A Bayesian Framework for Simultaneous Robot Localization and Target Detection and Engagement

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Conference Proceeding
IEEE International Conference on Intelligent Robots and Systems, 2018, pp. 7151 - 7157
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© 2018 IEEE. This paper presents a framework for engaging a target while approaching it from a long distance, using observation from sensors on-board a mobile robot. The proposed framework consists of two multi-stage Bayesian approaches to reliably detect and accurately engage with the target under uncertainties. The multi-stage localization approach localizes the robot and the target in a global coordinate frame. Their locations are estimated sequentially when the robot is at a long distance from the target, whereas they are localized simultaneously when the target is in the close vicinity. In the multi-stage target observation approach, a level of confidence and the associated probability of detection of the sensor are defined to make the target detectable in maximal occasions. This allows the extended Kalman filter to be implemented for the target engagement. The proposed framework was implemented on an unmanned ground vehicle equipped with multiple sensors. Results show the effectiveness of the proposed framework in solving real-world problems.
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