关键词: 机械臂, 轨迹规划, 改进白鹭群优化算法, 莱维飞行, Wilcoxon秩和检验

Abstract: This paper addresses the issue of overly conservative angular velocity and angular acceleration in segmented polynomial trajectory planning for robotic arms, and the traditional egret swarm optimization algorithm is easy to fall into the local optimum when optimizing the trajectory of the robotic arm, which results in longer movement completion time. To solve this problem, an improved egret swarm optimization algorithm (IESOA) is proposed for time-optimal trajectory planning of the robotic arm. Firstly, Sinusoidal chaotic mapping is introduced to enhance population diversity. Secondly, the long-tailed distribution jumping mechanism of Lévy flight is utilized to avoid the algorithm from falling into a local optimum, thus improving the algorithm?s global search capability. Finally, adaptive t-distribution is employed to dynamically adjust the algorithm?s degree of freedom parameter [v], balancing global exploration with local exploitation, thereby enhancing both optimization accuracy and convergence efficiency. To evaluate the superiority of algorithm, the improved algorithm is compared with five similar algorithms including the traditional ESOA for convergence analysis, Wilcoxon rank-sum tests, and robotic arm trajectory planning experiments. The results demonstrate that, compared with other algorithms, IESOA provides superior optimization accuracy and stability across single-peak, multi-peak, and fixed-dimension multi-peak functions.  Moreover, in the robotic arm trajectory planning simulations, the trajectory curve optimized by IESOA is smooth, without abrupt changes. Additionally, the running time and its standard deviation outperform those comparison algorithms, validating the superiority and robustness of IESOA in solving the time-optimal trajectory planning problem.

Key words: robotic arm, trajectory planning, improved egret swarm optimization algorithm, Lévy flight, Wilcoxon rank sum test