Using Hill Climb Assembler Encoding neural networks to control follower vehicles in an underwater swarm

Abstract

The paper presents the application of neural networks evolving under the Hill Climb Assembler Encoding (HCAE) algorithm to control follower autonomous underwater vehicles that are members of a swarm consisting of one leader vehicle and a group of followers. The leader is responsible for global navigation and guiding the swarm, while low-cost followers unable to cover longer distances on their own follow the leader. To locate the leader, the followers only use information about the distance to it. Directional information is unavailable to the followers. Moreover, information about the distance is transmitted to followers with a frequency depending on the number of followers: the more followers, the lower the frequency. In addition to tracking the leader, the followers must also be able to avoid collisions with other followers and the leader. To this end, they are equipped with short-range sensors looking around each follower. The simulations presented in the paper were carried out for different swarm sizes, different sensor ranges, different collision distances, and variable leader speed, and showed high effectiveness of the proposed neural solution. The most common strategy for the followers when following a leader was to circle it at a certain safe distance depending on the sensor range.