A Multi-Source-Data-Assisted AUV for Path Cruising: An Energy-Efficient DDPG Approach

Abstract

As marine activities expand, deploying underwater autonomous vehicles (AUVs) becomes critical. Efficiently navigating these AUVs through intricate underwater terrains is vital. This paper proposes a sophisticated motion-planning algorithm integrating deep reinforcement learning (DRL) with an improved artificial potential field (IAPF). The algorithm incorporates remote sensing information to overcome traditional APF challenges and combines the IAPF with the traveling salesman problem for optimal path cruising. Through a combination of DRL and multi-source data optimization, the approach ensures minimal energy consumption across all target points. Inertial sensors further refine trajectory, ensuring smooth navigation and precise positioning. The comparative experiments confirm the method’s energy efficiency, trajectory refinement, and safety excellence.

Keywords

• underwater autonomous vehicles (AUVs)
• deep reinforcement learning (DRL)
• improved artificial potential field (IAPF)
• remote sensing information
• multi-source data
• energy consumption