Robot peg-in-hole assembly based on contact force estimation compensated by convolutional neural network

作者：Zhang, Tie^*; Liang, Xiaohong; Zou, Yanbiao

来源：CONTROL ENGINEERING PRACTICE, 2022, 120: 105012.

DOI：10.1016/j.conengprac.2021.105012

• Contact force estimation
• Convolutional neural network
• Supervised learning
• Position and orientation adjustment algorithm
• Robot peg-in-hole assembly

In order to reduce the cost of robot integrated application, this paper proposes a robot peg-in-hole assembly method without force/torque sensor. A disturbance observer based on generalized momentum and joint motor torque is employed to obtain the force information on the end of the robot, as a replacement of force/torque sensor. Since the contact between the robot end and the external environment excites the unmodeled dynamics of robot, which leads to insufficient estimation accuracy of the disturbance observer, a convolutional neural network (CNN) supervised learning method to calibrate the force estimation algorithm is proposed. Then, the peg-in-hole assembly mechanism of a ball-end round shaft is analyzed, and on this basis, an assembly strategy and a fuzzy proportional-derivative orientation adjustment algorithm are proposed. Finally, the feasibility of the proposed method of robot peg-in-hole assembly without force/torque sensor is verified by experiment.