Sensorless External Force Detection Method for Robot Arm Based on Error Compensation Using BP Neural Network

This paper proposes an external force detection method for humanoid robot arm without using joint torque sensors, which can detect the external force of the joint space in real time during the operation of the robot. We first analyzed the structure of the humanoid robot arm we designed, and then established the external force detection model of the robot arm based on robot dynamics and motor dynamics. Subsequently, analyses were conducted on the error of the detection model and the dynamic model error of the robot arm is compensated by using the artificial neural network method to obtain more accurate external force value for the robot arm. In experiment, the accuracy test and the collision test were performed on the detected extern forces of the robot arm. The results show that the method can effectively improve the detection accuracy of the robot arm, and the robot arm can realize the real-time collision detection during its static and running states, which can ensure the safe operation of the robot.