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Safe and efficient maritime navigation is fundamental for autonomous surface vehicles to support many applications in the blue economy, including cargo transportation that covers 90% of the global marine industry. We developed MARCOL, a collision avoidance decision-making framework that provides safe, efficient, and explainable collision avoidance strategies and that allows for repeated experiments under diverse high-traffic scenarios. 

Navigation and obstacle avoidance in aquatic en-vironments for autonomous surface vehicles (ASVs) in high-traffic maritime scenarios is still an open challenge, as the Convention on the International Regulations for Preventing Collisions at Sea (COLREGs) is not defined for multi-encounter situations. Current state-of-the-art methods resolve single-to-single encounters with sequential actions and assume that other obstacles follow COLREGs. Our work proposes a novel real-time non-myopic obstacle avoidance method, allowing an ASV that has only partial knowledge of the surroundings within the sensor radius to navigate in high-traffic maritime scenarios. Specifically, we achieve a holistic view of the feasible ASV action space able to avoid deadlock scenarios, by proposing (1) a clustering method based on motion attributes of other obstacles, (2) a geometric framework for identifying the feasible action space, and (3) a multi-objective optimization to determine the best action. Theoretical analysis and extensive realistic experiments in simulation considering real-world traffic scenarios demonstrate that our proposed real-time obstacle avoidance method is able to achieve safer trajectories than other state-of-the-art methods and that is robust to uncertainty present in the current information available to the ASV. 

This paper presents SVIn2, a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system, which fuses Scanning Profiling Sonar, Visual, Inertial, and water-pressure information in a non-linear optimization framework for small and large scale challenging underwater environments. The developed real-time system features robust initialization, loop-closing, and relocalization capabilities, which make the system reliable in the presence of haze, blurriness, low light, and lighting variations, typically observed in underwater scenarios. Over the last decade, Visual-Inertial Odometry and SLAM systems have shown excellent performance for mobile robots in indoor and outdoor environments, but often fail underwater due to the inherent difficulties in such environments. Our approach combats the weaknesses of previous approaches by utilizing additional sensors and exploiting their complementary characteristics. In particular, we use (1) acoustic range information for improved reconstruction and localization, thanks to the reliable distance measurement; (2) depth information from water-pressure sensor for robust initialization, refining the scale, and assisting to limit the drift in the tightly-coupled integration. The developed software—made open source—has been successfully used to test and validate the proposed system in both benchmark datasets and numerous real world underwater scenarios, including datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle Aqua2. SVIn2 demonstrated outstanding performance in terms of accuracy and robustness on those datasets and enabled other robotic tasks, for example, planning for underwater robots in presence of obstacles. 

In this paper, we present a system for measuring water quality, with a focus on detecting and predicting Harmful Cyanobacterial Blooms (HCBs). The proposed approach includes stationary multi-sensor stations, Autonomous Surface Vehicles (ASVs) collecting water quality data, and manual deployments of vertical water sampling together with vertical water quality sensor data collection, in order to monitor the health of the lake and the progress of different types of algal blooms. Traditional water monitoring is performed by manual sampling, which is limited both in the spatial and the temporal domain. The proposed method will expand the range of measurements while reducing the cost. Human sampling is still included in order to provide a base of comparison and ground truth for the automated measurements. In addition, the collected data, over multiple years, will be analyzed to infer correlations between the different measured parameters and the presence of blooms. A detailed description of the proposed system is presented together with data collected during our first sampling season.