Fault detection and identification for dead reckoning system of mobile robot based on fuzzy logic particle filter

To deal with fault detection and diagnosis with incomplete model for dead reckoning system of mobile robot, an integrative framework of particle filter detection and fuzzy logic diagnosis was devised. Firstly, an adaptive fault space is designed for recognizing both known faults and unknown faults, in corresponding modes of modeled and model-free. Secondly, the particle filter is utilized to diagnose the modeled faults and detect model-free fault according to the low particle weight and reliability. Especially, the proposed fuzzy logic diagnosis can further analyze model-free modes and identify some soft faults in unknown fault space. The MORCS-1 experimental results show that the fuzzy diagnosis particle filter (FDPF) combinational framework improves fault detection and identification completeness. Specifically speaking, FDPF is feasible to diagnose the modeled faults in known space. Furthermore, the types of model-free soft faults can also be further identified and diagnosed in unknown fault space.