Particle Filter for Fault Diagnosis and Robust Navigation of Underwater Robot

Bo Zhao, Roger Skjetne, Mogens Blanke, Fredrik Dukan

    Research output: Contribution to journalJournal articleResearchpeer-review

    748 Downloads (Pure)


    A particle filter based robust navigation with fault diagnosis is designed for an underwater robot, where 10 failure modes of sensors and thrusters are considered. The nominal underwater robot and its anomaly are described by a switchingmode hidden Markov model. By extensively running a particle
    filter on the model, the fault diagnosis and robust navigation are achieved. Closed-loop full-scale experimental results show that the proposed method is robust, can diagnose faults effectively, and can provide good state estimation even in cases where multiple faults occur. Comparing with other methods, the proposed
    method can diagnose all faults within a single structure, it can diagnose simultaneous faults, and it is easily implemented.
    Original languageEnglish
    JournalI E E E Transactions on Control Systems Technology
    Issue number6
    Pages (from-to)2399 – 2407
    Publication statusPublished - 2014


    • Fault diagnosis
    • Fault Tolerance
    • Particle filter
    • Switch-mode hidden Markov model
    • ROV
    • Underwater navigation


    Dive into the research topics of 'Particle Filter for Fault Diagnosis and Robust Navigation of Underwater Robot'. Together they form a unique fingerprint.

    Cite this