基于神经动力学的机器人位置/力规划方案

Position/force planning scheme for robots based on neural dynamics

针对机器人在实际生产中因与环境相接触而可能导致损坏的问题,从运动学角度提出一种通过刚性系数将机器人位置误差和接触力误差建模和解耦的位置/力规划方案.该方案从机器人逆运动学出发,将末端执行器的接触力与位置误差建模为关于机器人速度与位置间的关系式,并且引入一种神经动力学模型.使用伪逆法对所提方案进行求解,将所得的解作为控制信号驱动机器人完成任务.数值仿真与实验结果表明:所提方案能够有效控制机器人完成轨迹跟踪的任务,末端执行器的接触力能够收敛到期望的接触力.

To solve the problem that the robot might be damaged due to the contact with the environment in the actual production,from the perspective of kinematics,a position/force planning scheme was proposed to model and decouple the robot position error and contact force error through the stiffness coefficients.Based on the inverse kinematics of a robot,the contact force and position error of the end effector were modeled as equations containing the velocity and position of the robot,and a neural dynamics model was introduced. The pseudo-inverse method was used to solve the proposed scheme,and the obtained solution was used as the control signal to drive the robot to complete the task. Numerical simulations and experimental results show that the proposed scheme can effectively control the robot to complete the trajectory tracking task,and the contact force of the end effector can converge to the desired contact force.