Time optimal trajectory planning of robotic arm based on improved sand cat swarm optimization algorithm

Abstract

In order to address the issue of automatic charging for electric vehicles, a hanging automatic charging system was proposed, with a particular focus on the time-optimal trajectory planning of the robotic arm within the system. Additionally, a multi-strategy improved Sand Cat Swarm Optimization Algorithm (YSCSO) was put forth as a potential solution. The 0805A six-axis manipulator was selected as the research object, and a kinematic model was constructed using the D-H parameter method. The 5-7-5 polynomial interpolation function was proposed and solved to construct the motion trajectory of the robotic arm joint. The cubic chaos-refraction inverse learning, introduced to initialize the population based on the sand cat swarm algorithm SCSO, balances the relationship between the elite pool weighted guided search behavior and the spiral Lévy flight predation behavior through the use of a dynamic nonlinear sensitivity range. Furthermore, the vigilance behavior mechanism of the sand cat was increased to improve the overall optimization performance of the algorithm. The proposed method was applied to 36 benchmark functions of global optimization, and the improvement strategy, convergence behavior, population diversity, exploration, and development of the algorithm were experimentally analyzed. The results demonstrated that the proposed method exhibited superior performance, with 80.86% of the test results significantly different from those of the comparison algorithm. Three constrained mechanical design optimization problems were employed to assess the algorithm’s practicality in engineering applications. Subsequently, the algorithm was applied to the optimal trajectory planning of a robotic arm, resulting in a significant reduction in the optimized joint motion time, a smooth and continuous kinematic curve devoid of abrupt changes, and a 42.72% reduction in motion time. These findings further substantiate the theoretical feasibility and superiority of the algorithm in addressing engineering challenges.