Towards End to End Motion Planning and Execution for Autonomous Underwater Vehicles Using Reinforcement Learning
Researchers have developed an end-to-end deep reinforcement learning system for autonomous underwater vehicles (AUVs) that maps raw sensor data directly to thruster commands. This hierarchical approach splits the task into high-level goal generation and low-level command execution, trained using methods like RLPD and SAC with HER. Evaluated in simulation, the system demonstrated effective obstacle avoidance and robustness to sensor noise, though it showed limitations in generalizing to novel obstacle shapes. AI
IMPACT Demonstrates a promising path for simplifying AUV control systems and improving navigation capabilities in complex underwater environments.