FC-RRT: A modified RRT with rapid convergence in complex environments

The Rapidly-exploring Random Tree algorithm (RRT) is currently the preferred algorithm for solving motion planning problems. It enables fast path generation on a large scale with high-latitude spatial species. RRT* as the optimal variant provides an asymptotically optimal solution and inspires the F...

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Bibliographic Details
Published in:Journal of computational science Vol. 77; p. 102239
Main Authors: Wang, Jing, Li, Junyang, Song, Yankui, Tuo, Yaoyao, Liu, Chengguo
Format: Journal Article
Language:English
Published: Elsevier B.V 01.04.2024
Subjects:
Motion planning problem
Optimal path
Rapidly-exploring Random Tree (RRT)
Sampling-based algorithm
Rapidly-exploring Random Tree (RRT)
Motion planning problem
Optimal path
Sampling-based algorithm
ISSN:1877-7503, 1877-7511
Online Access:Get full text
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Summary:The Rapidly-exploring Random Tree algorithm (RRT) is currently the preferred algorithm for solving motion planning problems. It enables fast path generation on a large scale with high-latitude spatial species. RRT* as the optimal variant provides an asymptotically optimal solution and inspires the F-RRT* algorithm, which significantly reduces the path cost but performs poorly in complex environments. A modified RRT* algorithm is proposed in this article, FC-RRT*, utilizing the prior knowledge of the mission to expand the path tree at the start point and goal point bidirectionally. Besides, based on F-RRT*, an obstacle proximity node is created to reduce the path cost while modifying its Rewire procedure by including this node as a potential parent node. In this paper, a numerical simulation is performed to compare ARA*, RRT*, F-RRT*, and FC-RRT*, and the advantages of the FC-RRT* algorithm in complex environments is demonstrated. •Sampling-based algorithms are generally applied to motion planning problems.•The two-tree expansion algorithm trades path cost for very few redundant nodes.•Creating a nearby obstacle node to reduce the path cost.•Searching for ancestors of the nearest node reduces path roughness and cost.•Connecting the reachable nearby obstacle node to enhance the optimization rate.
ISSN:1877-7503
1877-7511
DOI:10.1016/j.jocs.2024.102239