Error Identification and Recovery in Robotic Snap Assembly

TL;DR

This paper introduces a predictive method for robotic snap assembly errors using functional PCA and decision trees, enabling early error detection and recovery to improve assembly reliability.

Contribution

It presents a novel approach combining fPCA, decision trees, and SVMs for pre-error prediction and recovery in robotic snap assembly tasks.

Findings

Accurately predicts error states before failure occurs

Enables robot to perform error recovery motions

Improves assembly success rate with early error detection

Abstract

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.