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  • 1.
    Sundvall, Paul
    KTH, School of Electrical Engineering (EES).
    Mobile robot fault detection using multiple localization modules2006Licentiate thesis, monograph (Other academic)
    Abstract [en]

    Most applications in service robotics require that the position of the robot is accurately known. Faults affecting the localization system can thus have serious effects on the overall performance. This includes internal hardware and software faults, but external disturbances and faults from the surrounding dynamical and complex environment are even more common in service robotics applications.

    This thesis makes two main contributions. The first one is a method for detecting faults affecting the localization system of a mobile robot. Most fault detection systems work with detailed models at sensor level, where sensor data is processed to decide if the system is in a faulty state or not. While this is often a powerful approach, it requires reliable models of the environment, sensor noise and the robot’s motion. The proposed approach is based on the observation that most of the modelling required for fault detection is shared with robot localization algorithms. The problems of localization and navigation have been extensively studied in the robotics community, and there exist many reliable methods and robust implementations of such systems. By combining the outputs from several high-level localization modules, and hence avoiding working with raw sensor data and detailed models, it is possible to detect faults affecting the robot. In this thesis, a low complexity model of such a combined system is proposed, and a detailed discussion of the corresponding design choices is given. An Extended Kalman filter is used to calculate the posterior probability distribution of the outputs of the localization modules. The alarm decision is made based on the Mahalanobis distance of the innovations and a CUSUM test. This approach is very flexible and does not need direct access to sensor data, nor modification of existing localization algorithms. The proposed method has been implemented and tested on an ActivMedia service robot. Odometry and a laser based scan matching method, described below, were used as position modules. The experimental results show that the approach works. The second contribution of this thesis is a method to increase the efficiency of point-to-point search in a scan matching algorithm. Scan matching is a method to estimate the relative displacement of a laser-scanning sensor (light radar) between data acquired at two positions. Scan matching is a good independent complement to other sensors like odometry and sonars. Here, scans are matched by maximization of a score function. This function is calculated from the distance between every point in the scan to be matched and the closes point in the reference scan. Straightforward search needs as many checks as the square of the number of points in the scan. A method to reduce the search space

    is presented that significantly reduces the effort for score calculation.

  • 2.
    Sundvall, Paul
    et al.
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS. KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.
    Fault detection for mobile robots using redundant positioning systems2006In: 2006 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA), VOLS 1-10, NEW YORK, NY: IEEE , 2006, p. 3781-3786Conference paper (Refereed)
    Abstract [en]

    Reliable navigation is a very important part of an autonomous mobile robot system. This means for instance that the robot should not lose track of its position, even if unexpected events like wheel slip and collisions occur. The standard approach to this problem is to construct a navigation system that is robust in itself. This paper proposes that detecting faults can also be made outside the normal navigation system, as an additional fault detector. Besides increasing the robustness, a means for detecting deviations is obtained, which can be important for the rest of the robot system, for instance the top level planner. The method uses two or more sources of robot position estimates, and compares them to detect unexpected deviation without getting deceived by drift or different characteristics in the position systems it gets information from. Both relative and absolute position sources can be used. meaning that existing positioning systems already implemented can be used in the detector. For detection purposes, an extended Kalman filter is used in conjunction with a CUSUM test. The detector is able to not only detect faults, but also give an estimate of when the fault occurred, which is useful for doing fault recovery. The detector is easy to implement, as it requires no modification of existing systems. Also the computational demands are very low. The approach is implemented and demonstrated on a mobile robot, using odometry and a scan matcher as sources of position information. It is shown that the system is able to detect wheel slip in real-time.

  • 3.
    Sundvall, Paul
    et al.
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Jensfelt, Patric
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Wahlberg, Bo
    KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Computer Science and Communication (CSC), Centres, Centre for Autonomous Systems, CAS.
    Fault detection using redundant navigation modules2007In: Fault Detection, Supervision and Safety of Technical Processes 2006, Elsevier, 2007, Vol. 6, p. 522-527Conference paper (Refereed)
    Abstract [en]

    Mobile robots and other moving vehicles need to know their position with a certain level of confidence. In this chapter, a method is proposed to handle faults in the navigation system by considering the outputs of existing navigation modules rather than processing sensor data directly. The proposed method needs only a simple model for drift and noise, an extended Kalman filter and a CUSUM test. The approach is demonstrated using two providers, odometry and scan matching. It can handle position information given in different coordinate systems and does not require any modification of existing navigation modules. Promising experimental results are shown.

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