R-FAC: Resilient Value Function Factorization for Multi-Robot Efficient Search with Individual Failure Probabilities

Abstract

This paper investigates the resilient multi-robot efficient search problem (R-MuRES), which aims at coordinating multiple robots for the minimal time detection of a 'non-adversarial' moving target. R-MuRES faces challenges like robot malfunctions and withdrawals during task execution, leading to a variable number of searchers and new research hurdles. We propose resilient value function factorization (R-FAC) to construct a central value function resiliently, minimizing mean squared temporal difference (TD) errors across team compositions. R-FAC ensures that individual global maximum (IGM) principles are met, allowing functioning robots to contribute positively. We introduce variational value decomposition network (V2DN) as an instantiation of the R-FAC paradigm, proving superior to brute-force summation in multi-robot search tasks. V2DN is compared with state-of-the-art MuRES solutions and the vanilla VDN, showcasing superior resiliency when robots leave the team. Validation of V2DN is performed in a real multi-robot system in a self-constructed indoor environment, demonstrating its effectiveness and contributing valuable insights to the robotics community.