Underwater 3D Path Planning for AUV in Ocean Currents Based on Improved Informed RRT* Algorithm

Aiming at the problems of blind search, low environmental adaptability, and long path that exist in the Informed RRT* algorithm for AUV underwater 3D path planning, this paper introduces an improved version of the Informed RRT* algorithm. This enhanced algorithm introduces an adaptive step size to enhance the applicability and reduce the iteration count. Additionally, a bidirectional extension strategy is employed in the initial phase to minimize the time required for searching the initial path, and an angle constraint is added to improve goal-point orientation and search efficiency. A goal-directed sampling strategy is incorporated to further increase the efficiency of tree growth toward the goal point. Furthermore, a pruning strategy is used to optimize the found path by shortening the path length and reducing the number of path nodes, thus enhancing the smoothness of the path found by the algorithm. In this study, the underwater 3D environment modeling considers both the seabed topography and ocean currents, with ocean current information extracted and interpolated. The path length and the influence of ocean currents are taken as indicators to evaluate the path in the objective function. Simulation results demonstrate that the improved Informed RRT* algorithm provides a more effective solution for AUV underwater 3D path planning.

Authors

Institutions

Publication Details

Journal
Journal of Intelligent & Robotic Systems
Published
2025-10-28
DOI
https://doi.org/10.1007/s10846-025-02320-6
Citations
2
Primary Topic
Robotic Path Planning Algorithms
Type
article
Field-Weighted Citation Impact
2.55

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Underwater 3D Path Planning for AUV in Ocean Currents Based on Improved Informed RRT* Algorithm

Enjiao Zhao, Shixiong Wang, Tingting Cheng, Xuehang Lin
2 citations
Journal of Intelligent & Robotic Systems
Robotic Path Planning Algorithms
2.55
article

Underwater 3D Path Planning for AUV in Ocean Currents Based on Improved Informed RRT* Algorithm

Enjiao Zhao, Shixiong Wang, Tingting Cheng, Xuehang Lin
article en
2 citations

Abstract

Aiming at the problems of blind search, low environmental adaptability, and long path that exist in the Informed RRT* algorithm for AUV underwater 3D path planning, this paper introduces an improved version of the Informed RRT* algorithm. This enhanced algorithm introduces an adaptive step size to enhance the applicability and reduce the iteration count. Additionally, a bidirectional extension strategy is employed in the initial phase to minimize the time required for searching the initial path, and an angle constraint is added to improve goal-point orientation and search efficiency. A goal-directed sampling strategy is incorporated to further increase the efficiency of tree growth toward the goal point. Furthermore, a pruning strategy is used to optimize the found path by shortening the path length and reducing the number of path nodes, thus enhancing the smoothness of the path found by the algorithm. In this study, the underwater 3D environment modeling considers both the seabed topography and ocean currents, with ocean current information extracted and interpolated. The path length and the influence of ocean currents are taken as indicators to evaluate the path in the objective function. Simulation results demonstrate that the improved Informed RRT* algorithm provides a more effective solution for AUV underwater 3D path planning.

Journal of Intelligent & Robotic SystemsVol. 111(4)
Harbin Engineering University (CN)
National Natural Science Foundation of China, China Postdoctoral Science Foundation, Natural Science Foundation of Heilongjiang Province
Openalex Percentile: Top 9%
Robotic Path Planning Algorithms
2.55
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.