机械科学与技术2026，Vol.45Issue(8)：1349-1369,21.DOI:10.13433/j.cnki.1003-8728.20240147
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
Application of Multi-strategy Improved Snake Optimization Algorithm in Time-optimal Trajectory Planning of Robotic Arm
摘要
Abstract
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.关键词
5-7-5多项式/混沌-动态反向学习/减法平均优化器/搜寻巢穴/边界选择/垂直交叉Key words
5-7-5 polynomial/chaos-dynamic reverse learning/subtraction average optimizer/nest searching/boundary-based selection/vertical crossover分类
信息技术与安全科学引用本文复制引用
游智超,卢振坤,夏冰寒,姜永文..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[J].机械科学与技术,2026,45(8):1349-1369,21.基金项目
国家自然科学基金项目(61561008)、广西科技重大专项(桂科 AA23073013)及广西民族大学研究生教育创新计划(gxun-chxs2024119) （61561008）
