Quick-RRT: Triangular inequality-based implementation of RRT with improved initial solution and convergence rate

•Sampling-based algorithms are commonly used in motion planning problems.•The RRT* algorithm incrementally builds a tree of motion to find a solution.•Taking a shortcut to the ancestry increases the convergence rate to the optimal.•Combination with sampling strategies further improves the performanc...

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Vydáno v:Expert systems with applications Ročník 123; s. 82 - 90
Hlavní autoři: Jeong, In-Bae, Lee, Seung-Jae, Kim, Jong-Hwan
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Elsevier Ltd 01.06.2019
Elsevier BV
Témata:
Algorithms
Apexes
Computer simulation
Convergence
Hyperspheres
Motion planning
Navigation
Optimal path planning
Optimization
Parents
Pruning
Rewiring
RRT
Sampling
Sampling-based algorithms
Motion planning
Navigation
Optimal path planning
RRT
Sampling-based algorithms
ISSN:0957-4174, 1873-6793
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Shrnutí:•Sampling-based algorithms are commonly used in motion planning problems.•The RRT* algorithm incrementally builds a tree of motion to find a solution.•Taking a shortcut to the ancestry increases the convergence rate to the optimal.•Combination with sampling strategies further improves the performance. The Rapidly-exploring Random Tree (RRT) algorithm is a popular algorithm in motion planning problems. The optimal RRT (RRT*) is an extended algorithm of RRT, which provides asymptotic optimality. This paper proposes Quick-RRT* (Q-RRT*), a modified RRT* algorithm that generates a better initial solution and converges to the optimal faster than RRT*. Q-RRT* enlarges the set of possible parent vertices by considering not only a set of vertices contained in a hypersphere, as in RRT*, but also their ancestry up to a user-defined parameter, thus, resulting in paths with less cost than those of RRT*. It also applies a similar technique to the rewiring procedure resulting in acceleration of the tendency that near vertices share common parents. Since the algorithm proposed in this paper is a tree extending algorithm, it can be combined with other sampling strategies and graph-pruning algorithms. The effectiveness of Q-RRT* is demonstrated by comparing the algorithm with existing algorithms through numerical simulations. It is also verified that the performance can be further enhanced when combined with other sampling strategies.
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ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2019.01.032