Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Simultaneous localization and odometry self calibration for mobile robot
Autonomous Robots
Particle filtering based likelihood ratio approach to faultdiagnosis in nonlinear stochastic systems
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
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The diagnosis of mobile robot faults is one of the most serious problems which have to be overcome if one considers applications of mobile robotics in real life, outside laboratories. It would be desirable to perform the diagnosing routine in parallel with the standard activity of the robot, e.g., navigation, but without generating additional computational overhead. Recently the particle filter has become a very popular tool for state estimation of mobile robots. This is because it makes it easier to solve, e.g., the simultaneous localization and mapping problem. One of the biggest drawbacks of the method is its high computational burden closely related to the number of particles used. Therefore, it is often necessary to work out a compromise between the computational time and the quality of results. This work proposes a fault diagnosis system for a mobile robot which is based on a bank of adaptive particle filters. The idea behind is to reduce the total number of particles used in state estimation through activating and deactivating individual filters when needed, as well as by adapting the number of particles in each filter.