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Path planning algorithm for AUVs operating in unstructured underwater environments
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AI
DOI：10.1177/01423312251376778.png)
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Abstract
En 中文
Effective path planning is crucial for autonomous underwater vehicles (AUVs) in rescue and logistics operations. This paper proposes a Risk Aware Proximal Safe Deep Q-Network (RAPS-DQN) to address dynamic path planning challenges across complex underwater terrain. Traditional deep reinforcement learning (DRL) methods show limited generalization in high-dimensional environments, making the adaptive RAPS-DQN approach essential. The proposed method enhances the original DQN by incorporating Lyapunov stability criteria and selectively prioritizing significant transitions based on temporal-difference errors. The suggested approach develops paths that satisfy requirements both efficiently and steadily, as shown by real-time testing and comparison with traditional DRL techniques. The findings show RAPS-DQN facilitates effective learning in complex AUV navigation scenarios, with potential applications in underwater search and rescue missions in flooded urban environments and disaster-affected areas.
Keywords:
AUV
path planning
static and dynamic obstacle
DRL
deep Q learning
dueling neural network
ROS
Journal
IF:
1.9
Papers:
307
Citations:
4.2K