Adaptive Multi-Objective Optimization-Based Coverage Path Planning Method for UUVs

ZHAO Shaojing; FU Songchen; BAI Letian; GUO Yutong; LI Ta

doi:10.11993/j.issn.2096-3920.2025-0031

Volume 33 Issue 3

Jun 2025

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(3): 459-472

ZHAO Shaojing, FU Songchen, BAI Letian, GUO Yutong, LI Ta. Adaptive Multi-Objective Optimization-Based Coverage Path Planning Method for UUVs[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 459-472. doi: 10.11993/j.issn.2096-3920.2025-0031

Citation:

ZHAO Shaojing, FU Songchen, BAI Letian, GUO Yutong, LI Ta. Adaptive Multi-Objective Optimization-Based Coverage Path Planning Method for UUVs[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 459-472. doi: 10.11993/j.issn.2096-3920.2025-0031

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Adaptive Multi-Objective Optimization-Based Coverage Path Planning Method for UUVs

doi: 10.11993/j.issn.2096-3920.2025-0031

ZHAO Shaojing^{1, 2
,},
FU Songchen^{1, 2
,},
BAI Letian^{1, 2
,},
GUO Yutong^1
,,
LI Ta^{1, 2
,}

1.
Laboratory of Speech and Intelligent Information Processing, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China

Received Date: 2025-02-25
Accepted Date: 2025-03-25
Rev Recd Date: 2025-03-19

Available Online: 2025-06-03

Abstract

Abstract

Coverage path planning for unmanned undersea vehicles(UUVs) in unknown aquatic environments is a critical task. However, due to environmental uncertainties, motion constraints, and energy limitations, traditional path planning methods struggle to adapt to complex scenarios. This paper proposed an adaptive multi-objective optimization-based coverage path planning method for UUVs, integrating proximal policy optimization(PPO) with a dynamic weight adjustment mechanism. By analyzing the correlation between reward objectives and employing linear regression estimation, the proposed approach adaptively adjusted the weights of different optimization objectives, enabling UUVs to autonomously plan efficient coverage paths in environments with unknown obstacles and ocean currents. To validate the effectiveness of the proposed method, a UUV motion and sonar detection model based on a two-dimensional simulation environment was constructed. Among them, the UUV motion model was simplified to a planar motion model on the basis of the six-degree-of-freedom rigid-body motion. Comparative experiments were conducted under various obstacle distributions and random ocean currents. Experimental results demonstrate that compared with traditional methods, the proposed approach improves coverage while optimizing mission completion rate, trajectory length, energy consumption, and information latency. Specifically, it increases coverage by 4.03%, enhances mission completion rate by 10%, improves utility by 10.96%, reduces mission completion time by 14.13%, shortens trajectory length by 26.85%, lowers energy consumption by 10.3%, and decreases information latency by 19.34%. These results indicate that the proposed method significantly enhances the adaptability and robustness of UUVs in complex environments, providing a novel optimization strategy for autonomous underwater exploration tasks.
- unmanned undersea vehicle,
- coverage path planning,
- reinforcement learning,
- multi-objective optimization,
- adaptive weight adjustment

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References(24)

References

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