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.

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.

Aquaculture net pens inspection and monitoring are important to ensure net stability and fish health in the fish farms. Remotely operated vehicles (ROVs) offer a low-cost and sophisticated solution for the regular inspection of the underwater fish net pens due to their ability of visual sensing and autonomy in a challenging and dynamic aquaculture environment. In this paper, we report the integration of an ROV with a visual servoing scheme for regular inspection and tracking of the net pens. We propose a vision-based positioning scheme that consists of an object detector, a pose generator, and a closed-loop controller. The system employs a modular approach that first utilizes two easily identifiable parallel ropes attached to the net for image processing through traditional computer vision methods. Second, the reference positions of the ROV relative to the net plane are extracted on the basis of a vision triangulation method. Third, a closed-loop control law is employed to instruct the vehicle to traverse from top to bottom along the net plane to inspect its status. The proposed vision-based scheme has been implemented and tested both through simulations and field experiments. The extensive experimental results have allowed the assessment of the performance of the scheme that resulted satisfactorily and can supplement the traditional aquaculture net pens inspection and tracking systems.

Marine robotics requires high quality simulators due to complexity and availability of trials at sea. While there are several simulators in the literature, these have gaps in applications requiring high visual fidelity and human-robot interaction. This paper describes Marine Robotics Unity Simulator (MARUS), an open-source simulator for marine robotics based on Unity3D software that aims to close those gaps. It can simulate multi-robot systems in a marine environment offering water physics and a wide array of sensors commonly used in underwater and maritime domain. An interface allowing inter-connectivity with the simulator was developed where the focus was put on ROS 1/2 integration preferred in robotics community. Due to growing requirements on high-fidelity visual simulation required for machine learning, tools were developed for enabling dataset generation with automatic annotations. Examples of simulator application demonstrate the capabilities of the simulator for real world problems encountered during research project development.

SCUBA diving activities are classified as high-risk due to the dangerous environment, dependency on technical equipment that ensures life support, reduced underwater navigation and communication capabilities all of which compromise diver safety. While autonomous underwater vehicles (AUVs) have become irreplaceable tools for seabed exploration, monitoring, and mapping in various applications, they still lack the higher cognitive capabilities offered by a human diver. The research presented in this paper was carried out under the EU FP7 “CADDY—Cognitive Autonomous Diving Buddy”. It aims to take advantage of both human diver and AUV complementary traits by making their synergy a potential solution for mitigation of state of the art diving challenges. The AUV increases diver safety by constantly observing the diver, provides navigation aiding by directing the diver and offers assistance (e.g., lights, tool fetching, etc.). The control algorithms proposed in the paper provide a foundation for implementing these services. These algorithms use measurements from stereo-camera, sonar and ultra-short baseline acoustic localization to ensure the vehicle constantly follows and observes the diver. Additionally, the vehicle maintains a relative formation with the diver to allow observation from multiple viewpoints and to aid underwater navigation by pointing towards the next point of interest. Performance of the proposed algorithms is evaluated using results from pool experiments.