基于改进麻雀算法的机械臂轨迹优化

Trajectory Optimization of Manipulator Based on Improved Sparrow Algorithm

摘要: 机械臂轨迹优化是机器人运动控制领域的核心研究问题，旨在生成满足运动学与动力学约束的最优轨迹，以提高运动效率、稳定性及能耗控制能力。本文针对标准麻雀搜索算法在优化精度及局部搜索能力方面的不足，提出了一种基于改进麻雀搜索算法的机械臂轨迹优化方法。该方法结合3-5-3分段多项式插值，以时间最优为优化目标，在满足关节速度、加速度及冲击量等约束的前提下，通过引入动态权重调整策略和蝴蝶算法，增强搜索能力并提高解的质量。实验基于UR5机械臂进行仿真分析，并与标准麻雀算法及粒子群优化算法进行对比。结果表明，改进麻雀搜索算法在收敛速度、全局搜索能力及优化精度方面均优于其他方法，使轨迹规划总耗时较PSO减少15.67%，较SSA减少9.94%。该方法有效提升了机械臂运动的平稳性与执行效率，为机械臂运行复杂任务提供了一种高效的轨迹优化方案。

Abstract: The trajectory optimization of robot arm is the core research problem in the field of robot motion control, aiming at generating the optimal trajectory satisfying the kinematic and dynamic constraints to improve the motion efficiency, stability and energy consumption control ability. Aiming at the shortcomings of the standard sparrow search algorithm in optimization accuracy and local search ability, this paper proposes a robotic arm trajectory optimization method based on the improved sparrow search algorithm. This method combines 3-5-3 piecewise polynomial interpolation, takes the time optimization as the optimization goal, meets the constraints of joint velocity, acceleration and impact, and introduces dynamic weight adjustment strategy and butterfly algorithm to enhance the search ability and improve the quality of the solution. Simulation analysis was carried out based on UR5 robot arm, and comparison was made with standard Sparrow algorithm and particle swarm optimization algorithm. The results show that the improved sparrow search algorithm is superior to other methods in terms of convergence speed, global search ability and optimization accuracy, and the total trajectory planning time is reduced by 15.67% compared with PSO and 9.94% compared with SSA. This method effectively improves the smoothness and execution efficiency of the manipulator, and provides an efficient trajectory optimization scheme for the manipulator to run complex tasks.