Neural control system for a swarm of autonomous underwater vehicles

Abstract

The paper presents a neural control system for a swarm of underwater vehicles. A swarm consists of a leader vehicle and follower vehicles. The leader leads the swarm along a predetermined trajectory, detects obstacles and determines obstacle avoidance manoeuvrers for the whole swarm, while the followers follow the leader in a specific formation and avoid the leader and each other. In order to keep the formation without colliding with other members of the swarm, the followers are equipped with a neural control system designed with a neuro-evolutionary algorithm called Hill Climb Assembler Encoding. The system is fed with three sources of information. The first is the leader which, using an acoustic communication system, sends information about its manoeuvrers as well as about the desired type of formation. The second source is sonar, i.e. underwater acoustic radar, whose disadvantage is the difficulty in interpreting the results of operation, i.e. it is difficult to determine whether the observed object is another vehicle or an obstacle. The third source is a camera observation unit consisting of three cameras located around the vehicle. Unfortunately, the disadvantage of this unit is a very short range in opaque water. The proposed control system was tested in simulation conditions and proved to be highly effective. The results of the simulation as well as the design of the system are presented in the paper.