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Communication Dans Un Congrès Année : 2022

Disk-Graph Probabilistic Roadmap: Biased Distance Sampling for Path Planning in a Partially Unknown Environment

Résumé

In this paper, we propose a new sampling-based path planning approach, focusing on the challenges linked to autonomous exploration. Our method relies on the definition of a disk graph of free-space bubbles, from which we derive a biased sampling function that expands the graph towards known free space for maximal navigability and frontiers discovery. The proposed method demonstrates an exploratory behavior similar to Rapidly-exploring Random Trees, while retaining the connectivity and flexibility of a graph-based planner. We demonstrate the interest of our method by first comparing its path planning capabilities against state-of-theart approaches, before discussing exploration-specific aspects, namely replanning capabilities and incremental construction of the graph. A simple frontiers-driven exploration controller derived from our planning method is also demonstrated using the Pioneer platform.
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hal-03752195 , version 1 (16-08-2022)

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  • HAL Id : hal-03752195 , version 1

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Thibault Noël, Sala Kabbour, Antoine Lehuger, Eric Marchand, François Chaumette. Disk-Graph Probabilistic Roadmap: Biased Distance Sampling for Path Planning in a Partially Unknown Environment. IROS 2022 – IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2022, Kyoto, Japan. pp.1-8. ⟨hal-03752195⟩
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