Interleaving Planning and Control of Mobiles Robots in Urban Environments Using Road-Map [chapter]

Guillaume Lozenguez, Lounis Adouane, Aurélie Beynier, Abdel-Illah Mouaddib, Philippe Martinet
<span title="">2013</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/kax3wwzwmncwhi472pxbzqsjja" style="color: black;">Advances in Intelligent Systems and Computing</a> </i> &nbsp;
This paper presents a robot solution that allows to automatically reach a set of goals attributed to a robot. The challenge is to design autonomous robots assigned to perform missions without a predefined plan. We address the stochastic salesman problem where the goal is to visit a set of points of interest. A stochastic Road-Map is defined as a topological representation of an unstructured environment with uncertainty on the path achievement. The Road-Map allows us to split deliberation and
more &raquo; ... ctive control. The proposed decision making uses a computation of Markov Decision Processes (MDPs) in order to plan all the reactive tasks to perform while there are goals not yet reached. Finally, from a brief explanation on how the approach could be extend to multi-robot missions, experiments in real conditions permit to evaluate the proposed architecture for multi-robot stochastic salesmen missions.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-33926-4_65">doi:10.1007/978-3-642-33926-4_65</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gc6exnowsnc2nene7e2j27q3za">fatcat:gc6exnowsnc2nene7e2j27q3za</a> </span>
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