Controlling a Swarm of Low-Cost Underwater Vehicles Under Conditions of Limited Navigation, Communication and Observation.

This paper addresses the problem of controlling autonomous underwater vehicles (AUVs) operating in a swarm under realistic underwater conditions characterised by inaccurate navigation, limited acoustic communication, and noisy sonar observations. A novel Trail Sonar-Based Algorithm (TSBA) is proposed for leader-follower swarm control. Unlike conventional reactive approaches, TSBA combines sparse acoustic communication with prior knowledge of the mission plan, enabling predictive estimation of the tracked vehicle's state and reducing the dependence on continuous information exchange. To evaluate its effectiveness, TSBA was compared with a machine learning-based controller (NSCSUV) in a simulation environment incorporating navigation drift, sonar measurement errors, and a data-driven model of a real low-cost AUV. The proposed vehicle model achieved a mean speed error of 0.107 m/s and a mean heading error of 14.25°, providing a realistic basis for controller evaluation. Simulation results demonstrated that TSBA consistently outperformed the neural network-based approach in formation keeping while generating smoother control commands and requiring only minimal underwater communication. The algorithm maintained stable swarm behaviour despite sensor inaccuracies and communication constraints. Finally, experiments conducted with a real underwater vehicle confirmed the practical applicability and robustness of the proposed approach under real operating conditions.