改进灰狼算法在搬运机器人轨迹规划中的应用

Application of Improved Gray Wolf Algorithm in Trajectory Planning of Pallet Handling Robot

为提高托盘式搬运机器人的运行稳定性,提出一种基于改进灰狼算法的机器人加速度最优轨迹规划方法。针对灰狼算法局部收敛、寻优性能不足等问题,引入Logistic-Tent混沌映射,优化初始种群;引入差分优化算法,提高全局搜索能力;引入淘汰进化机制,优化种群结构,从而全面提升优化性能。仿真结果表明,对比标准灰狼算法和粒子群算法,改进灰狼算法在不同类型的测试函数中具有更好的收敛速度和算法精度;在搬运机器人轨迹规划的应用中,经过该算法优化后的机器人最大关节角加速度下降了44.11%,大幅提高了运行稳定性。

In order to improve the running stability of the pallet handling robot,an optimal trajectory planning method for robot acceleration based on the improved gray wolf algorithm is proposed.Aiming at the problems of local convergence and insufficient optimization performance of gray wolf algorithm,the Logistic-Tent chaotic map is introduced to optimize the initial population;the differential optimization algorithm is introduced to improve the global search ability;the elimination evolution mechanism is introduced to optimize the population structure and improve the optimization performance in all-round way.Compared with the standard gray wolf algorithm and the particle swarm algorithm,simulation results show that improved gray wolf algorithm has better convergence speed and algorithm accuracy in different types of test functions.In the application of the trajectory planning of the handling robot,after the optimization of the algorithm,the maximum joint angular acceleration of the robot is reduced by 44.11%,which greatly improves the running stability.