改进PSO在欠驱动机器人运动规划中的应用研究

Study and application of improved PSO in the motion planning of under-actuated robot

研究欠驱动六足仿生机器人简化模型(Rock&Roll Robot-RRRobot)运动规划问题。根据RRRobot动力学模型,以能量损耗最小为目标,提出一种基于遗传算子的改进粒子群算法,对RRRobot运动规划进行优化。仿真实验结果表明,所提出的算法对解决运动规划的优化问题是有效的,与基本粒子群算法相比,改进的粒子群算法对RRRobot模型终端位形的误差有明显的改善作用。

This paper researches the motion planning of under-actuated bionic robot which named Rock ＆ Roll Robot-RRRobot. According to the dynamic model of RRRobot, the objective is minimizing the energy loss; the improved Particle Swarm Optimization （PSO） algorithm based on genetic operator is proposed. At last, it gets motion planning of the robot from an initial state to a target state, and optimizes the motion planning on RRRobot. The simulation results show the improved PSO algorithm, compared with the PSO algorithm, the terminal configuration of the RRRobot error and system power consumption has significantly improvement.