Article
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A Possibilistic Formulation of Autonomous Search for Targets
Version 1
: Received: 23 May 2024 / Approved: 23 May 2024 / Online: 23 May 2024 (11:16:07 CEST)
How to cite: Chen, Z.; Ristic, B.; Kim, D. Y. A Possibilistic Formulation of Autonomous Search for Targets. Preprints 2024, 2024051538. https://doi.org/10.20944/preprints202405.1538.v1 Chen, Z.; Ristic, B.; Kim, D. Y. A Possibilistic Formulation of Autonomous Search for Targets. Preprints 2024, 2024051538. https://doi.org/10.20944/preprints202405.1538.v1
Abstract
Autonomous search is an ongoing cycle of sensing, statistical estimation and motion control with objective to find and localise targets in a designated search area. Traditionally, the theoretical framework for autonomous search combines the sequential Bayesian estimation with the information theoretic motion control. This paper formulates autonomous search in the framework of possibility theory. Although the possibilistic formulation is slightly more involved than the traditional, it provides means for quantitative modelling and reasoning in the presence of epistemic uncertainty. This feature is demonstrated in the paper in the context of partially known probability of detection, expressed as an interval value. The paper presents an elegant Bayes-like solution to sequential estimation, with the reward function for motion control defined to take into account the epistemic uncertainty. The advantages of the proposed search algorithm are demonstrated by numerical simulations.
Keywords
Possibility theory; autonomous systems; robust estimation
Subject
Computer Science and Mathematics, Signal Processing
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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