基于改进粒子群算法的机械臂逆运动学求解

Inverse Kinematics Solution of Manipulator Based on Improved Particle Swarm Optimization Algorithm

为了克服一般传统方法在求解机器人逆运动学问题时的不足,通过构建合理的适应函数将机械手逆运动学问题转变为目标优化问题,然后通过群智能算法加以优化,进而求解逆运动学问题。给出一种粒子群算法的优化方法,通过动态惯性权重来调节算法全局搜索和局部搜索的能力,同时引入收缩学习因子来避免算法陷入局部最优。以机器人末端执行设备的位置误差最小,设机器人在运动过程中的能量消耗最小为优化目标,在一种串联仿人机械臂上进行了仿真实验。通过计算机仿真结果便可发现,与其他粒子群算法对比,经过改进的粒子群算法具有较好的收敛速率和求解精度。可以看出,该方案能够合理地进行机器人逆运动学问题的解决。

In order to overcome the shortcomings of conventional methods in solving inverse kinematics problems,the inverse kinematics problem of manipulator was transformed into objective optimization problem by constructing reasonable adaptive function;and then the inverse kinematics problem was solved by swarm intelligence algorithm.In this paper,an improved particle swarm optimization(PSO) algorithm was presented;in which dynamic inertia weights were used to adjust the global and local search capabilities of the algorithm;and shrinkage learning factors were introduced to avoid falling into local optima.To minimize the position error of robot end-effector and minimize the energy consumption of robot in the process of motion,a simulation experiment was carried out on a series humanoid robot arm.Computer simulation results showed that compared with other particle swarm optimization algorithms,the improved particle swarm optimization algorithm had better convergence rate and solution accuracy.It could be seen that the scheme should solve the inverse kinematics of the robot reasonably.