多策略改进蛇优化算法在机械臂时间最优轨迹规划的应用OA

The multi-strategy improved snake optimization algorithm(MSSO)is proposed to address the problem of optimal trajectory planning for the charging manipulator of electric vehicles.Taking the AR4 six-axis manipulator as the research object,its kinematic model is established,and the joint motion trajectory is constructed using a 5-7-5 polynomial.Based on the snake optimization algorithm,MSSO introduces chaotic-dynamic reverse learning initialization of populations and utilizes a subtraction average optimizer to enhance the convergence speed in the exploration phase.Additionally,it incorporates a snake colony nest searching mechanism to mitigate the randomness and blindness of search behavior.The algorithm adopts a boundary-based selection of refracted reverse learning to replace local inferior solutions and proposes a vertical crossover strategy based on adaptive Gaussian-Cauchy mutation to enhance the algorithm's ability to escape local optima.MSSO's excellent solving performance is validated through tests against CEC2005 and CEC2022 and comparisons with six metaheuristic algorithms.The algorithm is applied to the trajectory planning of the robot arm with time as the optimization objective.The optimized joint running time is greatly shortened,and the obtained kinematic curve is smooth and continuous without mutation,which validates the theoretical feasibility and superiority of the algorithm in dealing with engineering problems.