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.
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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
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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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ABSTRACT: This paper presents a distributed version of our previous work, called SAFDetection, which is a sensor analysis-based fault detection approach that is used to monitor tightly-coupled multi-robot team tasks.While the centralized version of SAFDetection was shown to be successful, a shortcoming of the approach is that it does not scale well to large team sizes. The distributed SAFDetection approach addresses this problem by adapting and distributing the approach across team members. Distributed SAFDetection has the same theoretic foundation as centralized SAFDetection, which maps selected robot sensor data to a robot state by using a clustering algorithm, and builds state transition diagrams to describe the normal behavior of the robot system. However, rather than processing multiple robots' sensor data centralized on a server, distributed SAFDetection performs feature selection and clustering on individual robots to build the normal behavior model of an individual robot and the entire robot team. Fault detection is also accomplished in a distributed manner. We have implemented this distributed approach on a physical robot team and in simulation. This paper presents the results of these experiments, showing that distributed SAFDetection is an efficient approach to detect both local and interactive faults in tightly-coupled multi-robot team tasks. Compared to the centralized version, this approach provides more scalability and reliability.2009 IEEE International Conference on Robotics and Automation, ICRA 2009, Kobe, Japan, May 12-17, 2009; 01/2009