Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field Method
Aiming at the problems in the path planning of the manipulator by artificial potential field (APF) method, a method combining the APF adaptive variable step size in the joint space and goal-biased rapidly-exploring random tree (RRT) is proposed. The APF obstacle avoidance planning is performed in th...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2023-10-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.10.004 |
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| _version_ | 1841546990003945472 |
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| author | Yue Xu Zhou Haibo Shao Yanpeng Lu Shuai Xu Wangbei Deng Yuxin |
| author_facet | Yue Xu Zhou Haibo Shao Yanpeng Lu Shuai Xu Wangbei Deng Yuxin |
| author_sort | Yue Xu |
| collection | DOAJ |
| description | Aiming at the problems in the path planning of the manipulator by artificial potential field (APF) method, a method combining the APF adaptive variable step size in the joint space and goal-biased rapidly-exploring random tree (RRT) is proposed. The APF obstacle avoidance planning is performed in the joint space to reduce the number of inverse kinematics and the sudden change of joint angles. The collision and target unreachability problems in the path planning are solved by improving the repulsive and gravitational potential field functions. The Cauchy probability distribution is used to change the joint angle step size through the distance between the end point and the obstacle. By adjusting the bias of the RRT algorithm, suitable temporary target points are generated to solve the local minima problem of the APF. The obstacle avoidance simulation of the manipulator is carried out in the presence of local minima of the APF. Adaptive variable-step path planning can generate smooth trajectories and improve the search efficiency. The goal-biased RRT selects the temporary target point and the overall path length becomes smaller. The picking manipulator can effectively meet the requirements of obstacle avoidance picking tasks under the improved algorithm. |
| format | Article |
| id | doaj-art-abaecbbc863841d28d8f0f43acde8fc1 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2023-10-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-abaecbbc863841d28d8f0f43acde8fc12025-01-10T14:59:05ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392023-10-0147233042736686Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field MethodYue XuZhou HaiboShao YanpengLu ShuaiXu WangbeiDeng YuxinAiming at the problems in the path planning of the manipulator by artificial potential field (APF) method, a method combining the APF adaptive variable step size in the joint space and goal-biased rapidly-exploring random tree (RRT) is proposed. The APF obstacle avoidance planning is performed in the joint space to reduce the number of inverse kinematics and the sudden change of joint angles. The collision and target unreachability problems in the path planning are solved by improving the repulsive and gravitational potential field functions. The Cauchy probability distribution is used to change the joint angle step size through the distance between the end point and the obstacle. By adjusting the bias of the RRT algorithm, suitable temporary target points are generated to solve the local minima problem of the APF. The obstacle avoidance simulation of the manipulator is carried out in the presence of local minima of the APF. Adaptive variable-step path planning can generate smooth trajectories and improve the search efficiency. The goal-biased RRT selects the temporary target point and the overall path length becomes smaller. The picking manipulator can effectively meet the requirements of obstacle avoidance picking tasks under the improved algorithm.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.10.004Pickup robotArtificial potential field methodAdaptive variable step sizeRapidly-exploring random treeObstacle avoidance planning |
| spellingShingle | Yue Xu Zhou Haibo Shao Yanpeng Lu Shuai Xu Wangbei Deng Yuxin Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field Method Jixie chuandong Pickup robot Artificial potential field method Adaptive variable step size Rapidly-exploring random tree Obstacle avoidance planning |
| title | Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field Method |
| title_full | Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field Method |
| title_fullStr | Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field Method |
| title_full_unstemmed | Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field Method |
| title_short | Obstacle Avoidance Planning of Manipulator Joints Based on an Improved Artificial Potential Field Method |
| title_sort | obstacle avoidance planning of manipulator joints based on an improved artificial potential field method |
| topic | Pickup robot Artificial potential field method Adaptive variable step size Rapidly-exploring random tree Obstacle avoidance planning |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2023.10.004 |
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