Predictive System Reconfiguration for Fulfillment of Future Mission Requirements

Abstract

Equipment failures can cause major disruptions to system operations. Although this is the case for engineered systems in general, it is especially applicable to autonomous systems as an operation or maintenance crew may not be available to remediate the situation during operation. Autonomous vehicles, for instance, may be performing critical missions miles away from the nearest manned support personnel when a failure occurs. In this work we propose and test an approach for automated predictive reconfiguration of an autonomous vehicle with the goal of delaying the occurrence of failures that would otherwise compromise mission accomplishment. The proposed approach is based on the Monte Carlo Tree Search (MCTS) method and assumes the availability of models describing relevant failure mechanisms and the relation between degradation and performance for each failure mode. Our solution introduces novel means for taking into account the uncertainty resulting from estimation of relevant parameters and states , with benefits in terms of reduction of computational cost compared to existing solutions. The proposed approach is successfully tested in a simulation environment.