Particle swarm optimization versus genetic algorithm in manipulator trajectory planning
The aim of this study is the reduction of the computational burden associated with the evolutionary optimization of manipulator trajectory planning. This paper proposes the use of a particle swarm optimization algorithm to generate trajectories for robotic planar manipulators, based on direct kinema...
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| Outros Autores: | , , |
| Formato: | conferenceObject |
| Idioma: | eng |
| Publicado em: |
2019
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.22/13328 |
| País: | Portugal |
| Oai: | oai:recipp.ipp.pt:10400.22/13328 |
| Resumo: | The aim of this study is the reduction of the computational burden associated with the evolutionary optimization of manipulator trajectory planning. This paper proposes the use of a particle swarm optimization algorithm to generate trajectories for robotic planar manipulators, based on direct kinematics. The design objective is to minimize the ripple in the trajectory time evolution. Several redundant and hyperredundant manipulators are considered. The particle swarm optimization algorithm is compared with genetic algorithm in solving the manipulator trajectory planning problem. Preliminary simulation results are presented. |
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