Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles

Control theory provides engineers with a multitude of tools to design controllers that manipulate the closed-loop behavior and stability of dynamical systems. These methods rely heavily on insights about the mathematical model governing the physical system. However, in complex systems, such as autonomous underwater vehicles performing the dual objective of path-following and collision avoidance, decision making becomes non-trivial. We propose a solution using state-of-the-art Deep Reinforcement Learning (DRL) techniques, to develop autonomous agents capable of achieving this hybrid objective without having \`a priori knowledge about the goal or the environment. Our results demonstrate the viability of DRL in path-following and avoiding collisions toward achieving human-level decision making in autonomous vehicle systems within extreme obstacle configurations

A 3D Reactive Collision Avoidance Algorithm for Nonholonomic Vehicles

This paper presents a 3D reactive collision avoidance algorithm for vehicles with nonholonomic constraints. The algorithm steers the heading and pitch angle of the vehicle in order to maintain a constant avoidance angle to the obstacle, thus ensuring a safe collision avoidance maneuver. The flexibility provided by moving in three dimensions is utilized by choosing an optimal pair of safe pitch and heading angles for avoidance. Furthermore, the algorithm incorporates limits on the allowed pitch angle, which are often present in practical scenarios. The collision avoidance property is mathematically proved, and the analysis is validated by several numerical simulations