Error Recovery Action Planning for Robots Based on Deep Semantic Information of Failures

Abstract

Industrial robots are required to recover from errors autonomously under uncertain environment. In this paper, we propose a recovery action planning system by considering semantic information behind the detected error information. The proposed system uses Conceptual Graph to classify errors and Bayesian Network to evaluate the uncertainty in oeder to determine feasible repair strategies. We demonstrate the effectiveness of the proposed decision model by simulations of an assembly task in which actions of multiple robots affect each other.