in Anesthesia. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:2510-2521. [PMID: 35853065 DOI: 10.1109/tnnls.2022.3190379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
This article aims to provide a hierarchical reinforcement learning (RL)-based solution to the automated drug infusion field. The learning policy is divided into the tasks of: 1) learning trajectory generative model and 2) planning policy model. The proposed deep infusion assistant policy gradient (DIAPG) model draws inspiration from adversarial autoencoders (AAEs) and learns latent representations of hypnotic depth trajectories. Given the trajectories drawn from the generative model, the planning policy infers a dose of propofol for stable sedation of a patient under total intravenous anesthesia (TIVA) using propofol and remifentanil. Through extensive evaluation, the DIAPG model can effectively stabilize bispectral index (BIS) and effect site concentration given a potentially time-varying target sequence. The proposed DIAPG shows an increased performance of 530% and 15% when a human expert and a standard reinforcement algorithm are used to infuse drugs, respectively.
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21
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Rousseas P, Bechlioulis C, Kyriakopoulos K. Reactive optimal motion planning for a class of holonomic planar agents using reinforcement learning with provable guarantees. Front Robot AI 2024; 10:1255696. [PMID: 38234864 PMCID: PMC10791867 DOI: 10.3389/frobt.2023.1255696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Accepted: 10/20/2023] [Indexed: 01/19/2024] Open
Abstract
In control theory, reactive methods have been widely celebrated owing to their success in providing robust, provably convergent solutions to control problems. Even though such methods have long been formulated for motion planning, optimality has largely been left untreated through reactive means, with the community focusing on discrete/graph-based solutions. Although the latter exhibit certain advantages (completeness, complicated state-spaces), the recent rise in Reinforcement Learning (RL), provides novel ways to address the limitations of reactive methods. The goal of this paper is to treat the reactive optimal motion planning problem through an RL framework. A policy iteration RL scheme is formulated in a consistent manner with the control-theoretic results, thus utilizing the advantages of each approach in a complementary way; RL is employed to construct the optimal input without necessitating the solution of a hard, non-linear partial differential equation. Conversely, safety, convergence and policy improvement are guaranteed through control theoretic arguments. The proposed method is validated in simulated synthetic workspaces, and compared against reactive methods as well as a PRM and an RRT⋆ approach. The proposed method outperforms or closely matches the latter methods, indicating the near global optimality of the former, while providing a solution for planning from anywhere within the workspace to the goal position.
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Affiliation(s)
- Panagiotis Rousseas
- Control Systems Laboratory, School of Mechanical Engineering, National Technical University of Athens, Athens, Greece
| | - Charalampos Bechlioulis
- Division of Systems and Control, Department of Electrical and Computer Engineering, University of Patras, Patras, Greece
| | - Kostas Kyriakopoulos
- Center of AI & Robotics (CAIR), New York University, Abu Dhabi, United Arab Emirates
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22
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Marcucci T, Petersen M, von Wrangel D, Tedrake R. Motion planning around obstacles with convex optimization. Sci Robot 2023; 8:eadf7843. [PMID: 37967206 DOI: 10.1126/scirobotics.adf7843] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Accepted: 10/19/2023] [Indexed: 11/17/2023]
Abstract
From quadrotors delivering packages in urban areas to robot arms moving in confined warehouses, motion planning around obstacles is a core challenge in modern robotics. Planners based on optimization can design trajectories in high-dimensional spaces while satisfying the robot dynamics. However, in the presence of obstacles, these optimization problems become nonconvex and very hard to solve, even just locally. Thus, when facing cluttered environments, roboticists typically fall back to sampling-based planners that do not scale equally well to high dimensions and struggle with continuous differential constraints. Here, we present a framework that enables convex optimization to efficiently and reliably plan trajectories around obstacles. Specifically, we focus on collision-free motion planning with costs and constraints on the shape, the duration, and the velocity of the trajectory. Using recent techniques for finding shortest paths in Graphs of Convex Sets (GCS), we design a practical convex relaxation of the planning problem. We show that this relaxation is typically very tight, to the point that a cheap postprocessing of its solution is almost always sufficient to identify a collision-free trajectory that is globally optimal (within the parameterized class of curves). Through numerical and hardware experiments, we demonstrate that our planner, which we name GCS, can find better trajectories in less time than widely used sampling-based algorithms and can reliably design trajectories in high-dimensional complex environments.
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Affiliation(s)
- Tobia Marcucci
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Mark Petersen
- School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - David von Wrangel
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
| | - Russ Tedrake
- Department of Electrical Engineering and Computer Science, MIT, Cambridge, MA, USA
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23
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Lin S, Liu A, Wang J. A Dual-Layer Weight-Leader-Vicsek Model for Multi-AGV Path Planning in Warehouse. Biomimetics (Basel) 2023; 8:549. [PMID: 37999190 PMCID: PMC10669162 DOI: 10.3390/biomimetics8070549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 11/11/2023] [Accepted: 11/14/2023] [Indexed: 11/25/2023] Open
Abstract
Multiple automatic guided vehicles are widely involved in industrial intelligence. Path planning is crucial for their successful application. However, achieving robust and efficient path planning of multiple automatic guided vehicles for real-time implementation is challenging. In this paper, we propose a two-layer strategy for multi-vehicle path planning. The approach aims to provide fast computation and operation efficiency for implementation. The start-destination matrix groups all the vehicles, generating a dynamic virtual leader for each group. In the first layer, the hybrid A* algorithm is employed for the path planning of the virtual leaders. The second layer is named leader-follower; the proposed Weight-Leader-Vicsek model is applied to navigate the vehicles following their virtual leaders. The proposed method can reduce computational load and achieve real-time navigation by quickly updating the grouped vehicles' status. Collision and deadlock avoidance is also conducted in this model. Vehicles in different groups are treated as dynamic obstacles. We validated the method by conducted simulations through MATLAB to verify its path-planning functionality and experimented with a localization sensor.
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Affiliation(s)
| | | | - Jianguo Wang
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia; (S.L.); (A.L.)
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24
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Luo S, Zhang M, Zhuang Y, Ma C, Li Q. A survey of path planning of industrial robots based on rapidly exploring random trees. Front Neurorobot 2023; 17:1268447. [PMID: 38023457 PMCID: PMC10654791 DOI: 10.3389/fnbot.2023.1268447] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 09/29/2023] [Indexed: 12/01/2023] Open
Abstract
Path planning is an essential part of robot intelligence. In this paper, we summarize the characteristics of path planning of industrial robots. And owing to the probabilistic completeness, we review the rapidly-exploring random tree (RRT) algorithm which is widely used in the path planning of industrial robots. Aiming at the shortcomings of the RRT algorithm, this paper investigates the RRT algorithm for path planning of industrial robots in order to improve its intelligence. Finally, the future development direction of the RRT algorithm for path planning of industrial robots is proposed. The study results have particularly guided significance for the development of the path planning of industrial robots and the applicability and practicability of the RRT algorithm.
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Affiliation(s)
| | | | | | | | - Qingdang Li
- College of Electromechanical Engineering, Qingdao University of Science and Technology, Shandong, China
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25
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Fu M, Solovey K, Salzman O, Alterovitz R. Toward certifiable optimal motion planning for medical steerable needles. Int J Rob Res 2023; 42:798-826. [PMID: 37905207 PMCID: PMC10613120 DOI: 10.1177/02783649231165818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2023]
Abstract
Medical steerable needles can follow 3D curvilinear trajectories to avoid anatomical obstacles and reach clinically significant targets inside the human body. Automating steerable needle procedures can enable physicians and patients to harness the full potential of steerable needles by maximally leveraging their steerability to safely and accurately reach targets for medical procedures such as biopsies. For the automation of medical procedures to be clinically accepted, it is critical from a patient care, safety, and regulatory perspective to certify the correctness and effectiveness of the planning algorithms involved in procedure automation. In this paper, we take an important step toward creating a certifiable optimal planner for steerable needles. We present an efficient, resolution-complete motion planner for steerable needles based on a novel adaptation of multi-resolution planning. This is the first motion planner for steerable needles that guarantees to compute in finite time an obstacle-avoiding plan (or notify the user that no such plan exists), under clinically appropriate assumptions. Based on this planner, we then develop the first resolution-optimal motion planner for steerable needles that further provides theoretical guarantees on the quality of the computed motion plan, that is, global optimality, in finite time. Compared to state-of-the-art steerable needle motion planners, we demonstrate with clinically realistic simulations that our planners not only provide theoretical guarantees but also have higher success rates, have lower computation times, and result in higher quality plans.
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Affiliation(s)
- Mengyu Fu
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kiril Solovey
- Department of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa, Israel
| | - Oren Salzman
- Department of Computer Science, Technion-Israel Institute of Technology, Haifa, Israel
| | - Ron Alterovitz
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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26
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Wu B, Zhang W, Chi X, Jiang D, Yi Y, Lu Y. A Novel AGV Path Planning Approach for Narrow Channels Based on the Bi-RRT Algorithm with a Failure Rate Threshold. SENSORS (BASEL, SWITZERLAND) 2023; 23:7547. [PMID: 37688003 PMCID: PMC10490747 DOI: 10.3390/s23177547] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/26/2023] [Revised: 08/19/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023]
Abstract
The efficiency of the rapidly exploring random tree (RRT) falls short when efficiently guiding targets through constricted-passage environments, presenting issues such as sluggish convergence speed and elevated path costs. To overcome these algorithmic limitations, we propose a narrow-channel path-finding algorithm (named NCB-RRT) based on Bi-RRT with the addition of our proposed research failure rate threshold (RFRT) concept. Firstly, a three-stage search strategy is employed to generate sampling points guided by real-time sampling failure rates. By means of the balance strategy, two randomly growing trees are established to perform searching, which improves the success rate of the algorithm in narrow channel environments, accelerating the convergence speed and reducing the number of iterations required. Secondly, the parent node re-selection and path pruning strategy are integrated. This shortens the path length and greatly reduces the number of redundant nodes and inflection points. Finally, the path is optimized by utilizing segmented quadratic Bezier curves to achieve a smooth trajectory. This research shows that the NCB-RRT algorithm is better able to adapt to the complex narrow channel environment, and the performance is also greatly improved in terms of the path length and the number of inflection points. Compared with the RRT, RRT* and Bi-RRT algorithms, the success rate is increased by 2400%, 1900% and 11.11%, respectively.
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Affiliation(s)
| | | | | | | | - Yang Yi
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; (B.W.); (W.Z.); (X.C.); (D.J.)
| | - Yi Lu
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China; (B.W.); (W.Z.); (X.C.); (D.J.)
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27
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Wang L, Yang X, Chen Z, Wang B. Application of the Improved Rapidly Exploring Random Tree Algorithm to an Insect-like Mobile Robot in a Narrow Environment. Biomimetics (Basel) 2023; 8:374. [PMID: 37622979 PMCID: PMC10452469 DOI: 10.3390/biomimetics8040374] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Revised: 08/05/2023] [Accepted: 08/15/2023] [Indexed: 08/26/2023] Open
Abstract
When intelligent mobile robots perform global path planning in complex and narrow environments, several issues often arise, including low search efficiency, node redundancy, non-smooth paths, and high costs. This paper proposes an improved path planning algorithm based on the rapidly exploring random tree (RRT) approach. Firstly, the target bias sampling method is employed to screen and eliminate redundant sampling points. Secondly, the adaptive step size strategy is introduced to address the limitations of the traditional RRT algorithm. The mobile robot is then modeled and analyzed to ensure that the path adheres to angle and collision constraints during movement. Finally, the initial path is pruned, and the path is smoothed using a cubic B-spline curve, resulting in a smoother path with reduced costs. The evaluation metrics employed include search time, path length, and the number of sampling nodes. To evaluate the effectiveness of the proposed algorithm, simulations of the RRT algorithm, RRT-connect algorithm, RRT* algorithm, and the improved RRT algorithm are conducted in various environments. The results demonstrate that the improved RRT algorithm reduces the generated path length by 25.32% compared to the RRT algorithm, 26.42% compared to the RRT-connect algorithm, and 4.99% compared to the RRT* algorithm. Moreover, the improved RRT algorithm significantly improves the demand for reducing path costs. The planning time of the improved RRT algorithm is reduced by 64.96% compared to that of the RRT algorithm, 40.83% compared to that of the RRT-connect algorithm, and 27.34% compared to that of the RRT* algorithm, leading to improved speed. These findings indicate that the proposed method exhibits a notable improvement in the three crucial evaluation metrics: sampling time, number of nodes, and path length. Additionally, the algorithm performed well after undergoing physical verification with an insect-like mobile robot in a real environment featuring narrow elevator entrances.
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Affiliation(s)
- Lina Wang
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
- Key Laboratory of Intelligent Manufacturing Quality Big Data Tracing and Analysis of Zhejiang Province, China Jiliang University, Hangzhou 310018, China
| | - Xin Yang
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
| | - Zeling Chen
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
| | - Binrui Wang
- College of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China
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28
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Chen H, Zang X, Liu Y, Zhang X, Zhao J. A Hierarchical Motion Planning Method for Mobile Manipulator. SENSORS (BASEL, SWITZERLAND) 2023; 23:6952. [PMID: 37571736 PMCID: PMC10422355 DOI: 10.3390/s23156952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 07/31/2023] [Accepted: 08/03/2023] [Indexed: 08/13/2023]
Abstract
This paper focuses on motion planning for mobile manipulators, which includes planning for both the mobile base and the manipulator. A hierarchical motion planner is proposed that allows the manipulator to change its configuration autonomously in real time as needed. The planner has two levels: global planning for the mobile base in two dimensions and local planning for both the mobile base and the manipulator in three dimensions. The planner first generates a path for the mobile base using an optimized A* algorithm. As the mobile base moves along the path with the manipulator configuration unchanged, potential collisions between the manipulator and the environment are checked using the environment data obtained from the on-board sensors. If the current manipulator configuration is in a potential collision, a new manipulator configuration is searched. A sampling-based heuristic algorithm is used to effectively find a collision-free configuration for the manipulator. The experimental results in simulation environments proved that our heuristic sampling-based algorithm outperforms the conservative random sampling-based method in terms of computation time, percentage of successful attempts, and the quality of the generated configuration. Compared with traditional methods, our motion planning method could deal with 3D obstacles, avoid large memory requirements, and does not require a long time to generate a global plan.
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Affiliation(s)
- Hanlin Chen
- State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China; (X.Z.); (X.Z.); (J.Z.)
| | | | - Yubin Liu
- State Key Laboratory of Robotics and Systems, Harbin Institute of Technology, Harbin 150001, China; (X.Z.); (X.Z.); (J.Z.)
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29
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Waltz M, Okhrin O. Spatial-temporal recurrent reinforcement learning for autonomous ships. Neural Netw 2023; 165:634-653. [PMID: 37364473 DOI: 10.1016/j.neunet.2023.06.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 05/11/2023] [Accepted: 06/07/2023] [Indexed: 06/28/2023]
Abstract
This paper proposes a spatial-temporal recurrent neural network architecture for deep Q-networks that can be used to steer an autonomous ship. The network design makes it possible to handle an arbitrary number of surrounding target ships while offering robustness to partial observability. Furthermore, a state-of-the-art collision risk metric is proposed to enable an easier assessment of different situations by the agent. The COLREG rules of maritime traffic are explicitly considered in the design of the reward function. The final policy is validated on a custom set of newly created single-ship encounters called 'Around the Clock' problems and the commonly used Imazu (1987) problems, which include 18 multi-ship scenarios. Performance comparisons with artificial potential field and velocity obstacle methods demonstrate the potential of the proposed approach for maritime path planning. Furthermore, the new architecture exhibits robustness when it is deployed in multi-agent scenarios and it is compatible with other deep reinforcement learning algorithms, including actor-critic frameworks.
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Affiliation(s)
- Martin Waltz
- Technische Universität Dresden, Chair of Econometrics and Statistics, esp. in the Transport Sector, Dresden, 01062, Germany.
| | - Ostap Okhrin
- Technische Universität Dresden, Chair of Econometrics and Statistics, esp. in the Transport Sector, Dresden, 01062, Germany; Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI), Dresden/Leipzig, Germany
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Cao C, Zhu H, Ren Z, Choset H, Zhang J. Representation granularity enables time-efficient autonomous exploration in large, complex worlds. Sci Robot 2023; 8:eadf0970. [PMID: 37467309 DOI: 10.1126/scirobotics.adf0970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Accepted: 06/21/2023] [Indexed: 07/21/2023]
Abstract
We propose a dual-resolution scheme to achieve time-efficient autonomous exploration with one or many robots. The scheme maintains a high-resolution local map of the robot's immediate vicinity and a low-resolution global map of the remaining areas of the environment. We believe that the strength of our approach lies in this low- and high-resolution representation of the environment: The high-resolution local map ensures that the robots observe the entire region in detail, and because the local map is bounded, so is the computation burden to process it. The low-resolution global map directs the robot to explore the broad space and only requires lightweight computation and low bandwidth to communicate among the robots. This paper shows the strength of this approach for both single-robot and multirobot exploration. For multirobot exploration, we also introduce a "pursuit" strategy for sharing information among robots with limited communication. This strategy directs the robots to opportunistically approach each other. We found that the scheme could produce exploration paths with a bounded difference in length compared with the theoretical shortest paths. Empirically, for single-robot exploration, our method produced 80% higher time efficiency with 50% lower computational runtimes than state-of-the-art methods in more than 300 simulation and real-world experiments. For multirobot exploration, our pursuit strategy demonstrated higher exploration time efficiency than conventional strategies in more than 3400 simulation runs with up to 20 robots. Last, we discuss how our method was deployed in the DARPA Subterranean Challenge and demonstrated the fastest and most complete exploration among all teams.
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Affiliation(s)
- C Cao
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - H Zhu
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - Z Ren
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - H Choset
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
| | - J Zhang
- Robotics Institute, Carnegie Mellon University, Pittsburgh, PA, USA
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31
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Gongora A, Monroy J, Rahbar F, Ercolani C, Gonzalez-Jimenez J, Martinoli A. Information-Driven Gas Distribution Mapping for Autonomous Mobile Robots. SENSORS (BASEL, SWITZERLAND) 2023; 23:5387. [PMID: 37420554 PMCID: PMC10305319 DOI: 10.3390/s23125387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Revised: 05/23/2023] [Accepted: 05/30/2023] [Indexed: 07/09/2023]
Abstract
The ability to sense airborne pollutants with mobile robots provides a valuable asset for domains such as industrial safety and environmental monitoring. Oftentimes, this involves detecting how certain gases are spread out in the environment, commonly referred to as a gas distribution map, to subsequently take actions that depend on the collected information. Since the majority of gas transducers require physical contact with the analyte to sense it, the generation of such a map usually involves slow and laborious data collection from all key locations. In this regard, this paper proposes an efficient exploration algorithm for 2D gas distribution mapping with an autonomous mobile robot. Our proposal combines a Gaussian Markov random field estimator based on gas and wind flow measurements, devised for very sparse sample sizes and indoor environments, with a partially observable Markov decision process to close the robot's control loop. The advantage of this approach is that the gas map is not only continuously updated, but can also be leveraged to choose the next location based on how much information it provides. The exploration consequently adapts to how the gas is distributed during run time, leading to an efficient sampling path and, in turn, a complete gas map with a relatively low number of measurements. Furthermore, it also accounts for wind currents in the environment, which improves the reliability of the final gas map even in the presence of obstacles or when the gas distribution diverges from an ideal gas plume. Finally, we report various simulation experiments to evaluate our proposal against a computer-generated fluid dynamics ground truth, as well as physical experiments in a wind tunnel.
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Affiliation(s)
- Andres Gongora
- Machine Perception and Intelligent Robotics (MAPIR) Research Group, Malaga Institute for Mechatronics Engineering and Cyber-Physical Systems (IMECH.UMA), University of Malaga, 29071 Malaga, Spain; (A.G.); (J.G.-J.)
| | - Javier Monroy
- Machine Perception and Intelligent Robotics (MAPIR) Research Group, Malaga Institute for Mechatronics Engineering and Cyber-Physical Systems (IMECH.UMA), University of Malaga, 29071 Malaga, Spain; (A.G.); (J.G.-J.)
| | - Faezeh Rahbar
- Distributed Intelligent Systems and Algorithms Laboratory (DISAL), School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland; (F.R.); (C.E.); (A.M.)
| | - Chiara Ercolani
- Distributed Intelligent Systems and Algorithms Laboratory (DISAL), School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland; (F.R.); (C.E.); (A.M.)
| | - Javier Gonzalez-Jimenez
- Machine Perception and Intelligent Robotics (MAPIR) Research Group, Malaga Institute for Mechatronics Engineering and Cyber-Physical Systems (IMECH.UMA), University of Malaga, 29071 Malaga, Spain; (A.G.); (J.G.-J.)
| | - Alcherio Martinoli
- Distributed Intelligent Systems and Algorithms Laboratory (DISAL), School of Architecture, Civil and Environmental Engineering, École Polytechnique Fédérale de Lausanne, 1015 Lausanne, Switzerland; (F.R.); (C.E.); (A.M.)
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32
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Yan X, Zeng Z, He K, Hong H. Multi-robot cooperative autonomous exploration via task allocation in terrestrial environments. Front Neurorobot 2023; 17:1179033. [PMID: 37342391 PMCID: PMC10277487 DOI: 10.3389/fnbot.2023.1179033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 05/16/2023] [Indexed: 06/22/2023] Open
Abstract
Cooperative autonomous exploration is a challenging task for multi-robot systems, which can cover larger areas in a shorter time or path length. Using multiple mobile robots for cooperative exploration of unknown environments can be more efficient than a single robot, but there are also many difficulties in multi-robot cooperative autonomous exploration. The key to successful multi-robot cooperative autonomous exploration is effective coordination between the robots. This paper designs a multi-robot cooperative autonomous exploration strategy for exploration tasks. Additionally, considering the fact that mobile robots are inevitably subject to failure in harsh conditions, we propose a self-healing cooperative autonomous exploration method that can recover from robot failures.
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Affiliation(s)
- Xiangda Yan
- Laboratory of Unmanned Combat Systems, National University of Defense Technology, Changsha, China
| | - Zhe Zeng
- Rescue & Salvage Department, Navy Submarine Academy, Qingdao, China
| | - Keyan He
- Laboratory of Unmanned Combat Systems, National University of Defense Technology, Changsha, China
| | - Huajie Hong
- Laboratory of Unmanned Combat Systems, National University of Defense Technology, Changsha, China
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33
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Cui Q, Liu P, Du H, Wang H, Ma X. Improved multi-objective artificial bee colony algorithm-based path planning for mobile robots. Front Neurorobot 2023; 17:1196683. [PMID: 37324978 PMCID: PMC10267332 DOI: 10.3389/fnbot.2023.1196683] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/15/2023] [Indexed: 06/17/2023] Open
Abstract
Mobile robots are widely used in various fields, including cosmic exploration, logistics delivery, and emergency rescue and so on. Path planning of mobile robots is essential for completing their tasks. Therefore, Path planning algorithms capable of finding their best path are needed. To address this challenge, we thus develop improved multi-objective artificial bee colony algorithm (IMOABC), a Bio-inspired algorithm-based approach for path planning. The IMOABC algorithm is based on multi-objective artificial bee colony algorithm (MOABC) with four strategies, including external archive pruning strategy, non-dominated ranking strategy, crowding distance strategy, and search strategy. IMOABC is tested on six standard test functions. Results show that IMOABC algorithm outperforms the other algorithms in solving complex multi-objective optimization problems. We then apply the IMOABC algorithm to path planning in the simulation experiment of mobile robots. IMOABC algorithm consistently outperforms existing algorithms (the MOABC algorithm and the ABC algorithm). IMOABC algorithm should be broadly useful for path planning of mobile robots.
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Ishihara Y, Takahashi M. Image-based robot navigation with task achievability. Front Robot AI 2023; 10:944375. [PMID: 37323640 PMCID: PMC10264687 DOI: 10.3389/frobt.2023.944375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 03/20/2023] [Indexed: 06/17/2023] Open
Abstract
Image-based robot action planning is becoming an active area of research owing to recent advances in deep learning. To evaluate and execute robot actions, recently proposed approaches require the estimation of the optimal cost-minimizing path, such as the shortest distance or time, between two states. To estimate the cost, parametric models consisting of deep neural networks are widely used. However, such parametric models require large amounts of correctly labeled data to accurately estimate the cost. In real robotic tasks, collecting such data is not always feasible, and the robot itself may require collecting it. In this study, we empirically show that when a model is trained with data autonomously collected by a robot, the estimation of such parametric models could be inaccurate to perform a task. Specifically, the higher the maximum predicted distance, the more inaccurate the estimation, and the robot fails navigating in the environment. To overcome this issue, we propose an alternative metric, "task achievability" (TA), which is defined as the probability that a robot will reach a goal state within a specified number of timesteps. Compared to the training of optimal cost estimator, TA can use both optimal and non-optimal trajectories in the training dataset to train, which leads to a stable estimation. We demonstrate the effectiveness of TA through robot navigation experiments in an environment resembling a real living room. We show that TA-based navigation succeeds in navigating a robot to different target positions, even when conventional cost estimator-based navigation fails.
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Affiliation(s)
- Yu Ishihara
- Graduate School of Science and Technology, Keio University, Yokohama, Japan
| | - Masaki Takahashi
- Department of System Design Engineering, Keio University, Yokohama, Japan
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35
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Vlantis P, Bechlioulis CP, Kyriakopoulos KJ. Robot Navigation in Complex Workspaces Employing Harmonic Maps and Adaptive Artificial Potential Fields. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094464. [PMID: 37177668 PMCID: PMC10181592 DOI: 10.3390/s23094464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 04/29/2023] [Accepted: 05/02/2023] [Indexed: 05/15/2023]
Abstract
In this work, we address the single robot navigation problem within a planar and arbitrarily connected workspace. In particular, we present an algorithm that transforms any static, compact, planar workspace of arbitrary connectedness and shape to a disk, where the navigation problem can be easily solved. Our solution benefits from the fact that it only requires a fine representation of the workspace boundary (i.e., a set of points), which is easily obtained in practice via SLAM. The proposed transformation, combined with a workspace decomposition strategy that reduces the computational complexity, has been exhaustively tested and has shown excellent performance in complex workspaces. A motion control scheme is also provided for the class of non-holonomic robots with unicycle kinematics, which are commonly used in most industrial applications. Moreover, the tuning of the underlying control parameters is rather straightforward as it affects only the shape of the resulted trajectories and not the critical specifications of collision avoidance and convergence to the goal position. Finally, we validate the efficacy of the proposed navigation strategy via extensive simulations and experimental studies.
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Affiliation(s)
- Panagiotis Vlantis
- Department of Electrical and Computer Engineering, University of Patras, 26504 Patras, Greece
| | | | - Kostas J Kyriakopoulos
- School of Mechanical Engineering, National Technical University of Athens, 15780 Athens, Greece
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36
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Xia X, Li T, Sang S, Cheng Y, Ma H, Zhang Q, Yang K. Path Planning for Obstacle Avoidance of Robot Arm Based on Improved Potential Field Method. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23073754. [PMID: 37050814 PMCID: PMC10098783 DOI: 10.3390/s23073754] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 03/21/2023] [Accepted: 03/25/2023] [Indexed: 06/12/2023]
Abstract
In medical and surgical scenarios, the trajectory planning of a collaborative robot arm is a difficult problem. The artificial potential field (APF) algorithm is a classic method for robot trajectory planning, which has the characteristics of good real-time performance and low computing consumption. There are many variants of the APF algorithm, among which the most widely used variants is the velocity potential field (VPF) algorithm. However, the traditional VPF algorithm has inherent defects and problems, such as easily falling into local minimum, being unable to reach the target, poor dynamic obstacle avoidance ability, and safety and efficiency problems. Therefore, this work presents the improved velocity potential field (IVPF) algorithm, which considers direction factors, obstacle velocity factor, and tangential velocity. When encountering dynamic obstacles, the IVPF algorithm can avoid obstacles better to ensure the safety of both the human and robot arm. The IVPF algorithm also does not easily fall into a local problem when encountering different obstacles. The experiments informed the RRT* algorithm, VPF algorithm, and IVPF algorithm for comparison. Compared with the informed RRT* and VPF algorithm, the result of experiments indicate that the performances of the IVPF algorithm have significant improvements when dealing with different obstacles. The main aim of this paper is to provide a safe and efficient path planning algorithm for the robot arm in the medical field. The proposed algorithm can ensure the safety of both the human and the robot arm when the medical and surgical robot arm is working, and enables the robot arm to cope with emergencies and perform tasks better. The application of the proposed algorithm could make the collaborative robots work in a flexible and safe condition, which could open up new opportunities for the future development of medical and surgical scenarios.
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Affiliation(s)
- Xinkai Xia
- Shanxi Key Laboratory of Micro Nano Sensor & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
- Shanxi Institute of 6D Artificial Intelligence Biomedical Science, Taiyuan 030031, China
| | - Tao Li
- Medical Big Data Research Center, Department of Medical Innovation Research, Chinese PLA General Hospital, Beijing 100853, China
| | - Shengbo Sang
- Shanxi Key Laboratory of Micro Nano Sensor & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
- Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China
| | - Yongqiang Cheng
- Shanxi Key Laboratory of Micro Nano Sensor & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
- Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China
| | - Huanzhou Ma
- Shanxi Key Laboratory of Micro Nano Sensor & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
- Shanxi Institute of 6D Artificial Intelligence Biomedical Science, Taiyuan 030031, China
| | - Qiang Zhang
- Shanxi Key Laboratory of Micro Nano Sensor & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
- Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China
| | - Kun Yang
- Shanxi Key Laboratory of Micro Nano Sensor & Artificial Intelligence Perception, College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China
- Key Lab of Advanced Transducers and Intelligent Control System of the Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China
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37
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Mohammad El-Basioni BM, Abd El-Kader SM. Mission-based PTR triangle for multi-UAV systems flight planning. AD HOC NETWORKS 2023; 142:103115. [DOI: 10.1016/j.adhoc.2023.103115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/01/2023]
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38
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Goal distance-based UAV path planning approach, path optimization and learning-based path estimation: GDRRT*, PSO-GDRRT* and BiLSTM-PSO-GDRRT*. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/17/2023]
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39
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Abujabal N, Fareh R, Sinan S, Baziyad M, Bettayeb M. A comprehensive review of the latest path planning developments for multi-robot formation systems. ROBOTICA 2023. [DOI: 10.1017/s0263574723000322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
Abstract
Abstract
There has been a continuous interest in multi-robot formation systems in the last few years due to several significant advantages such as robustness, scalability, and efficiency. However, multi-robot formation systems suffer from well-known problems such as energy consumption, processing speed, and security. Therefore, developers are continuously researching for optimal solutions that can gather the benefits of multi-robot formation systems while overcoming the possible challenges. A backbone process required by any multi-robot system is path planning. Thus, path planning for multi-robot systems is a recent top research topic. However, the literature lacks a recent comprehensive review of path planning works designed for multi-robot systems. The aim of this review paper is to provide a comprehensive assessment and an insightful look into various path planning techniques developed in multi-robot formation systems, in addition to highlighting the basic problems involved in this field. This will allow the reader to discover the research gaps that must be solved for a better path planning experience for multi-robot formation systems. Finally, an illustrative comparative example is presented at the end of the paper to show the advantages and disadvantages of some popular path planning techniques.
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40
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Liu C, Xie S, Sui X, Huang Y, Ma X, Guo N, Yang F. PRM-D* Method for Mobile Robot Path Planning. SENSORS (BASEL, SWITZERLAND) 2023; 23:3512. [PMID: 37050570 PMCID: PMC10098883 DOI: 10.3390/s23073512] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 03/17/2023] [Accepted: 03/24/2023] [Indexed: 06/19/2023]
Abstract
Various navigation tasks involving dynamic scenarios require mobile robots to meet the requirements of a high planning success rate, fast planning, dynamic obstacle avoidance, and shortest path. PRM (probabilistic roadmap method), as one of the classical path planning methods, is characterized by simple principles, probabilistic completeness, fast planning speed, and the formation of asymptotically optimal paths, but has poor performance in dynamic obstacle avoidance. In this study, we use the idea of hierarchical planning to improve the dynamic obstacle avoidance performance of PRM by introducing D* into the network construction and planning process of PRM. To demonstrate the feasibility of the proposed method, we conducted simulation experiments using the proposed PRM-D* (probabilistic roadmap method and D*) method for maps of different complexity and compared the results with those obtained by classical methods such as SPARS2 (improving sparse roadmap spanners). The experiments demonstrate that our method is non-optimal in terms of path length but second only to graph search methods; it outperforms other methods in static planning, with an average planning time of less than 1 s, and in terms of the dynamic planning speed, our method is two orders of magnitude faster than the SPARS2 method, with a single dynamic planning time of less than 0.02 s. Finally, we deployed the proposed PRM-D* algorithm on a real vehicle for experimental validation. The experimental results show that the proposed method was able to perform the navigation task in a real-world scenario.
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Affiliation(s)
- Chunyang Liu
- School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China; (C.L.)
- Longmen Laboratory, Luoyang 471000, China
| | - Saibao Xie
- School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China; (C.L.)
| | - Xin Sui
- School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China; (C.L.)
- Key Laboratory of Mechanical Design and Transmission System of Henan Province, Henan University of Science and Technology, Luoyang 471003, China
| | - Yan Huang
- School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China; (C.L.)
| | - Xiqiang Ma
- School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China; (C.L.)
- Longmen Laboratory, Luoyang 471000, China
| | - Nan Guo
- School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China; (C.L.)
| | - Fang Yang
- School of Mechatronics Engineering, Henan University of Science and Technology, Luoyang 471003, China; (C.L.)
- Longmen Laboratory, Luoyang 471000, China
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41
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Wang WC, Yeh YW, Chen R. A polynomial-time hybrid solver for multi-agent motion navigation against deadlocks. JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2023. [DOI: 10.3233/jifs-223157] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
Abstract
In the cooperative multi-agent pathfinding and motion planning, given a unique start position and a unique goal position for each agent, all agents are able to pursue their own goals without colliding with each other. To aim at realizing the collision-free motion of the agents within the tractable time, this work proposes a polynomial-time solver, called the HBD-AOI, hybridizing centralized and decentralized schemes. Firstly, an algorithm of centralized pathfinding is utilized to plan the optimal paths of all agents. Afterwards, each of the agents updates the local motion pattern to tracks its own planned waypoints with the obstacle avoidance in a decentralized manner. Furthermore, to resolve unavoidable egoistic conflicts occurring in the decentralized scheme, a centralized intervener with the route replanning is invoked to coach the involved agents to abort the existing deadlocks. Bounded by an amount of time, the performances of the proposed and benchmarked algorithms are simulated on the same instance, from the evaluated testbeds that consists of various maps and scenarios. In the simulations, it is proved that this work outperforms other benchmarked algorithms for all presented instances in the term of the success rate. The experimental results are also demonstrated to verify the feasibility of the proposed methodology.
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Affiliation(s)
- W.-C. Wang
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - Y.-W. Yeh
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan
| | - R. Chen
- Department of Power Mechanical Engineering, National Tsing Hua University, Hsinchu, Taiwan
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42
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Sun X, Deng S, Tong B, Wang S, Zhang C, Jiang Y. Hierarchical framework for mobile robots to effectively and autonomously explore unknown environments. ISA TRANSACTIONS 2023; 134:1-15. [PMID: 36153189 DOI: 10.1016/j.isatra.2022.09.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/06/2022] [Revised: 09/03/2022] [Accepted: 09/03/2022] [Indexed: 06/16/2023]
Abstract
Achieving efficient and safe autonomous exploration in unknown environments is an urgent challenge to be overcome in the field of robotics. Existing exploration methods based on random and greedy strategies cannot ensure that the robot moves to the unknown area as much as possible, and the exploration efficiency is not high. In addition, because the robot is located in an unknown environment, the robot cannot obtain enough information to process the surrounding environment and cannot guarantee absolute safety. To improve the efficiency and safety of exploring unknown environments, we propose an autonomous exploration motion planning framework that is divided into the exploration and obstacle avoidance levels. The two levels are independent and interconnected. The exploration level finds the optimal frontier target point in the global scope based on the forward filtering angle and cost function, attracting the robot to move to the unknown area as much as possible, and improving the exploration efficiency; the obstacle avoidance level establishes a scenario-speed conversion mechanism, and the target point and obstacle information are weighed to realise dynamic motion planning and completes obstacle avoidance control, and ensures the safety of exploration. Experiments in different simulation scenarios and real environments verify the superiority of the method. Results show that our method is superior to the existing methods.
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Affiliation(s)
- Xuehao Sun
- School of Mechanical Engineering, Anhui University of Technology, Ma'anshan 243032, China.
| | - Shuchao Deng
- School of Mechanical Engineering, Anhui University of Technology, Ma'anshan 243032, China; Anhui Province Key Laboratory of Special Heavy Load Robot, Ma'anshan 243032, China.
| | - Baohong Tong
- School of Mechanical Engineering, Anhui University of Technology, Ma'anshan 243032, China; Anhui Province Key Laboratory of Special Heavy Load Robot, Ma'anshan 243032, China.
| | - Shuang Wang
- School of Mechanical Engineering, Anhui University of Science and Technology, Huainan 232001, China.
| | - Chenyang Zhang
- School of Mechanical Engineering, Anhui University of Technology, Ma'anshan 243032, China.
| | - Yuxiang Jiang
- School of Mechanical Engineering, Anhui University of Technology, Ma'anshan 243032, China.
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43
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Tamizi MG, Yaghoubi M, Najjaran H. A review of recent trend in motion planning of industrial robots. INTERNATIONAL JOURNAL OF INTELLIGENT ROBOTICS AND APPLICATIONS 2023. [DOI: 10.1007/s41315-023-00274-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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44
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Mavrogiannis C, Baldini F, Wang A, Zhao D, Trautman P, Steinfeld A, Oh J. Core Challenges of Social Robot Navigation: A Survey. ACM TRANSACTIONS ON HUMAN-ROBOT INTERACTION 2023. [DOI: 10.1145/3583741] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/19/2023]
Abstract
Robot navigation in crowded public spaces is a complex task that requires addressing a variety of engineering and human factors challenges. These challenges have motivated a great amount of research resulting in important developments for the fields of robotics and human-robot interaction over the past three decades. Despite the significant progress and the massive recent interest, we observe a number of significant remaining challenges that prohibit the seamless deployment of autonomous robots in crowded environments. In this survey article, we organize existing challenges into a set of categories related to broader open problems in robot planning, behavior design, and evaluation methodologies. Within these categories, we review past work, and offer directions for future research. Our work builds upon and extends earlier survey efforts by a) taking a critical perspective and diagnosing fundamental limitations of adopted practices in the field and b) offering constructive feedback and ideas that could inspire research in the field over the coming decade.
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Affiliation(s)
| | - Francesca Baldini
- Honda Research Institute and California Institute of Technology, USA
| | - Allan Wang
- The Robotics Institute, Carnegie Mellon University, USA
| | - Dapeng Zhao
- The Robotics Institute, Carnegie Mellon University, USA
| | | | | | - Jean Oh
- The Robotics Institute, Carnegie Mellon University, USA
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Han JW, Jeon S, Kwon HJ. Hierarchical Topology Map with Explicit Corridor for global path planning of mobile robots. INTEL SERV ROBOT 2023. [DOI: 10.1007/s11370-023-00458-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
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46
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Wei C, Chen C, Tanner HG. Navigation functions with moving destinations and obstacles. Auton Robots 2023. [DOI: 10.1007/s10514-023-10088-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/16/2023]
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47
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Yu F, Shang H, Zhu Q, Zhang H, Chen Y. An efficient RRT-based motion planning algorithm for autonomous underwater vehicles under cylindrical sampling constraints. Auton Robots 2023. [DOI: 10.1007/s10514-023-10083-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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48
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Li S, Dantam NT. A sampling and learning framework to prove motion planning infeasibility. Int J Rob Res 2023. [DOI: 10.1177/02783649231154674] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
We present a learning-based approach to prove infeasibility of kinematic motion planning problems. Sampling-based motion planners are effective in high-dimensional spaces but are only probabilistically complete. Consequently, these planners cannot provide a definite answer if no plan exists, which is important for high-level scenarios, such as task-motion planning. We apply data generated during multi-directional sampling-based planning (such as PRM) to a machine learning approach to construct an infeasibility proof. An infeasibility proof is a closed manifold in the obstacle region of the configuration space that separates the start and goal into disconnected components of the free configuration space. We train the manifold using common machine learning techniques and then triangulate the manifold into a polytope to prove containment in the obstacle region. Under assumptions about the hyper-parameters and robustness of configuration space optimization, the output is either an infeasibility proof or a motion plan in the limit. We demonstrate proof construction for up to 4-DOF configuration spaces. A large part of the algorithm is parallelizable, which offers potential to address higher dimensional configuration spaces.
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Affiliation(s)
- Sihui Li
- Department of Computer Science, Colorado School of Mines, Golden, CO, USA
| | - Neil T. Dantam
- Department of Computer Science, Colorado School of Mines, Golden, CO, USA
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Kala R. Mission planning on preference-based expression trees using heuristics-assisted evolutionary computation. Appl Soft Comput 2023. [DOI: 10.1016/j.asoc.2023.110090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
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50
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Coordinated flight path planning for a fleet of missiles in high-risk areas. ROBOTICA 2023. [DOI: 10.1017/s0263574722001886] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Abstract
This paper addresses the flight path planning problem for multiple missiles engaging stationary targets in high-risk areas. Targets protected by air defence are preferably engaged by a fleet or swarm of missiles, not individual missiles. The concept of a swarm attack is that a large number of approaching missiles overwhelm air defence. The deployment of missiles is often part of a broader mission including further participants. Flight path planning is then an integral element of mission planning, requiring strict timing coordination of all members involved. The flight times of the missiles are dictated by the master planning. We present algorithms for offline planning and online re-planning of flight paths for a fleet of missiles with flight time constraints. The algorithms are based on an advanced bidirectional RRT* algorithm that generates risk-minimizing flight paths with predefined flight times. Online planning generates the flight paths of the fleet sequentially, maintaining a safety distance between the missiles to prevent mutual collision. Offline planning uses a global optimization approach to determine an optimal selection of flight paths from a large set of potential paths. The selection is performed by a branch and bound algorithm that determines optimal cliques in the path compatibility graph. The optimization is embedded in an iterative algorithm that allows to successively improve the mission success.
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