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This paper describes an underwater acoustic sensor network consisting of a heterogeneous robotic swarm used for long-term monitoring of underwater environments. The swarm consists of a large number of underwater robots acting as sensor nodes with limited movement capabilities, and a few surface robots aiding them in accomplishing underwater monitoring scenarios. Main interactions between two types of robots include underwater sensor deployment and relocation, energy and data exchange, and acoustic localisation aiding. Hardware capabilities of each vehicle are described in detail. Inter-agent communication is split into two layers: surface and underwater communication. Surface communication utilises wireless communication using WiFi routers configured for decentralised routing. Underwater communication mainly uses acoustic communication which, when used within a large swarm, poses a challenging task because of high probability of interference and data loss. The acoustic communication protocol used to prevent these issues is presented in detail. Finally, more complex functionalities of the robotic swarm are presented, including several results from real-life experiments.

In the scenario where an underwater vehicle tracks an underwater target, reliable estimation of the target position is required. While USBL measurements provide target position measurements at low but regular update rate, multibeam sonar imagery gives high precision measurements but in a limited field of view. This paper describes the development of the tracking filter that fuses USBL and processed sonar image measurements for tracking underwater targets for the purpose of obtaining reliable tracking estimates at steady rate, even in cases when either sonar or USBL measurements are not available or are faulty. The proposed algorithms significantly increase safety in scenarios where underwater vehicle has to maneuver in close vicinity to human diver who emits air bubbles that can deteriorate tracking performance. In addition to the tracking filter development, special attention is devoted to adaptation of the region of interest within the sonar image by using tracking filter covariance transformation for the purpose of improving detection and avoiding false sonar measurements. Developed algorithms are tested on real experimental data obtained in field conditions. Statistical analysis shows superior performance of the proposed filter compared to conventional tracking using pure USBL or sonar measurements.