Error State Probability-Based Compliance Control for Peg-in-Hole Assembly

This paper proposes an error state classifier based on the Gaussian Mixture Model (GMM) and a compliance controller for the peg-in-hole assembly task. The concepts of error states and error responses are introduced, and the mapping relationship between them is demonstrated. The GMM-based error state classifier maps the contact forces and torques generated in the assembly process to the probability of different error states. Furthermore, a new compliance controller based on error state probability is proposed to precisely adjust the pose error of the peg and hole. To validate the proposed method, a comparison with Admittance Control (ADC), Variable Compliance Center Control (VCC) and Feature-based Compliance Control (FBCC) is conducted in the task of inserting the vial into the centrifugal hole in the chemical experiment. The results show that the proposed method has a higher insertion success rate and less completion time and can significantly reduce the contact forces and torques during the insertion process. Note to Practitioners —This paper presents a novel error state classifier and a compliance controller for peg-in-hole assembly tasks. Compared to traditional methods, the proposed method can significantly reduce the forces and torques during the assembly process. The error state classifier is obtained by fitting the distribution of contact forces and torques under different error states using the GMM via offline training. During assembly, the classifier calculates the probability of different error states according to the forces and torques, and the proposed compliance controller then uses the error state probability and forces and torques to accurately adjust the pose error, thus ensuring assembly success and quality.