Optimal Path Planning in Complex Cost Spaces With Sampling-Based Algorithms

Sampling-based algorithms for path planning, such as the Rapidly-exploring Random Tree (RRT), have achieved great success, thanks to their ability to efficiently solve complex high-dimensional problems. However, standard versions of these algorithms cannot guarantee optimality or even high-quality f...

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Veröffentlicht in:IEEE transactions on automation science and engineering Jg. 13; H. 2; S. 415 - 424
Hauptverfasser: Devaurs, Didier, Simeon, Thierry, Cortes, Juan
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York IEEE 01.04.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Institute of Electrical and Electronics Engineers
Schlagworte:
Anytime path planning
Automatic
Complex systems
Computer Science
Cost function
cost space path planning
Engineering Sciences
optimal path planning
Optimization algorithms
Path planning
Probabilistic logic
Problem solving
Robotics
Robots
Robustness
Sampling
sampling-based path planning
Search problems
Space exploration
Stochastic models
Topology
ISSN:1545-5955, 1558-3783
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Zusammenfassung:Sampling-based algorithms for path planning, such as the Rapidly-exploring Random Tree (RRT), have achieved great success, 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, such as T-RRT, have been proposed to deal with cost spaces: by taking configuration-cost functions into account during the exploration process, they can produce high-quality (i.e., low-cost) paths. Other novel variants, such as RRT*, can deal with optimal path planning: they ensure convergence toward the optimal path, with respect to a given path-quality criterion. In this paper, we propose to solve a complex problem encompassing this two paradigms: optimal path planning in a cost space. For that, we develop two efficient sampling-based approaches that combine the underlying principles of RRT* and T-RRT. These algorithms, called T-RRT* and AT-RRT, offer the same asymptotic optimality guarantees as RRT*. 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.
Bibliographie:SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 14
ISSN:1545-5955
1558-3783
DOI:10.1109/TASE.2015.2487881