摘要
针对传统粒子群算法在求解机械臂轨迹优化问题时存在的搜索精度不够、容易陷入局部最优等问题,提出了一种混沌莱维粒子群优化算法(TLPSO)。对标准粒子群算法(PSO)进行优化,引入Tent混沌映射和莱维飞行的方法进行改进,提高了算法的搜索能力和跳出局部最优解能力。以六自由度机械臂为研究对象,建立时间优化目标模型,以3-5-3多项式插值方法为基础对其进行轨迹规划。将该算法应用于求解多种测试函数以及机器人时间最优轨迹规划问题,与遗传算法改进的粒子群算法(PSO-GA)、鲸鱼优化算法(WOA)和布谷鸟搜索算法(CS)相比,该算法取得了较好的效果。优化后得到的机械臂位移、速度和加速度曲线平滑、无突变。结果表明,所提出的优化算法能够有效降低轨迹的执行时间。
Aiming at the problems that the traditional particle swarm optimization algorithm has insufficient search accuracy and is easy to fall into local optimum when solving the trajectory optimization problem of manipulator,a Tent-Levy particle swarm optimization algorithm(TLPSO)is proposed.The standard particle swarm algorithm(PSO)is optimized and improved by introducing Tent chaotic mapping and Levy flight to improve the algorithm′s searching ability and the ability to jump out of the local optimal solution.Taking the 6-degree-of-freedom robotic arm as the research object,a time-optimized objective model is established,and its trajectory planning is based on the 3-5-3 polynomial interpolation method.The algorithm is applied to solve a variety of test functions as well as the robot time-optimal trajectory planning problem,which achieves better results compared to the particle swarm algorithm with genetic algorithm improvement(PSO-GA),the whale optimization algorithm(WOA),and the cuckoo search algorithm(CS).The optimized displacement,velocity and acceleration curves of the robotic arm are smooth and free from abrupt change.The results show that the algorithm can effectively reduce the execution time of the trajectory.
作者
盖荣丽
王康
王晓红
GAI Rongli;WANG Kang;WANG Xiaohong(College of Information Engineering,Dalian University,Dalian 116622,China)
出处
《组合机床与自动化加工技术》
北大核心
2025年第5期101-105,109,共6页
Modular Machine Tool & Automatic Manufacturing Technique
基金
辽宁省自然基金指导计划项目(2019ZD0309)。
