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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Efficient Sampling-Based Approaches to Optimal Path Planning in Complex Cost Spaces
verfasst von : Didier Devaurs, Thierry Siméon, Juan Cortés
Erschienen in: Algorithmic Foundations of Robotics XI
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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.