Optimizing Underwater Robot Navigation: A Study of DRL Algorithms and Multi-Modal Sensor Fusion

Autonomous underwater navigation faces significant challenges due to the complexity of the environment, limited localization methods, and poor visibility. This paper investigates the performance of various reinforcement learning (RL) algorithms-Proximal Policy Optimization (PPO), Trust Region Policy Optimization (TRPO), Soft Actor-Critic (SAC), Twin Delayed DDPG (TD3), and Advantage Actor-Critic (A2C)-to improve navigation capabilities of low-cost underwater robots equipped with multi-modal sensors. Advanced depth estimation models such as MiDaS and Depth Anything, combined with domain randomization techniques, are employed to enhance the system's robustness and generalization across varying underwater conditions. The proposed approach integrates real-time sensor data and historical actions to enable 3D maneuvering in simulated environments, leading to significant improvements in sensor fusion, depth perception, and obstacle avoidance. Simulation results demonstrate that the combination of RL techniques with sensor fusion considerably improves mapless autonomous underwater exploration, providing a robust solution for navigating unstructured aquatic environments. The complete implementation is available in an open-source repository, https://github.com/eather0056/BlueROV_Nav_DRL.