Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.558767
Title: Stochastic beacon selection for mobile robots : collective localization using Kalman filters
Author: Pratumshat, Vorawut
Awarding Body: University of Reading
Current Institution: University of Reading
Date of Award: 2010
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Abstract:
In the domain of a homogenous multi robot system where only single beacon/robot can be activated during the sensor measurement, the problem arises such that which robot should be selected in order to prevent signal interference and more importantly to improve the accuracy of the team position estimation. This research addresses the solution to the problem of a stochastic beacon selection for mobile robot collective localization system. The 'stochastic' and 'collective' terms here differentiate the proposed work from other work. The term 'stochastic' means that beacon selection algorithm does not require a constant or arbitrary topology of sensor/robot formation during the robot navigation. In addition the term 'collective' implies that the shared knowledge among the robot group can be combined and each member can benefit from their team mate resources. The proposed beacon selection algorithm is accomplished by the use of Kalman filter gain as a measure of information between robot relative measurements. The computation of the Kalman gain value and its relation to information quantities contained in the measurements are derived and graphically described through the position and direction of the measurements and the 30" ellipse of the filter a priori error estimates. The preliminary investigations show that the more information results in high Kalman gain and hence high reduction in position uncertainty, i.e. better result in state correction. Therefore the beacon robot whose the set of relative measurements that attains in the highest value of Kalman filter gain, i.e. the most informative measurements, will be selected to activate as a beacon. The proposed stochastic beacon selection algorithm based on Kalman filter gain is then applied to the localization framework based on a recursive estimator, an extended Kalman filter (EKF). The 2D computer based simulations on a fleet of four robots are setup in order to assess the improvement of the fleet localization. The numerical result shows that NRMS errors from EKF are smaller in all states compared to the error from the odometry system by one order. Therefore the EKF localization with relative information significantly improves the accuracy of the odometry measurement. Then the beacon selection algorithm based on Kalman filter gain is added to the simulations. The results show the further improvement of the team localization accuracy over sequential algorithm. The beacon selection strategy based on Kalman filter gain is proved to perform better than sequential algorithm, especially when robots move in random motion. The extension to 3D simulation with the robot group behaviours, cornfield vector and flocking behaviour, also yield the same results obtained from 2D.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.558767  DOI: Not available
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