Learning based approach towards AUV and Marine Life Interaction

Autonomous Underwater Vehicles (AUVs) have significantly advanced in their capabilities, enabling exploration and operations in diverse underwater environments. While navigation in sparse and obstacle-free terrains is relatively simple, navigating deeper waters introduces new challenges due to the presence of marine life, such as migrating shoals of fish and predators hunting for food. This paper explores a novel approach with the integration of reinforcement learning for motion planning in underwater robotics. The primary focus is on the implementation of the Proximal Policy Optimization (PPO) algorithm and Gumbel Social Transformer (GST), which enables the robot to learn how to navigate in an underwater environment with dynamic obstacles. The obstacles are modeled as a shoal of fish and a predator, with the robot tasked with avoiding collisions. The underwater system will be simulated using the Robot Operating System (ROS) framework, and onboard sonar sensors will be employed to detect and track any dynamic obstacles in the vicinity.