The Improved Informed‐RRT Algorithm, Which Optimizes the Sampling Strategy and Integrates an Artificial Potential Field

ABSTRACT This article presents an algorithm for mobile robots that enables autonomous navigation in complex environments. Currently, achieving autonomous navigation for ground mobile robots in intricate and unstructured settings continues to pose significant challenges. To address issues such as dis...

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Vydáno v:Journal of field robotics Ročník 42; číslo 8; s. 4033 - 4052
Hlavní autoři: Kai‐shen, Kang, Hai‐long, Huang, Zi‐qi, S. U., Hai‐ze, Wang
Médium: Journal Article
Jazyk:angličtina
Vydáno: Hoboken Wiley Subscription Services, Inc 01.12.2025
Témata:
Algorithms
Artificial potential field method
Autonomous navigation
Cubic spline interpolation
Global path planning algorithm
Informed‐RRT algorithm
Optimized Sampling Strategy
Path planning
Potential fields
Rapidly exploring random tree algorithm
Robots
RRT algorithm
Sampling
Smoothness
Strategy
Waypoints
ISSN:1556-4959, 1556-4967
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Abstract ABSTRACT This article presents an algorithm for mobile robots that enables autonomous navigation in complex environments. Currently, achieving autonomous navigation for ground mobile robots in intricate and unstructured settings continues to pose significant challenges. To address issues such as dispersed sampling points, low sampling efficiency, and excessive path waypoints encountered in traditional Rapidly‐Exploring Random Trees (RRT) algorithms, this paper proposes an Optimized Sampling Strategy and Artificial Potential Fields Fusion‐based Informed‐RRT* global path planning algorithm. Initially, sampling angles are determined based on the position of the target point, and the workspace is partitioned into regions with varying levels of importance. Subsequently, an improved artificial potential fields algorithm is integrated to further refine the resultant forces acting on the nodes. Finally, cubic spline interpolation is utilized to smooth the generated path. The proposed algorithm was validated through simulation and experimental studies conducted on simple, narrow, and complex maps. The results demonstrated significant reductions in search time, path length, and the number of path waypoints compared to conventional A*, Dijkstra, RRT, RRT*, and Informed‐RRT algorithms. Additionally, the smoothness of the generated paths was notably improved. In the virtual maze experiments and real‐world environment tests, the improved algorithm presented in this paper demonstrates significant advantages over five other algorithms.
AbstractList This article presents an algorithm for mobile robots that enables autonomous navigation in complex environments. Currently, achieving autonomous navigation for ground mobile robots in intricate and unstructured settings continues to pose significant challenges. To address issues such as dispersed sampling points, low sampling efficiency, and excessive path waypoints encountered in traditional Rapidly‐Exploring Random Trees (RRT) algorithms, this paper proposes an Optimized Sampling Strategy and Artificial Potential Fields Fusion‐based Informed‐RRT* global path planning algorithm. Initially, sampling angles are determined based on the position of the target point, and the workspace is partitioned into regions with varying levels of importance. Subsequently, an improved artificial potential fields algorithm is integrated to further refine the resultant forces acting on the nodes. Finally, cubic spline interpolation is utilized to smooth the generated path. The proposed algorithm was validated through simulation and experimental studies conducted on simple, narrow, and complex maps. The results demonstrated significant reductions in search time, path length, and the number of path waypoints compared to conventional A*, Dijkstra, RRT, RRT*, and Informed‐RRT algorithms. Additionally, the smoothness of the generated paths was notably improved. In the virtual maze experiments and real‐world environment tests, the improved algorithm presented in this paper demonstrates significant advantages over five other algorithms.
ABSTRACT This article presents an algorithm for mobile robots that enables autonomous navigation in complex environments. Currently, achieving autonomous navigation for ground mobile robots in intricate and unstructured settings continues to pose significant challenges. To address issues such as dispersed sampling points, low sampling efficiency, and excessive path waypoints encountered in traditional Rapidly‐Exploring Random Trees (RRT) algorithms, this paper proposes an Optimized Sampling Strategy and Artificial Potential Fields Fusion‐based Informed‐RRT* global path planning algorithm. Initially, sampling angles are determined based on the position of the target point, and the workspace is partitioned into regions with varying levels of importance. Subsequently, an improved artificial potential fields algorithm is integrated to further refine the resultant forces acting on the nodes. Finally, cubic spline interpolation is utilized to smooth the generated path. The proposed algorithm was validated through simulation and experimental studies conducted on simple, narrow, and complex maps. The results demonstrated significant reductions in search time, path length, and the number of path waypoints compared to conventional A*, Dijkstra, RRT, RRT*, and Informed‐RRT algorithms. Additionally, the smoothness of the generated paths was notably improved. In the virtual maze experiments and real‐world environment tests, the improved algorithm presented in this paper demonstrates significant advantages over five other algorithms.
Author Zi‐qi, S. U.
Hai‐ze, Wang
Kai‐shen, Kang
Hai‐long, Huang
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  organization: Liaoning University of Technology
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Snippet ABSTRACT This article presents an algorithm for mobile robots that enables autonomous navigation in complex environments. Currently, achieving autonomous...
This article presents an algorithm for mobile robots that enables autonomous navigation in complex environments. Currently, achieving autonomous navigation for...
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SubjectTerms Algorithms
Artificial potential field method
Autonomous navigation
Cubic spline interpolation
Global path planning algorithm
Informed‐RRT algorithm
Optimized Sampling Strategy
Path planning
Potential fields
Rapidly exploring random tree algorithm
Robots
RRT algorithm
Sampling
Smoothness
Strategy
Waypoints
Title The Improved Informed‐RRT Algorithm, Which Optimizes the Sampling Strategy and Integrates an Artificial Potential Field
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Frob.70000
https://www.proquest.com/docview/3272331216
Volume 42
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