Neural Swarm Control Algorithm for Underwater Vehicles

Abstract

The paper presents the application of an evolutionary recurrent neural network to control the swarm of underwater vehicles. In the swarm, one vehicle is the leader and the others are followers. The leader leads the swarm along a predefined trajectory without regard for the followers while the followers follow the leader and avoid collisions with all other vehicles. Avoiding collisions by the swarm with external obstacles is done by changing the depth. The leader is responsible for detecting the obstacles and informing all the followers about the need to change the depth. To follow the leader, the followers use the information about the distance to it. Directional information is unavailable to them. To avoid collisions inside the swarm, the followers use shortrange sensors.