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.
"The TCG constructed from bond-graph model of physical process provided basic for isolation methodology. In  , a particle filter was used for fault detection and isolation. Several discrete states were used for each fault mode. "
[Show abstract][Hide abstract] ABSTRACT: This paper presents implementation and experimental validation of fault detection algorithm for sensors and motors of Automatic Guided Vehicle (AGV) system based on multiple positioning modules. In this paper, firstly the system description and mathematical model of differential drive AGV system are described. Then, characteristics of each positioning modules are explained. On the next step, the fault detection based on multiple positioning modules is proposed. The fault detection method uses two or more positioning systems and compares them to detect unexpected deviation effected by drift or different characteristics of each positioning systems. For fault detection algorithm, an Extended Kalman Filter (EKF) is used. EKF calculates the measurement probability distribution of the AGV position for nonlinear models driven by Gaussian noise. Using the probability distribution of innovation obtained from EKF, it is possible to test if the measured data are fit with the models. When the faults such as sensors malfunction, wheel slip or motor broken, the models will not be valid and the innovation will not be Gaussian and white. The pairwise differences between the estimated positions obtained from sensors are called as residue. Fault isolation is obtained by examining the biggest residue. Finally, to demonstrate the capability of the proposed algorithm, the algorithm is implemented on a differential drive AGV system, which uses encoder, laser scanner, and laser navigation system to obtain position information. The experimental result shows that the proposed algorithm successfully detects faults when the faults occur.
"In order to increase the safety and reliability of mobile robots, the fault diagnosis and fault tolerant control must be considered. Over the past two decades, several researchers have been investigating WMR fault detection and fault tolerant control          . Survey papers by Luo  and Duan  provide excellent overviews of recent research. "
[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 · 0.95 Impact Factor
"Advantages of the proposed FDI method were that they were based on the nonlinear dynamic model of a robot manipulator, did not require acceleration measurements , and were independent of the controller. In , a multiple particle filter based approach to fault diagnosis of the dead reckoning system of mobile robots was presented . A rule-based inference engine was employed to determine which particle filter would be associated with the current movement state. "
[Show abstract][Hide abstract] ABSTRACT: A combined logic- and model-based approach to fault detection and identification (FDI) in a suction foot control system of
a wall-climbing robot is presented in this paper. For the control system, some fault models are derived by kinematics analysis.
Moreover, the logic relations of the system states are known in advance. First, a fault tree is used to analyze the system
by evaluating the basic events (elementary causes), which can lead to a root event (a particular fault). Then, a multiple-model
adaptive estimation algorithm is used to detect and identify the model-known faults. Finally, based on the system states of
the robot and the results of the estimation, the model-unknown faults are also identified using logical reasoning. Experiments
show that the proposed approach based on the combination of logical reasoning and model estimating is efficient in the FDI
of the robot.
Journal of Control Theory and Applications 02/2009; 7(2):157-162. DOI:10.1007/s11768-009-6077-y
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