Error Identification and Recovery in Robotic Snap Assembly
This addresses the problem of assembly failures in robotics for manufacturing, but it is incremental as it builds on existing methods for error prediction.
The paper tackles the problem of predicting failures in robotic snap joint assembly by proposing a method to identify error states before they occur, enabling timely recovery; experimental results show that error states can be correctly estimated and a robot can recover from them.
Existing methods for predicting robotic snap joint assembly cannot predict failures before their occurrence. To address this limitation, this paper proposes a method for predicting error states before the occurence of error, thereby enabling timely recovery. Robotic snap joint assembly requires precise positioning; therefore, even a slight offset between parts can lead to assembly failure. To correctly predict error states, we apply functional principal component analysis (fPCA) to 6D force/torque profiles that are terminated before the occurence of an error. The error state is identified by applying a feature vector to a decision tree, wherein the support vector machine (SVM) is employed at each node. If the estimation accuracy is low, we perform additional probing to more correctly identify the error state. Finally, after identifying the error state, a robot performs the error recovery motion based on the identified error state. Through the experimental results of assembling plastic parts with four snap joints, we show that the error states can be correctly estimated and a robot can recover from the identified error state.