A particle filtering-based approach for distributed fault diagnosis and estimation of multi-robot systems

This paper deals with the problem of designing a distributed fault diagnosis and estimation algorithm for multi-robot systems that are subject to faults in the form of abrupt velocity biases. To solve this problem, the multi-robot collective is converted to a network of interconnected diagnostic nodes (DNs) that is deployed to monitor the health of the system. Each node consists of a reduced-order estimator with relative state measurements and an online parameter learning filter. The local estimator executes a distributed variation of the particle filtering algorithm using the local sensor measurements and the fault progression model of the robots. The parameter learning filter is used to obtain an approximation of the severity of faults. Numerical simulations demonstrate the efficiency of the proposed approach.