AUV Path Planning Method Based on RRT* and Improved Artificial Potential Field

LIU Yuqing; CHEN Mojiang; HAO Chengpeng

doi:10.11993/j.issn.2096-3920.2026-0025

Article Contents

Article Navigation > Journal of Unmanned Undersea Systems > 2026 > Accepted Manuscript

LIU Yuqing, CHEN Mojiang, HAO Chengpeng. AUV Path Planning Method Based on RRT* and Improved Artificial Potential Field[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0025

Citation:

LIU Yuqing, CHEN Mojiang, HAO Chengpeng. AUV Path Planning Method Based on RRT* and Improved Artificial Potential Field[J]. Journal of Unmanned Undersea Systems. doi: 10.11993/j.issn.2096-3920.2026-0025

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AUV Path Planning Method Based on RRT* and Improved Artificial Potential Field

doi: 10.11993/j.issn.2096-3920.2026-0025

1.
Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 101408, China

Received Date: 2026-01-21
Accepted Date: 2026-03-02
Rev Recd Date: 2026-02-14

Available Online: 2026-09-04

Abstract

Abstract

Aiming at the problem that the complex ocean current environment significantly affects the navigation efficiency of autonomous underwater vehicles (AUVs), this paper proposes an energy consumption-optimized path planning algorithm (FAPF-Bi-RRT*) that integrates bidirectional rapidly-exploring random tree with flow field-guided sampling and improved artificial potential field method. Firstly, a multi-strategy sampling probability correction method based on flow field information is designed. By calculating the consistency measure between the flow field and the target direction, the sampling weight is dynamically adjusted to guide the random tree to expand toward downstream and low-energy-consumption areas, thus solving the blind search problem of the traditional RRT*. Secondly, an adaptive flow field potential field model is established, which converts the pushing and resistance effects of ocean currents into potential field gradients. An adaptive adjustment mechanism of flow field gain is introduced into the artificial potential field to assist node expansion and reconnection in real time, thereby avoiding countercurrent traps. Finally, a composite cost function including path length and energy consumption is constructed for progressive optimization. Simulation results under real terrain and flow field data show that while ensuring probabilistic completeness and obstacle avoidance capability, the proposed algorithm reduces the path length by approximately 14% and the path energy consumption by approximately 10% compared with the traditional Bi-RRT* algorithm in strong flow field environments. This realizes efficient and energy-saving path planning in complex marine environments.
- path planning,
- autonomous underwater vehicle,
- RRT*,
- artificial potential field method,
- ocean currents

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References(24)

References

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