Efficient sampling-based approaches to optimal path planning in complex cost spaces

Abstract : Sampling-based algorithms for path planning have achieved great success during the last 15 years, thanks to their ability to efficiently solve complex high-dimensional problems. However, standard versions of these algorithms cannot guarantee optimality or even high-quality for the produced paths. In recent years, variants of these methods, taking cost criteria into account during the exploration process, have been proposed to compute high-quality paths (such as T-RRT), some even guaranteeing asymptotic optimality (such as RRT*). In this paper, we propose two new sampling-based approaches that combine the underlying principles of RRT* and T-RRT. These algorithms, called T-RRT* and AT-RRT, offer probabilistic completeness and asymptotic optimality guarantees. Results presented on several classes of problems show that they converge faster than RRT* toward the optimal path, especially when the topology of the search space is complex and/or when its dimensionality is high.
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Communication dans un congrès
International Workshop on the Algorithmic Foundations of Robotics (WAFR), Aug 2014, Istanbul, Turkey. pp.16, 2014
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https://hal.archives-ouvertes.fr/hal-01062970
Contributeur : Didier Devaurs <>
Soumis le : jeudi 11 septembre 2014 - 10:20:15
Dernière modification le : mercredi 11 janvier 2017 - 01:04:22
Document(s) archivé(s) le : vendredi 12 décembre 2014 - 10:20:31

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  • HAL Id : hal-01062970, version 1

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Didier Devaurs, Thierry Siméon, Juan Cortés. Efficient sampling-based approaches to optimal path planning in complex cost spaces. International Workshop on the Algorithmic Foundations of Robotics (WAFR), Aug 2014, Istanbul, Turkey. pp.16, 2014. 〈hal-01062970〉

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