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This study presents a particle filter method for wheeled mobile robots (WMRs) to simultaneously diagnose faults and perform accurate dead reckoning, even with sensor noise and wheel slippage.

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Area of Science:

  • Robotics
  • Control Systems
  • Artificial Intelligence

Background:

  • Robust dead reckoning for wheeled mobile robots (WMRs) is challenging due to sensor faults and wheel slippage.
  • Simultaneous estimation of discrete fault models and continuous states is crucial for reliable fault diagnosis and accurate dead reckoning.
  • Particle filters are effective for hybrid system estimation and widely applied in WMRs applications.

Purpose of the Study:

  • To propose a systematic method for concurrent fault diagnosis and dead reckoning within a particle filter framework.
  • To utilize laser range finder data, accounting for potential noise and faults, for precise dead reckoning.
  • To demonstrate the accuracy and efficiency of the integrated approach.

Main Methods:

  • Development of a perception model for laser range finders, including faulty scan handling.
  • Formulation of kinematics for both normal and various fault models of WMRs.
  • Implementation and discussion of a particle filter designed for simultaneous fault diagnosis and dead reckoning.

Main Results:

  • Experimental validation of the proposed method's accuracy in dead reckoning.
  • Demonstration of the method's efficiency in real-time applications.
  • Successful concurrent estimation of robot states and fault diagnosis.

Conclusions:

  • The presented particle filter framework effectively integrates fault diagnosis and dead reckoning for WMRs.
  • The method addresses challenges posed by sensor noise and internal robot faults.
  • This systematic approach enhances the reliability and precision of WMR navigation systems.