Synergistic Hierarchical AI Framework for USV Navigation: Closing the Loop Between Swin-Transformer Perception, T-ASTAR Planning, and Energy-Aware TD3 Control
Autonomous Unmanned Surface Vehicle (USV) operations in complex ocean engineering scenarios necessitate robust navigation, guidance, and control technologies. These systems require reliable sensor-based object detection and efficient, safe, and energy-aware path planning. To address these multifacet...
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2025-07-01
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| author | Haonan Ye Hongjun Tian Qingyun Wu Yihong Xue Jiayu Xiao Guijie Liu Yang Xiong |
| author_facet | Haonan Ye Hongjun Tian Qingyun Wu Yihong Xue Jiayu Xiao Guijie Liu Yang Xiong |
| author_sort | Haonan Ye |
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| description | Autonomous Unmanned Surface Vehicle (USV) operations in complex ocean engineering scenarios necessitate robust navigation, guidance, and control technologies. These systems require reliable sensor-based object detection and efficient, safe, and energy-aware path planning. To address these multifaceted challenges, this paper proposes a novel synergistic AI framework. The framework integrates (1) a novel adaptation of the Swin-Transformer to generate a dense, semantic risk map from raw visual data, enabling the system to interpret ambiguous marine conditions like sun glare and choppy water, enabling real-time environmental understanding crucial for guidance; (2) a Transformer-enhanced A-star (T-ASTAR) algorithm with spatio-temporal attentional guidance to generate globally near-optimal and energy-aware static paths; (3) a domain-adapted TD3 agent featuring a novel energy-aware reward function that optimizes for USV hydrodynamic constraints, making it suitable for long-endurance missions tailored for USVs to perform dynamic local path optimization and real-time obstacle avoidance, forming a key control element; and (4) CUDA acceleration to meet the computational demands of real-time ocean engineering applications. Simulations and real-world data verify the framework’s superiority over benchmarks like A* and RRT, achieving 30% shorter routes, 70% fewer turns, 64.7% fewer dynamic collisions, and a 215-fold speed improvement in map generation via CUDA acceleration. This research underscores the importance of integrating powerful AI components within a hierarchical synergy, encompassing AI-based perception, hierarchical decision planning for guidance, and multi-stage optimal search algorithms for control. The proposed solution significantly advances USV autonomy, addressing critical ocean engineering challenges such as navigation in dynamic environments, object avoidance, and energy-constrained operations for unmanned maritime systems. |
| format | Article |
| id | doaj-art-df19ba4313ba49a2a036bae3e373c210 |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-df19ba4313ba49a2a036bae3e373c2102025-08-20T03:36:23ZengMDPI AGSensors1424-82202025-07-012515469910.3390/s25154699Synergistic Hierarchical AI Framework for USV Navigation: Closing the Loop Between Swin-Transformer Perception, T-ASTAR Planning, and Energy-Aware TD3 ControlHaonan Ye0Hongjun Tian1Qingyun Wu2Yihong Xue3Jiayu Xiao4Guijie Liu5Yang Xiong6College of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaDepartment of Mechanical and Electrical Engineering, Ocean University of China, Qingdao 266404, ChinaCollege of Engineering Science and Technology, Shanghai Ocean University, Shanghai 201306, ChinaAutonomous Unmanned Surface Vehicle (USV) operations in complex ocean engineering scenarios necessitate robust navigation, guidance, and control technologies. These systems require reliable sensor-based object detection and efficient, safe, and energy-aware path planning. To address these multifaceted challenges, this paper proposes a novel synergistic AI framework. The framework integrates (1) a novel adaptation of the Swin-Transformer to generate a dense, semantic risk map from raw visual data, enabling the system to interpret ambiguous marine conditions like sun glare and choppy water, enabling real-time environmental understanding crucial for guidance; (2) a Transformer-enhanced A-star (T-ASTAR) algorithm with spatio-temporal attentional guidance to generate globally near-optimal and energy-aware static paths; (3) a domain-adapted TD3 agent featuring a novel energy-aware reward function that optimizes for USV hydrodynamic constraints, making it suitable for long-endurance missions tailored for USVs to perform dynamic local path optimization and real-time obstacle avoidance, forming a key control element; and (4) CUDA acceleration to meet the computational demands of real-time ocean engineering applications. Simulations and real-world data verify the framework’s superiority over benchmarks like A* and RRT, achieving 30% shorter routes, 70% fewer turns, 64.7% fewer dynamic collisions, and a 215-fold speed improvement in map generation via CUDA acceleration. This research underscores the importance of integrating powerful AI components within a hierarchical synergy, encompassing AI-based perception, hierarchical decision planning for guidance, and multi-stage optimal search algorithms for control. The proposed solution significantly advances USV autonomy, addressing critical ocean engineering challenges such as navigation in dynamic environments, object avoidance, and energy-constrained operations for unmanned maritime systems.https://www.mdpi.com/1424-8220/25/15/4699unmanned surface vehicle (USV)underwater navigationguidance and controlocean engineeringswin-transformerT-ASTAR algorithm |
| spellingShingle | Haonan Ye Hongjun Tian Qingyun Wu Yihong Xue Jiayu Xiao Guijie Liu Yang Xiong Synergistic Hierarchical AI Framework for USV Navigation: Closing the Loop Between Swin-Transformer Perception, T-ASTAR Planning, and Energy-Aware TD3 Control Sensors unmanned surface vehicle (USV) underwater navigation guidance and control ocean engineering swin-transformer T-ASTAR algorithm |
| title | Synergistic Hierarchical AI Framework for USV Navigation: Closing the Loop Between Swin-Transformer Perception, T-ASTAR Planning, and Energy-Aware TD3 Control |
| title_full | Synergistic Hierarchical AI Framework for USV Navigation: Closing the Loop Between Swin-Transformer Perception, T-ASTAR Planning, and Energy-Aware TD3 Control |
| title_fullStr | Synergistic Hierarchical AI Framework for USV Navigation: Closing the Loop Between Swin-Transformer Perception, T-ASTAR Planning, and Energy-Aware TD3 Control |
| title_full_unstemmed | Synergistic Hierarchical AI Framework for USV Navigation: Closing the Loop Between Swin-Transformer Perception, T-ASTAR Planning, and Energy-Aware TD3 Control |
| title_short | Synergistic Hierarchical AI Framework for USV Navigation: Closing the Loop Between Swin-Transformer Perception, T-ASTAR Planning, and Energy-Aware TD3 Control |
| title_sort | synergistic hierarchical ai framework for usv navigation closing the loop between swin transformer perception t astar planning and energy aware td3 control |
| topic | unmanned surface vehicle (USV) underwater navigation guidance and control ocean engineering swin-transformer T-ASTAR algorithm |
| url | https://www.mdpi.com/1424-8220/25/15/4699 |
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