Three-Dimensional Path Planning of AUVs in Dynamic Obstacle Environments

CHEN Chaoyang; TANG Yute; HUANG Yi; LIU Zhiqun

doi:10.11993/j.issn.2096-3920.2025-0008

Volume 33 Issue 3

Jun 2025

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Article Navigation > Journal of Unmanned Undersea Systems > 2025 > 33(3): 400-409

CHEN Chaoyang, TANG Yute, HUANG Yi, LIU Zhiqun. Three-Dimensional Path Planning of AUVs in Dynamic Obstacle Environments[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 400-409. doi: 10.11993/j.issn.2096-3920.2025-0008

Citation:

CHEN Chaoyang, TANG Yute, HUANG Yi, LIU Zhiqun. Three-Dimensional Path Planning of AUVs in Dynamic Obstacle Environments[J]. Journal of Unmanned Undersea Systems, 2025, 33(3): 400-409. doi: 10.11993/j.issn.2096-3920.2025-0008

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Three-Dimensional Path Planning of AUVs in Dynamic Obstacle Environments

doi: 10.11993/j.issn.2096-3920.2025-0008

CHEN Chaoyang^{1, 2, 3
,},
TANG Yute^{1, 2
,},
HUANG Yi^{1, 2
,},
LIU Zhiqun^2
,

1.
Sanya Institute of Hunan University of Science and Technology, Sanya 572024, China
2.
School of Information and Electrical Engineering, Hunan University of Science and Technology, Xiangtan 411100, China
3.
Hunan Key Laboratory of Intelligent Control and aintenance for Complex Systems, Xiangtan 411100, China )

Received Date: 2025-01-13
Accepted Date: 2025-03-24
Rev Recd Date: 2025-03-13

Available Online: 2025-06-03

Abstract

Abstract

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 proposed approach effectively avoids collisions in complex dynamic environments and achieves real-time planning of safe and efficient paths. Compared to traditional methods, the DDQN algorithm shows significant advantages in terms of path length, obstacle avoidance success rate, and computational efficiency, effectively addressing the challenges associated with three-dimensional path planning of AUVs in dynamic obstacle environments.
- autonomous undersea vehicle,
- deep reinforcement learning,
- three-dimensional path planning,
- dynamic obstacle

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References(23)

References

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