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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Vydáno v:	Sensors (Basel, Switzerland) Ročník 25; číslo 7; s. 2067
Hlavní autoři:	Chu, Yang, Chen, Quanlin, Yan, Xuefeng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Switzerland MDPI AG 26.03.2025 MDPI
Témata:	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
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Abstract	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.
AbstractList	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. 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.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.
Audience	Academic
Author	Yan, Xuefeng Chu, Yang Chen, Quanlin
AuthorAffiliation	2 State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China; quanlinchen@smail.nju.edu.cn 1 School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China; chuyang_716@nuaa.edu.cn
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Cites_doi	10.15607/RSS.2019.XV.031 10.1016/j.ejor.2022.06.019 10.1109/CDC51059.2022.9993278 10.1109/ICCV.2015.312 10.1016/j.artint.2003.12.001 10.1016/0196-8858(83)90014-3 10.4018/978-1-4666-3634-7.ch002 10.1177/02783640122067453 10.1109/TII.2015.2416435 10.1109/SFCS.1985.65 10.1109/TII.2012.2198665 10.1007/978-0-8176-4606-6 10.1145/359156.359164 10.1109/IROS.2014.6942976 10.1177/02783649922067753 10.1109/ICARCV.2012.6485184 10.1145/3365265.3365282 10.23943/9781400890088 10.1109/ICRA.2013.6631299 10.1109/21.148404 10.1109/TSMC.1985.6313352 10.1109/ETFA.2015.7301635 10.1007/978-3-319-32552-1 10.1007/s12008-019-00574-7 10.1177/0278364911406761 10.1109/ICRA.2015.7139511 10.1109/TRO.2004.838026 10.1109/TSSC.1968.300136 10.1109/WISP.2009.5286557 10.1016/j.robot.2015.02.007 10.1109/TITS.2015.2498841 10.1109/SFCS.1988.21947 10.1109/ICRA.2012.6225177
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SubjectTerms	Algorithms Control systems Controllers Equilibrium Heuristic kinodynamic planning Motion motion planning Neighborhoods Optimization algorithms Robotics Robots sampling-based algorithms
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