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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 a novel risk vector-based near miss prediction and obstacle avoidance method. The proposed method uses the sensor readings about the pose of the other obstacles to infer their motion model (velocity and heading) and, accordingly, adapt the risk assessment and take corrective actions if necessary. Relative vector calculations allow the method to perform in real-time. The algorithm has 1.68 times faster computation performance with less change of motion than other methods and it enables a robot to avoid 25 obstacles in a congested area. Fallback behaviors are also proposed in case of faulty sensors or situation changes. Simulation experiments with parameters inferred from experiments in the ocean with our custom-made robotic boat show the flexibility and adaptability of the proposed method to many obstacles present in the environment. Results highlight more efficient trajectories and comparable safety as other state-of-the-art methods, as well as robustness to failures. 

This paper presents an optimized design of research-oriented ASVs and a systematic design evaluation methodology for reliable in-water sensing. The objective is to minimize the interference on sensor readings by any ASV maneuver. The design space includes motors and sensors locations. In addition, this paper analyzes modularity - i.e., the effects of new sensor's installation. All prototype designs are thoroughly tested using hydrostatic analyses, Computational Fluid Dynamics (CFD) simulations, and real-world field testings. Quantitative metrics, including trim, pitch, velocity magnitude of flow, and turbulence, are used to compare different configurations. Our experiments show that a motor configuration at the back part of the straights hulls is the most optimal design, resulting in high-quality data collection.