Three-Dimensional Path Planning of AUVs in Dynamic Obstacle Environments

• Published in: 水下无人系统学报 Vol. 33; no. 3; pp. 400 - 409
• Main Authors: Chaoyang CHEN, Yute TANG, Yi HUANG, Zhiqun LIU
• Format: Journal Article
• Language: Chinese
• Published: Science Press (China) 01.06.2025
• Subjects: autonomous undersea vehicle; deep reinforcement learning; dynamic obstacle; three-dimensional path planning
• ISSN: 2096-3920
• DOI: 10.11993/j.issn.2096-3920.2025-0008

Traditional algorithms suffer from heavy computational burden and insufficient accuracy in the high-dimensional space and dynamic obstacle environment faced by autonomous underwater vehicles(AUVs). To overcome the challenges posed by dynamic obstacles to AUV path planning in complex three-dimensional environments, this study proposed a three-dimensional path planning method for AUVs based on an enhanced double deep Q-network(DDQN). By optimizing the network architecture and designing an efficient reward function, the AUV path planning efficiency and accuracy were significantly improved. Moreover, dynamic obstacle trajectories were modeled, and the Singer model, combined with the Kalman filter algorithm, was used to precisely predict obstacle states, thereby enhancing the dynamic obstacle avoidance capabilities of AUVs. Additionally, Basis spline functions were utilized to smooth the paths, thereby improving the path continuity and stability of AUVs. Simulation and experimental results demonstrate that the pro