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Fast Path-Planning Algorithm for Mobile Robots via Straight Strategyopen access

Authors
Jeong, JihongJung, Jin-Woo
Issue Date
Feb-2026
Publisher
MDPI
Keywords
path planning; sampling based algorithms; rapidly explored random tree (RRT); triangular inequality; linear interpolation
Citation
Applied Sciences, v.16, no.4, pp 1 - 32
Pages
32
Indexed
SCIE
SCOPUS
Journal Title
Applied Sciences
Volume
16
Number
4
Start Page
1
End Page
32
URI
https://scholarworks.dongguk.edu/handle/sw.dongguk/63915
DOI
10.3390/app16041952
ISSN
2076-3417
2076-3417
Abstract
RRT*, which augments the RRT with the ChooseParent and Rewire steps, is a widely used algorithm for path planning. RRT*-based algorithms are effective for improving the quality of paths in the Rewire step. However, as the expansion continues, there are more nodes to be inspected, which can slow down the path search. In addition, due to the parameters set in the rewire step, a trade-off issue between path quality and planning time arises. In this paper, we propose Straight-RRT to improve upon these limitations. Straight-RRT applies the Straight strategy to rapidly obtain an initial path and then returns a refined path using the MoveParent strategies. Accordingly, Straight-RRT adopts the following mechanisms. (1) The Straight strategy is applied for rapidly finding an initial path. This procedure quickly finds a feasible initial path. (2) MoveParent is a strategy inspired by parametric equations for path optimization. This complements the weaknesses of the Straight strategy and the limitations of the triangle inequality. The MoveParent strategy is applied bidirectionally. These procedures progressively refine the path and improve efficiency. We propose an algorithm that is faster than other algorithms using these strategies and minimizes the trade-off caused by parameter settings. In the experimental comparison results across most environments, our approach achieved shorter planning times than the compared baseline algorithms and produced paths of comparable quality.
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Jung, Jin Woo
College of Advanced Convergence Engineering (Department of Computer Science and Artificial Intelligence)
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