Adaptive Causal Models for Fault Diagnosis and Recovery in Multi-Robot Teams

Abstract

This paper presents an adaptive causal model method (adaptive CMM) for fault diagnosis and recovery in complex multi-robot teams. We claim that a causal model approach is effective for anticipating and recovering from many types of robot team errors, presenting extensive experimental results to support this claim. To our knowledge, these results show the first, full implementation of a CMM on a large multi-robot team. However, because of the significant number of possible failure modes in a complex multi-robot application, and the difficulty in anticipating all possible failures in advance, our empirical results show that one cannot guarantee the generation of a complete a priori causal model that identifies and specifies all faults that may occur in the system. Instead, an adaptive method is needed to enable the robot team to use its experience to update and extend its causal model to enable the team, over time, to better recover from faults when they occur. We present our case-based learning approach, called LeaF (for learning-based fault diagnosis), that enables robot team members to adapt their causal models, thereby improving their ability to diagnose and recover from these faults over time