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 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
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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
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