Time-optimal trajectory planning algorithm for robotic arms based on ADFMSSA chaotic optimization

Aiming at the problems that the Sparrow Search algorithm (SSA) is prone to fall into local extreme points in the early stage and has low optimization accuracy in the later stage, an adaptive step-size factor mutation sparrow search algorithm (ADFMSSA) is proposed. Firstly, the population is initialized through the improved Tent mapping chaotic sequence to enhance the randomness and ergoability of the initial population and improve the global search ability of the algorithm; The Caucy variation and Tent chaotic perturbation were introduced again to expand the local search ability, enabling the individuals trapped in the local extreme points to break free from the restrictions and continue the search. The algorithm was further improved by combining the adaptive step factor. Finally, an adaptive adjustment strategy for the number of explorers and followers is proposed. The changes in the number of explorers and followers at each stage are utilized to enhance the global search ability in the early stage and the local deep mining ability in the later stage of the algorithm, and improve the optimization accuracy of the algorithm. Through experiments on 23 benchmark functions, the performance of ADFMSSA was tested and its performance was compared with that of other algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and traditional Sparrow Search Algorithm (SSA). The experiment verified the superiority of ADFMSSA, and further optimized the generation time of the robot trajectory through the improved sparrow search algorithm. The experimental results show that this method can not only accelerate the convergence speed and enhance the global search ability, but also ensure the smoothness of the trajectory. The simulation results show that ADFMSSA outperforms GA, PSO and SSA in terms of accuracy, convergence speed, stability and robustness.