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A Particle Filtering-Based Approach for Distributed Fault Diagnosis and Estimation of Multi-Robot Systems

[+] Author Affiliations
Elaheh Noursadeghi, Ioannis Raptis

University of Massachusetts Lowell, Lowell, MA

Paper No. DSCC2016-9789, pp. V002T23A006; 10 pages
doi:10.1115/DSCC2016-9789
From:
  • ASME 2016 Dynamic Systems and Control Conference
  • Volume 2: Mechatronics; Mechatronics and Controls in Advanced Manufacturing; Modeling and Control of Automotive Systems and Combustion Engines; Modeling and Validation; Motion and Vibration Control Applications; Multi-Agent and Networked Systems; Path Planning and Motion Control; Robot Manipulators; Sensors and Actuators; Tracking Control Systems; Uncertain Systems and Robustness; Unmanned, Ground and Surface Robotics; Vehicle Dynamic Controls; Vehicle Dynamics and Traffic Control
  • Minneapolis, Minnesota, USA, October 12–14, 2016
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5070-1
  • Copyright © 2016 by ASME

abstract

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.

Copyright © 2016 by ASME

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