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

• Published in: 水下无人系统学报 Vol. 33; no. 3; pp. 459 - 472
• Main Authors: Shaojing ZHAO, Songchen FU, Letian BAI, Yutong GUO, Ta LI
• Format: Journal Article
• Language: Chinese
• Published: Science Press (China) 01.06.2025
• Subjects: adaptive weight adjustment; coverage path planning; multi-objective optimization; reinforcement learning; unmanned undersea vehicle
• ISSN: 2096-3920
• DOI: 10.11993/j.issn.2096-3920.2025-0031

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 ba