A multiple particle filters method for fault diagnosis of mobile robot dead-reckoning system
ABSTRACT Fault detection and diagnosis (FDD) is increasingly important for wheeled mobile robots (WMRs). One of the most promising approaches is the so-called particle filter (also known as sequential Monte Carlo) method. In this paper, rule based inference and multiple particle filters are integrated to diagnose hard faults of WMR's dead reckoning system. The rule based inference method is employed to determine the states of the movement of the robot in plane and each state of movement is monitored with a particle filter. This approach presents a general framework to combine domain knowledge with particle filters. The key advantage of the proposed method is that it decreases the size of the state space for each particle filter. As a result, it decreases particle number and increases efficiency and accuracy for each particle filter. Experiment performed on a mobile robot shows the improvement in accuracy and efficiency.
SourceAvailable from: Hoang Ngoc Bach[Show abstract] [Hide abstract]
ABSTRACT: In this paper, a fault diagnosis scheme for wheeled mobile robots is presented. In the fault detection module, a nonlinear observer is designed based on the mobile robot dynamic model. A fault is detected when at least one of the residuals exceeds its corresponding threshold. After the fault is detected, the fault isolation module is activated to isolate three types of fault: right wheel fault, left wheel fault, and other changing dynamic parameter faults. Three simulation examples are performed to show the effect of each fault to the tracking performance of mobile robot in a real situation. The simulation results demonstrate the effectiveness of our proposed approach for fault detection and isolation in wheeled mobile robots.International Journal of Control Automation and Systems 06/2014; 12(3-3):637-651. DOI:10.1007/s12555-013-0012-1 · 1.07 Impact Factor
Conference Paper: Fault detection approach for a 4 - wheel skid steering mobile robot[Show abstract] [Hide abstract]
ABSTRACT: In this paper we develop and we propose a method to detect faults in a four wheel skid steering mobile robot (SSMR). The basic idea behind the method is to use odometry in order to estimate robot parameters. For this purpose we develop kinematic model of the mobile robot that can be used to estimate odometry parameters. These parameters are the radius of right and left wheels RR and RL respectively. Faults are detected using residuals generated from differences between sensors measurements and estimated values. The advantage of the proposed method is that does not need additional sensors and only position encoders are used. Pioneer 3-AT was used as a robotic platform.Industrial Technology (ICIT), 2013 IEEE International Conference on; 01/2013
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ABSTRACT: In this paper we present a theoretical approach of a model based fault diagnosis for a four wheel skid steering mobile robot (SSMR). The basic idea is to use structural analysis based technique in order to generate residuals. For this purpose we develop the kinematic model of the mobile robot that serves to the creation of the structural model of the system. This technique provides the parity equations which can be used as residual generators. The advantage of the proposed method is that can offers feasible solution to residual generation for nonlinear systems. Pioneer 3-AT was used as a robotic platform.Control & Automation (MED), 2013 21st Mediterranean Conference on; 01/2013