Robotic Curved Surface Tracking with a Neural Network for Angle Identification and Constant Force Control based on Reinforcement Learning

Abstract

Aiming to solve the problem that the contact force at a robot end effector when tracking an unknown curved-surface workpiece is difficult to keep constant, a robot force control algorithm based on reinforcement learning is proposed. In this paper, a contact model and force mapping relationship are established for a robot end effector and surface. For the problem that the tangential angle of the workpiece surface is difficult to obtain in the mapping relationship, a neural network is used to identify the tangential angle of the unknown curved-surface workpiece. To keep the normal force of the robot end effector constant, a compensation term is added to a traditional explicit force controller to adapt to the robot constant force tracking scenario. For the problem that the compensation term parameters are difficult to select, the reinforcement learning algorithm A2C (advantage actor critic) is used to find the optimal parameters, and the return function and state values are modified in the A2C algorithm to satisfy the robot tracking scenario. The results show that the neural network algorithm has a good recognition effect on the tangential angle of the curved surface. The force error between the normal force and the expected force is substantially within ± 2 N after 60 iterations of the robot force control algorithm based on A2C; additionally, the variance of the force error decreases by 50.7%, 34.05% and 79.41%, respectively, compared with the force signals obtained by a fuzzy iterative algorithm and an explicit force control with two sets of fixed control parameters.