Manipulator Trajectory Planning based on ADPSO Algorithm
The working path of welding manipulator is complex，which requires high smoothness of the planning trajectory，and the planning trajectory needs to meet the kinematics constraints of each joint. An adaptive particle swarm optimization （ADPSO） algorithm with disturbance is proposed，which can plan the o...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Mechanical Transmission
2022-05-01
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| Series: | Jixie chuandong |
| Subjects: | |
| Online Access: | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.05.017 |
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| author | Xiaohong Tang Yongjian Gong Nianjiao Wang Hong Zhang Leilei Ren |
| author_facet | Xiaohong Tang Yongjian Gong Nianjiao Wang Hong Zhang Leilei Ren |
| author_sort | Xiaohong Tang |
| collection | DOAJ |
| description | The working path of welding manipulator is complex，which requires high smoothness of the planning trajectory，and the planning trajectory needs to meet the kinematics constraints of each joint. An adaptive particle swarm optimization （ADPSO） algorithm with disturbance is proposed，which can plan the optimal trajectory of time，ability and jump under joint constraints. The quintic NURBS curve is used to interpolate the joint working path points，so that the joint position，velocity，acceleration and jump curves are continuous and smooth. The ADPSO algorithm is used for multi-objective optimal trajectory planning. Firstly，the idea of particle extrapolation is combined with particle swarm optimization （PSO） algorithm to enhance the ability of particle search，and then disturbance is introduced to the individual extremum and group extremum to accelerate the convergence speed of particles. Simulation analysis is carried out in Matlab environment， compared with other intelligent algorithms，ADPSO algorithm has better optimization effect and faster optimization timeliness. |
| format | Article |
| id | doaj-art-c1cc197fbccd4cdc8140c7d27ae95895 |
| institution | Kabale University |
| issn | 1004-2539 |
| language | zho |
| publishDate | 2022-05-01 |
| publisher | Editorial Office of Journal of Mechanical Transmission |
| record_format | Article |
| series | Jixie chuandong |
| spelling | doaj-art-c1cc197fbccd4cdc8140c7d27ae958952025-01-10T13:58:06ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392022-05-014612312930479532Manipulator Trajectory Planning based on ADPSO AlgorithmXiaohong TangYongjian GongNianjiao WangHong ZhangLeilei RenThe working path of welding manipulator is complex，which requires high smoothness of the planning trajectory，and the planning trajectory needs to meet the kinematics constraints of each joint. An adaptive particle swarm optimization （ADPSO） algorithm with disturbance is proposed，which can plan the optimal trajectory of time，ability and jump under joint constraints. The quintic NURBS curve is used to interpolate the joint working path points，so that the joint position，velocity，acceleration and jump curves are continuous and smooth. The ADPSO algorithm is used for multi-objective optimal trajectory planning. Firstly，the idea of particle extrapolation is combined with particle swarm optimization （PSO） algorithm to enhance the ability of particle search，and then disturbance is introduced to the individual extremum and group extremum to accelerate the convergence speed of particles. Simulation analysis is carried out in Matlab environment， compared with other intelligent algorithms，ADPSO algorithm has better optimization effect and faster optimization timeliness.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.05.017ManipulatorTrajectory planningQuintic NURBS curveAdaptive particle swarm optimization algorithmMultiobjective optimization |
| spellingShingle | Xiaohong Tang Yongjian Gong Nianjiao Wang Hong Zhang Leilei Ren Manipulator Trajectory Planning based on ADPSO Algorithm Jixie chuandong Manipulator Trajectory planning Quintic NURBS curve Adaptive particle swarm optimization algorithm Multiobjective optimization |
| title | Manipulator Trajectory Planning based on ADPSO Algorithm |
| title_full | Manipulator Trajectory Planning based on ADPSO Algorithm |
| title_fullStr | Manipulator Trajectory Planning based on ADPSO Algorithm |
| title_full_unstemmed | Manipulator Trajectory Planning based on ADPSO Algorithm |
| title_short | Manipulator Trajectory Planning based on ADPSO Algorithm |
| title_sort | manipulator trajectory planning based on adpso algorithm |
| topic | Manipulator Trajectory planning Quintic NURBS curve Adaptive particle swarm optimization algorithm Multiobjective optimization |
| url | http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2022.05.017 |
| work_keys_str_mv | AT xiaohongtang manipulatortrajectoryplanningbasedonadpsoalgorithm AT yongjiangong manipulatortrajectoryplanningbasedonadpsoalgorithm AT nianjiaowang manipulatortrajectoryplanningbasedonadpsoalgorithm AT hongzhang manipulatortrajectoryplanningbasedonadpsoalgorithm AT leileiren manipulatortrajectoryplanningbasedonadpsoalgorithm |