Improved RRT Path-Planning Algorithm Based on the Clothoid Curve for a Mobile Robot Under Kinematic Constraints

In this paper, we propose an algorithm based on the Rapidly-exploring Random Trees* (RRT*) algorithm for the path planning of mobile robots under kinematic constraints, aiming to generate efficient and smooth paths quickly. Compared to other algorithms, the main contributions of our proposed algorit...

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Published inSensors (Basel, Switzerland) Vol. 24; no. 23; p. 7812
Main Authors Ran, Kemeng, Wang, Yujun, Fang, Can, Chai, Qisen, Dong, Xingxiang, Liu, Guohui
Format Journal Article
LanguageEnglish
Published Switzerland MDPI AG 06.12.2024
MDPI
Subjects
Algorithms
Clothoid curve
Comparative analysis
Deep learning
Efficiency
Genetic algorithms
Kinematics
obstacle avoidance
Optimization algorithms
path planning
Planning
Robotics
Robots
RRT
obstacle avoidance
path planning
Clothoid curve
RRT
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Abstract In this paper, we propose an algorithm based on the Rapidly-exploring Random Trees* (RRT*) algorithm for the path planning of mobile robots under kinematic constraints, aiming to generate efficient and smooth paths quickly. Compared to other algorithms, the main contributions of our proposed algorithm are as follows: First, we introduce a bidirectional expansion strategy that quickly identifies a direct path to the goal point in a short time. Second, a node reconnection strategy is used to eliminate unnecessary nodes, thereby reducing the path length and saving memory. Third, a path deformation strategy based on the Clothoid curve is devised to enhance obstacle avoidance and path-planning capability, ensuring collision-free paths that comply with the kinematic constraints of mobile robots. Simulation results demonstrate that our algorithm is simpler, more computationally efficient, expedites pathfinding, achieves higher success rates, and produces smoother paths compared to existing algorithms.
AbstractList In this paper, we propose an algorithm based on the Rapidly-exploring Random Trees* (RRT*) algorithm for the path planning of mobile robots under kinematic constraints, aiming to generate efficient and smooth paths quickly. Compared to other algorithms, the main contributions of our proposed algorithm are as follows: First, we introduce a bidirectional expansion strategy that quickly identifies a direct path to the goal point in a short time. Second, a node reconnection strategy is used to eliminate unnecessary nodes, thereby reducing the path length and saving memory. Third, a path deformation strategy based on the Clothoid curve is devised to enhance obstacle avoidance and path-planning capability, ensuring collision-free paths that comply with the kinematic constraints of mobile robots. Simulation results demonstrate that our algorithm is simpler, more computationally efficient, expedites pathfinding, achieves higher success rates, and produces smoother paths compared to existing algorithms.
In this paper, we propose an algorithm based on the Rapidly-exploring Random Trees* (RRT*) algorithm for the path planning of mobile robots under kinematic constraints, aiming to generate efficient and smooth paths quickly. Compared to other algorithms, the main contributions of our proposed algorithm are as follows: First, we introduce a bidirectional expansion strategy that quickly identifies a direct path to the goal point in a short time. Second, a node reconnection strategy is used to eliminate unnecessary nodes, thereby reducing the path length and saving memory. Third, a path deformation strategy based on the Clothoid curve is devised to enhance obstacle avoidance and path-planning capability, ensuring collision-free paths that comply with the kinematic constraints of mobile robots. Simulation results demonstrate that our algorithm is simpler, more computationally efficient, expedites pathfinding, achieves higher success rates, and produces smoother paths compared to existing algorithms.In this paper, we propose an algorithm based on the Rapidly-exploring Random Trees* (RRT*) algorithm for the path planning of mobile robots under kinematic constraints, aiming to generate efficient and smooth paths quickly. Compared to other algorithms, the main contributions of our proposed algorithm are as follows: First, we introduce a bidirectional expansion strategy that quickly identifies a direct path to the goal point in a short time. Second, a node reconnection strategy is used to eliminate unnecessary nodes, thereby reducing the path length and saving memory. Third, a path deformation strategy based on the Clothoid curve is devised to enhance obstacle avoidance and path-planning capability, ensuring collision-free paths that comply with the kinematic constraints of mobile robots. Simulation results demonstrate that our algorithm is simpler, more computationally efficient, expedites pathfinding, achieves higher success rates, and produces smoother paths compared to existing algorithms.
Audience Academic
Author Dong, Xingxiang
Ran, Kemeng
Liu, Guohui
Fang, Can
Chai, Qisen
Wang, Yujun
AuthorAffiliation College of Computer and Information Science, Southwest University, Chongqing 400715, China; rkm666@email.swu.edu.cn (K.R.); wangyjun@swu.edu.cn (Y.W.); cqsllyt@email.swu.edu.cn (Q.C.); d13538@email.swu.edu.cn (X.D.); liu914904@email.swu.edu.cn (G.L.)
AuthorAffiliation_xml – name: College of Computer and Information Science, Southwest University, Chongqing 400715, China; rkm666@email.swu.edu.cn (K.R.); wangyjun@swu.edu.cn (Y.W.); cqsllyt@email.swu.edu.cn (Q.C.); d13538@email.swu.edu.cn (X.D.); liu914904@email.swu.edu.cn (G.L.)
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Keywords obstacle avoidance
path planning
Clothoid curve
RRT
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Snippet In this paper, we propose an algorithm based on the Rapidly-exploring Random Trees* (RRT*) algorithm for the path planning of mobile robots under kinematic...
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SubjectTerms Algorithms
Clothoid curve
Comparative analysis
Deep learning
Efficiency
Genetic algorithms
Kinematics
obstacle avoidance
Optimization algorithms
path planning
Planning
Robotics
Robots
RRT
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Title Improved RRT Path-Planning Algorithm Based on the Clothoid Curve for a Mobile Robot Under Kinematic Constraints
URI https://www.ncbi.nlm.nih.gov/pubmed/39686348
https://www.proquest.com/docview/3144172475
https://www.proquest.com/docview/3146918730
https://pubmed.ncbi.nlm.nih.gov/PMC11645000
https://doaj.org/article/fb88bdfadac94a3c809e6d94a6fd8e93
Volume 24
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