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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Acknowledgments
This work has been partially supported by the European Community under Contract ICT 287617 “ARCAS”. The authors would like to thank Sertac Karaman for helpful discussions on the RRT* algorithm.
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Devaurs, D., Siméon, T., Cortés, J. (2015). Efficient Sampling-Based Approaches to Optimal Path Planning in Complex Cost Spaces. In: Akin, H., Amato, N., Isler, V., van der Stappen, A. (eds) Algorithmic Foundations of Robotics XI. Springer Tracts in Advanced Robotics, vol 107. Springer, Cham. https://doi.org/10.1007/978-3-319-16595-0_9
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DOI: https://doi.org/10.1007/978-3-319-16595-0_9
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