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Xin G, Shi L, Long G, Pan W, Li Y, Xu J. Mobile robot path planning with reformative bat algorithm. PLoS One 2022; 17:e0276577. [PMID: 36331930 PMCID: PMC9635739 DOI: 10.1371/journal.pone.0276577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Accepted: 10/10/2022] [Indexed: 11/06/2022] Open
Abstract
Mobile robot path planning has attracted much attention as a key technology in robotics research. In this paper, a reformative bat algorithm (RBA) for mobile robot path planning is proposed, which is employed as the control mechanism of robots. The Doppler effect is applied to frequency update to ameliorate RBA. When the robot is in motion, the Doppler effect can be adaptively compensated to prevent the robot from prematurely converging. In the velocity update and position update, chaotic map and dynamic disturbance coefficient are introduced respectively to enrich the population diversity and weaken the limitation of local optimum. Furthermore, Q-learning is incorporated into RBA to reasonably choose the loudness attenuation coefficient and the pulse emission enhancement coefficient to reconcile the trade-off between exploration and exploitation, while improving the local search capability of RBA. The simulation experiments are carried out in two different environments, where the success rate of RBA is 93.33% and 90%, respectively. Moreover, in terms of the results of success rate, path length and number of iterations, RBA has better robustness and can plan the optimal path in a relatively short time compared with other algorithms in this field, thus illustrating its validity and reliability. Eventually, by the aid of the Robot Operating System (ROS), the experimental results of real-world robot navigation indicate that RBA has satisfactory real-time performance and path planning effect, which can be considered as a crucial choice for dealing with path planning problems.
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Affiliation(s)
- Gongfeng Xin
- Innovation Research Institute, Shandong Hi-speed Group Co. LTD, Jinan, Shandong, China
| | - Lei Shi
- School of Information Science and Electrical Engineering (School of Artificial Intelligence), Shandong Jiaotong University, Jinan, Shandong, China
| | - Guanxu Long
- Innovation Research Institute, Shandong Hi-speed Group Co. LTD, Jinan, Shandong, China
| | - Weigang Pan
- School of Information Science and Electrical Engineering (School of Artificial Intelligence), Shandong Jiaotong University, Jinan, Shandong, China
- * E-mail:
| | - Yiming Li
- Innovation Research Institute, Shandong Hi-speed Group Co. LTD, Jinan, Shandong, China
| | - Jicun Xu
- School of Information Science and Electrical Engineering (School of Artificial Intelligence), Shandong Jiaotong University, Jinan, Shandong, China
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A systematic overview of developments in differential evolution and particle swarm optimization with their advanced suggestion. APPL INTELL 2022. [DOI: 10.1007/s10489-021-02803-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Target Search Algorithm for AUV Based on Real-Time Perception Maps in Unknown Environment. MACHINES 2021. [DOI: 10.3390/machines9080147] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
For the problem of AUV target searches in unknown underwater environments, a target search algorithm for AUVs based on a real-time perception map is proposed. Real-time perception maps, including target existence probability maps, uncertainty maps, and pheromone maps and their updating rules, are established. Attraction source maps and search status maps based on the environmental information detected by the AUV are established. The maps are used for the AUV to search for corner areas that are unknown to a high degree and areas with low coverage around the current location. At the same time, a release mechanism for attraction and revisiting pheromones is established by combining a neural excitation network algorithm to make the gradient spread in the pheromone grid map. By setting up a search revenue function based on real-time perception maps, an AUV search decision-making method is established. When the AUV finds a suspected target, the AUV approaches the suspected target. The path planning of the AUV is carried out through an improved artificial potential field method. The short-distance confirmation of the target and obstacle avoidance in the search process are realized. The simulation results show that the algorithm has high search efficiency. Additionally, when the target exists in a corner area, the probability of the AUV to quickly search for the target is fast and feasible.
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Path-Planning for Mobile Robots Using a Novel Variable-Length Differential Evolution Variant. MATHEMATICS 2021. [DOI: 10.3390/math9040357] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
Mobile robots are currently exploited in various applications to enhance efficiency and reduce risks in hard activities for humans. The high autonomy in those systems is strongly related to the path-planning task. The path-planning problem is complex and requires in its formulation the adjustment of path elements that take the mobile robot from a start point to a target one at the lowest cost. Nevertheless, the identity or the number of the path elements to be adjusted is unknown; therefore, the human decision is necessary to determine this information reducing autonomy. Due to the above, this work conceives the path-planning as a Variable-Length-Vector optimization problem (VLV-OP) where both the number of variables (path elements) and their values must be determined. For this, a novel variant of Differential Evolution for Variable-Length-Vector optimization named VLV-DE is proposed to handle the path-planning VLV-OP for mobile robots. VLV-DE uses a population with solution vectors of different sizes adapted through a normalization procedure to allow interactions and determine the alternatives that better fit the problem. The effectiveness of this proposal is shown through the solution of the path-planning problem in complex scenarios. The results are contrasted with the well-known A* and the RRT*-Smart path-planning methods.
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MARTİNS O. Quantitative Performance Review of Wheeled Mobile Robot Path Planning Algorithms. GAZI UNIVERSITY JOURNAL OF SCIENCE 2021. [DOI: 10.35378/gujs.792682] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
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de Almeida JPLS, Nakashima RT, Neves-Jr F, de Arruda LVR. A Global/Local Path Planner for Multi-Robot Systems with Uncertain Robot Localization. J INTELL ROBOT SYST 2020. [DOI: 10.1007/s10846-020-01196-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
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Tang B, Xiang K, Pang M, Zhanxia Z. Multi-robot path planning using an improved self-adaptive particle swarm optimization. INT J ADV ROBOT SYST 2020. [DOI: 10.1177/1729881420936154] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Path planning is of great significance in motion planning and cooperative navigation of multiple robots. Nevertheless, because of its high complexity and nondeterministic polynomial time hard nature, efficiently tackling with the issue of multi-robot path planning remains greatly challenging. To this end, enhancing a coevolution mechanism and an improved particle swarm optimization (PSO) algorithm, this article presents a coevolution-based particle swarm optimization method to cope with the multi-robot path planning issue. Attempting to well adjust the global and local search abilities and address the stagnation issue of particle swarm optimization, the proposed particle swarm optimization enhances a widely used standard particle swarm optimization algorithm with the evolutionary game theory, in which a novel self-adaptive strategy is proposed to update the three main control parameters of particles. Since the convergence of particle swarm optimization significantly influences its optimization efficiency, the convergence of the proposed particle swarm optimization is analytically investigated and a parameter selection rule, sufficiently guaranteeing the convergence of this particle swarm optimization, is provided in this article. The performance of the proposed planning method is verified through different scenarios both in single-robot and in multi-robot path planning problems. The numerical simulation results reveal that, compared to its contenders, the proposed method is highly promising with respect to the path optimality. Also, the computation time of the proposed method is comparable with those of its peers.
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Affiliation(s)
- Biwei Tang
- School of Automation, Intelligent Systems Research Institute, Wuhan University of Technology, Wuhan, Hubei, China
| | - Kui Xiang
- School of Automation, Intelligent Systems Research Institute, Wuhan University of Technology, Wuhan, Hubei, China
| | - Muye Pang
- School of Automation, Intelligent Systems Research Institute, Wuhan University of Technology, Wuhan, Hubei, China
| | - Zhu Zhanxia
- National Key Laboratory of Aerospace and Flight Dynamics, School of Astronautics, Northwestern Polytechnical University, Xi’an, Shaanxi, China
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Ajeil FH, Ibraheem IK, Azar AT, Humaidi AJ. Autonomous navigation and obstacle avoidance of an omnidirectional mobile robot using swarm optimization and sensors deployment. INT J ADV ROBOT SYST 2020. [DOI: 10.1177/1729881420929498] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. Two modifications are suggested to improve the searching process of the standard bat algorithm with the result of two novel algorithms. The first algorithm is a Modified Frequency Bat algorithm, and the second is a hybridization between the Particle Swarm Optimization with the Modified Frequency Bat algorithm, namely, the Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithm. Both Modified Frequency Bat and Hybrid Particle Swarm Optimization-Modified Frequency Bat algorithms have been integrated with a proposed technique for obstacle detection and avoidance and are applied to different static and dynamic environments using free-space modeling. Moreover, a new procedure is proposed to convert the infeasible solutions suggested via path the proposed swarm-inspired optimization-based path planning algorithm into feasible ones. The simulations are run in MATLAB environment to test the validation of the suggested algorithms. They have shown that the proposed path planning algorithms result in superior performance by finding the shortest and smoothest collision-free path under various static and dynamic scenarios.
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Affiliation(s)
- Fatin Hassan Ajeil
- Department of Electrical Engineering, College of Engineering, University of Baghdad, Al-Jadriyah, Baghdad, Iraq
| | - Ibraheem Kasim Ibraheem
- Department of Electrical Engineering, College of Engineering, University of Baghdad, Al-Jadriyah, Baghdad, Iraq
| | - Ahmad Taher Azar
- Robotics and Internet-of-Things Lab (RIOTU), Prince Sultan University, Riyadh, Saudi Arabia
- Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt
| | - Amjad J Humaidi
- Department of Control and Systems Engineering, University of Technology, Baghdad, Iraq
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Abstract
Good path planning technology of mobile robot can not only save a lot of time, but also reduce the wear and capital investment of mobile robot. Several methodologies have been proposed and reported in the literature for the path planning of mobile robot. Although these methodologies do not guarantee an optimal solution, they have been successfully applied in their works. The purpose of this paper is to review the modeling, optimization criteria and solution algorithms for the path planning of mobile robot. The survey shows GA (genetic algorithm), PSO (particle swarm optimization algorithm), APF (artificial potential field), and ACO (ant colony optimization algorithm) are the most used approaches to solve the path planning of mobile robot. Finally, future research is discussed which could provide reference for the path planning of mobile robot.
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