Sonar Path Planning Using Reinforcement Learning

Passive towed array sonar systems play an essential role in submarine situational awareness. However, the detection and localisation of sound-emitting objects is a more challenging task compared to their active counterparts due to a lack of immediate range information. By making manoeuvres and changing the bearings at multiple positions, a passive sonar can localise and track the source of the sound. Reinforcement learning is the process of learning an optimal strategy to guide an agent’s actions towards optimising its cumulative reward for a given task. This work evaluates an agent’s ability to control a passive towed array sonar system for optimal source localisation and tracking in the underwater environment, using collision avoidance as a practical example application.