摘要
针对在实际运动过程中,机械臂存在着许多运动约束的问题,提出了一种多约束条件下基于改进粒子群算法的最优时间轨迹规划方法,该方法在考虑了机械臂角度、速度、加速度等多个约束条件的情况下,对标准粒子群算法进行改进,使粒子群在混沌状态和稳定状态之间切换,并且在粒子到达局部极值时及时改变速度,从而增加整个粒子群的多样性。通过与标准粒子群算法和遗传算法的比较,其仿真结果表明,改进后的算法在满足多个约束条件的同时,整个粒子群在优化过程中不易收敛到局部最优解,使得机械臂的运动时间有所缩短,验证了该算法的可行性和有效性。
Shortening motion time is of great significance for improving the efficiency of manipulators.However,there are many kinematic constraints in pursuit of the shortest motion time.In this study,an approach to the time-optimal trajectory planning with multiple constraints is proposed by using a modified particle swarm optimization algorithm.The multiple constraints of each joint including angle,velocity and acceleration are considered.The algorithm makes the swarm switch between the chaotic state and the stable state while changing the particle’s velocity in time when it reaches the local extremum.Thus,it increases the diversity of the whole particle swarm.Simulation results show that the motion time of 6 degree-of-freedom manipulator is reduced.The proposed algorithm successfully optimized the time-optimal trajectory while satisfying the multiple constraints,and the whole particle swarm is less likely to converge to a locally optimal solution in the process of optimizing.The effectiveness of the proposed algorithm is confirmed by comparing it with the classic particle swarm optimization and genetic algorithm.
作者
马腾宇
胡孝楠
郭新华
MA Tengyu;HU Xiaonan;GUO Xinhua(School of Mechanical and Electronic Engineering,Wuhan University of Technology,Wuhan 430070,China)
基金
中央高校基本科研业务费专项资金资助项目(2017III045)
关键词
六自由度机械臂
时间最优
轨迹规划
多重约束
6 degree-of-freedom manipulator
time-optimal trajectory
path planning
multiple constraints
