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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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Yang J, Chen Y, Yu J. Convolutional neural network based on the fusion of image classification and segmentation module for weed detection in alfalfa. PEST MANAGEMENT SCIENCE 2024; 80:2751-2760. [PMID: 38299763 DOI: 10.1002/ps.7979] [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: 03/13/2023] [Revised: 01/08/2024] [Accepted: 01/17/2024] [Indexed: 02/02/2024]
Abstract
BACKGROUND Accurate and reliable weed detection in real time is essential for realizing autonomous precision herbicide application. The objective of this research was to propose a novel neural network architecture to improve the detection accuracy for broadleaf weeds growing in alfalfa. RESULTS A novel neural network, ResNet-101-segmentation, was developed by fusing an image classification and segmentation module with the backbone selected from ResNet-101. Compared with existing neural networks (AlexNet, GoogLeNet, VGG16, and ResNet-101), ResNet-101-segmentation improved the detection of Carolina geranium, catchweed bedstraw, mugwort and speedwell from 78.27% to 98.17%, from 79.49% to 98.28%, from 67.03% to 96.23%, and from 75.95% to 98.06%, respectively. The novel network exhibited high values of confusion matrices (>90%) when trained with sufficient data sets. CONCLUSION ResNet-101-segmentation demonstrated excellent performance compared with existing models (AlexNet, GoogLeNet, VGG16, and ResNet-101) for detecting broadleaf weeds growing in alfalfa. This approach offers a promising solution to increase the accuracy of weed detection, especially in cases where weeds and crops have similar plant morphology. © 2024 Society of Chemical Industry.
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Affiliation(s)
- Jie Yang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China
- Peking University Institute of Advanced Agricultural Sciences/Shandong Laboratory of Advanced Agricultural Sciences at Weifang, Weifang, China
| | - Yong Chen
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, China
| | - Jialin Yu
- Peking University Institute of Advanced Agricultural Sciences/Shandong Laboratory of Advanced Agricultural Sciences at Weifang, Weifang, China
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Zhao T, Wang M, Zhao Q, Zheng X, Gao H. A Path-Planning Method Based on Improved Soft Actor-Critic Algorithm for Mobile Robots. Biomimetics (Basel) 2023; 8:481. [PMID: 37887612 PMCID: PMC10604071 DOI: 10.3390/biomimetics8060481] [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: 08/04/2023] [Revised: 09/30/2023] [Accepted: 10/06/2023] [Indexed: 10/28/2023] Open
Abstract
The path planning problem has gained more attention due to the gradual popularization of mobile robots. The utilization of reinforcement learning techniques facilitates the ability of mobile robots to successfully navigate through an environment containing obstacles and effectively plan their path. This is achieved by the robots' interaction with the environment, even in situations when the environment is unfamiliar. Consequently, we provide a refined deep reinforcement learning algorithm that builds upon the soft actor-critic (SAC) algorithm, incorporating the concept of maximum entropy for the purpose of path planning. The objective of this strategy is to mitigate the constraints inherent in conventional reinforcement learning, enhance the efficacy of the learning process, and accommodate intricate situations. In the context of reinforcement learning, two significant issues arise: inadequate incentives and inefficient sample use during the training phase. To address these challenges, the hindsight experience replay (HER) mechanism has been presented as a potential solution. The HER mechanism aims to enhance algorithm performance by effectively reusing past experiences. Through the utilization of simulation studies, it can be demonstrated that the enhanced algorithm exhibits superior performance in comparison with the pre-existing method.
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Affiliation(s)
- Tinglong Zhao
- School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China; (T.Z.); (X.Z.); (H.G.)
| | - Ming Wang
- School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China; (T.Z.); (X.Z.); (H.G.)
| | - Qianchuan Zhao
- Department of Automation, Tsinghua University, Beijing 100018, China;
| | - Xuehan Zheng
- School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China; (T.Z.); (X.Z.); (H.G.)
| | - He Gao
- School of Information and Electrical Engineering, Shandong Jianzhu University, Jinan 250101, China; (T.Z.); (X.Z.); (H.G.)
- Shandong Zhengchen Technology Co., Ltd., Jinan 250000, China
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Vladareanu L, Yu H, Wang H, Feng Y. Advanced Intelligent Control in Robots. SENSORS (BASEL, SWITZERLAND) 2023; 23:5699. [PMID: 37420865 PMCID: PMC10300857 DOI: 10.3390/s23125699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 06/13/2023] [Indexed: 07/09/2023]
Abstract
Advanced intelligent control (AIC) is a rapidly evolving and complex field that poses significant challenges [...].
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Affiliation(s)
- Luige Vladareanu
- Institute of Solid Mechanics of the Romanian Academy, 010141 Bucharest, Romania
| | - Hongnian Yu
- The School of Computing, Engineering and the Built Environment, Edinburgh Napier University, Edinburgh EH11 4BN, UK;
| | - Hongbo Wang
- Academy for Engineering and Technology, Fudan University, Shanghai 200433, China;
| | - Yongfei Feng
- Faculty of Mechanical Engineering & Mechanics, Ningbo University, Ningbo 315211, China;
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Liu Y, Tao W, Li S, Li Y, Wang Q. A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115172. [PMID: 37299899 DOI: 10.3390/s23115172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 05/22/2023] [Accepted: 05/25/2023] [Indexed: 06/12/2023]
Abstract
The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, which reduces search efficiency. Herein, a bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT) was proposed for the path planning of a dual manipulator by introducing a search strategy of a step size with a target angle and random value. The artificial potential field method was introduced, combining the search features with the bidirectional goal bias and the concept of greedy path optimization. According to simulations, taking the main manipulator as an example, compared with goal bias RRT, variable step size RRT, and goal bias bidirectional RRT, the proposed algorithm reduces the search time by 23.53%, 15.45%, and 43.78% and decreases the path length by 19.35%, 18.83%, and 21.38%, respectively. Moreover, taking the slave manipulator as another example, the proposed algorithm reduces the search time by 6.71%, 1.49%, and 46.88% and decreases the path length by 19.88%, 19.39%, and 20.83%, respectively. The proposed algorithm can be adopted to effectively achieve path planning for the dual manipulator.
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Affiliation(s)
- Youyu Liu
- Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China
- Research Office, Wuhu Institute of Technology, Wuhu 241000, China
- Mechanical Engineering Department, Anhui Polytechnic University, Wuhu 241000, China
| | - Wanbao Tao
- Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China
- Mechanical Engineering Department, Anhui Polytechnic University, Wuhu 241000, China
| | - Shunfang Li
- Research Office, Wuhu Institute of Technology, Wuhu 241000, China
| | - Yi Li
- Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China
- Mechanical Engineering Department, Anhui Polytechnic University, Wuhu 241000, China
| | - Qijie Wang
- Anhui Key Laboratory of Detection Technology and Energy Saving Devices, Anhui Polytechnic University, Wuhu 241000, China
- Mechanical Engineering Department, Anhui Polytechnic University, Wuhu 241000, China
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Luo Y, Qin Q, Hu Z, Zhang Y. Path Planning for Unmanned Delivery Robots Based on EWB-GWO Algorithm. SENSORS (BASEL, SWITZERLAND) 2023; 23:1867. [PMID: 36850464 PMCID: PMC9965765 DOI: 10.3390/s23041867] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2023] [Revised: 01/20/2023] [Accepted: 01/24/2023] [Indexed: 06/18/2023]
Abstract
With the rise of robotics within various fields, there has been a significant development in the use of mobile robots. For mobile robots performing unmanned delivery tasks, autonomous robot navigation based on complex environments is particularly important. In this paper, an improved Gray Wolf Optimization (GWO)-based algorithm is proposed to realize the autonomous path planning of mobile robots in complex scenarios. First, the strategy for generating the initial wolf pack of the GWO algorithm is modified by introducing a two-dimensional Tent-Sine coupled chaotic mapping in this paper. This guarantees that the GWO algorithm generates the initial population diversity while improving the randomness between the two-dimensional state variables of the path nodes. Second, by introducing the opposition-based learning method based on the elite strategy, the adaptive nonlinear inertia weight strategy and random wandering law of the Butterfly Optimization Algorithm (BOA), this paper improves the defects of slow convergence speed, low accuracy, and imbalance between global exploration and local mining functions of the GWO algorithm in dealing with high-dimensional complex problems. In this paper, the improved algorithm is named as an EWB-GWO algorithm, where EWB is the abbreviation of three strategies. Finally, this paper enhances the rationalization of the initial population generation of the EWB-GWO algorithm based on the visual-field line detection technique of Bresenham's line algorithm, reduces the number of iterations of the EWB-GWO algorithm, and decreases the time complexity of the algorithm in dealing with the path planning problem. The simulation results show that the EWB-GWO algorithm is very competitive among metaheuristics of the same type. It also achieves optimal path length measures and smoothness metrics in the path planning experiments.
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Affiliation(s)
- Yuan Luo
- Key Laboratory of Optoelectronic Information Sensing and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Qiong Qin
- Key Laboratory of Optoelectronic Information Sensing and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Zhangfang Hu
- Key Laboratory of Optoelectronic Information Sensing and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
| | - Yi Zhang
- School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
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