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- Title
基于改进粒子群算法的机器人时间最优轨迹规划.
- Authors
程浩田; 祝锡晶; 黎相孟; 赵 晶; 冯琪渊; 丁帅帅
- Abstract
For the shortcomings of the standard particle swarm optimization algorithm in the time-optimal trajectory planning of the robot, such as easy to fall into the local optimum and premature, an improved algorithm with fast convergence was proposed. The algorithm uses a dynamic learning factor strategy to replace the traditional fixed learning factor, and on this basis uses the “3-5-3”hybrid polynomial interpolation function for planning, and finally completes the fitting of the motion trajectory of the robot joints in the MATLAB simulation software. The research results show that both the local convergence speed and global convergence speed of the improved particle swarm algorithm are better than those of the standard particle swarm algorithm, and the time required for trajectory planning is reduced by 26% compared with purely using the“3-5-3”hybrid polynomial. The joint trajectory is smooth and continuous, which proves the superiority, effectiveness and feasibility of the improved algorithm.
- Publication
Packaging & Food Machinery, 2022, Vol 40, Issue 2, p38
- ISSN
1005-1295
- Publication type
Article
- DOI
10.3969/j.issn.1005-1295.2022.02.007