An Overview and Comparison of Traditional Motion Planning Based on Rapidly Exploring Random Trees

Motion planning is a fundamental problem in robotics that involves determining feasible or optimal paths within finite time. While complete motion planning algorithms are guaranteed to converge to a path solution in finite time, they are proven to be computationally inefficient, making them unsuitab...

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Veröffentlicht in:Sensors (Basel, Switzerland) Jg. 25; H. 7; S. 2067
Hauptverfasser: Chu, Yang, Chen, Quanlin, Yan, Xuefeng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Switzerland MDPI AG 26.03.2025
MDPI
Schlagworte:
Algorithms
Control systems
Controllers
Equilibrium
Heuristic
kinodynamic planning
Motion
motion planning
Neighborhoods
Optimization algorithms
Robotics
Robots
sampling-based algorithms
China
kinodynamic planning
motion planning
sampling-based algorithms
ISSN:1424-8220, 1424-8220
Online-Zugang:Volltext
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Zusammenfassung:Motion planning is a fundamental problem in robotics that involves determining feasible or optimal paths within finite time. While complete motion planning algorithms are guaranteed to converge to a path solution in finite time, they are proven to be computationally inefficient, making them unsuitable for most practical problems. Resolution-complete algorithms, on the other hand, ensure completeness only if the resolution parameter is sufficiently fine, but they suffer severely from the curse of dimensionality. In contrast, sampling-based algorithms, such as Rapidly Exploring Random Trees (RRT) and its variants, have gained the increasing attention of researchers due to their computational efficiency and effectiveness, particularly in high-dimensional problems. This review paper introduces RRT-based algorithms and provides an overview of their key methodological aspects.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s25072067