Abstract
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 into 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 nontrivial. 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 towards achieving human-level decision making in autonomous vehicle systems within extreme obstacle configurations.
Highlights
Autonomous Underwater Vehicles (AUVs) are used in many subsea commercial applications such as seafloor mapping, inspection of pipelines and subsea structures, ocean exploration, environmental monitoring, and various research operations
Test reports from quantitative tests, which are obtained by running the simulation for a large sample of episodes and calculating statistical averages, are given
The quantitative results are obtained by running each training scenario, configured randomly in each episode, for N 100 episodes
Summary
Autonomous Underwater Vehicles (AUVs) are used in many subsea commercial applications such as seafloor mapping, inspection of pipelines and subsea structures, ocean exploration, environmental monitoring, and various research operations. The wide range of operational contexts implies that truly autonomous vehicles must be able to follow spatial trajectories (path following), avoid collisions along these trajectories (collision avoidance), and maintain a desired velocity profile (velocity control). AUVs are often underactuated by the fact that they operate with three generalized actuators (propeller, elevation, and rudder fins) in six degrees of freedom (6-DOF) (Fossen, 2011). Since path following and collision avoidance are the two main challenges addressed in this paper, a brief overview of the state of the art is provided in the following subsections
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