Time-optimal trajectory planning for robotic arms based on enhanced particle swarm optimization algorithm

Teng Huang; YuChao Fang; Ming Fang

doi:10.1117/12.3039824

28 August 2024 Time-optimal trajectory planning for robotic arms based on enhanced particle swarm optimization algorithm

Teng Huang, YuChao Fang, Ming Fang

Author Affiliations +

Proceedings Volume 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024); 132511K (2024) https://doi.org/10.1117/12.3039824
Event: 9th International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 2024, Guilin, China

Abstract

In response to the issues of prolonged execution time and susceptibility to local optima in traditional Particle Swarm Optimization (PSO) algorithms for robotic arm trajectory planning, this paper proposes a 3-5-3 polynomial interpolation trajectory planning method based on an improved PSO algorithm. The algorithm dynamically optimizes the inertia weight and learning factor in the PSO algorithm and uses the sum of interpolation point time as the fitness function. Under velocity constraints, it achieves optimal time trajectory planning. The Elite EC66 robotic arm is selected as the research object, and simulations and data analysis are conducted using Matlab. Experimental results indicate that the improved PSO algorithm exhibits significantly enhanced convergence speed and optimization accuracy compared to traditional PSO algorithms, effectively reducing the time required for the robotic arm to complete target tasks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Teng Huang, YuChao Fang, and Ming Fang "Time-optimal trajectory planning for robotic arms based on enhanced particle swarm optimization algorithm", Proc. SPIE 13251, Ninth International Conference on Electromechanical Control Technology and Transportation (ICECTT 2024), 132511K (28 August 2024); https://doi.org/10.1117/12.3039824

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