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Parameter estimation based-FDI method enhancement with mixed particle filter

Abstract : Mobile robots have a crucial role in many domains. Currently, service robots can be found in our homes, transportation robots in warehouses, logistic robots in factories and even on Mars, where rovers are exploring its surface. These intelligent machines are becoming increasingly autonomous, as they have to achieve longer and longer missions. Thus, to maintain autonomy and to guarantee material security, Fault Detection and Isolation (FDI) is a mandatory task. A fault causes a mission to fail or, worse, it may damage materials and operators if the robot goes out of control. In this paper, we propose to address the FDI process. First, we present two modified versions of two known methods based on parameter estimation. Then, a mixed version of Particle Filter (PF) which combines a standard PF and a Gaussian PF to generate a better performing FDI method is proposed. These methods are studied in the case of a wheel block fault. Simulation and real robot experimentation results prove the added value of our proposed FDI method over the other two approaches.
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https://hal.inrae.fr/hal-03204814
Contributor : Kareen Louembe <>
Submitted on : Wednesday, April 21, 2021 - 7:06:49 PM
Last modification on : Thursday, April 22, 2021 - 3:05:50 AM

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Mahmoud Almasri, Nicolas Tricot, Roland Lenain. Parameter estimation based-FDI method enhancement with mixed particle filter. Neurocomputing, Elsevier, 2020, 403, pp.441-451. ⟨10.1016/j.neucom.2020.04.048⟩. ⟨hal-03204814⟩

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