This brief addresses the problem of collision avoidance by robotic fish that have a single caudal fin. The fish is hybrid-tailed in that the caudal fin is driven by a double-jointed mechanism. One joint is driven by a servomotor and the second joint is driven by an ionic polymer–metal composite (IPMC) actuator, which is often called an artificial muscle. For this type of robotic fish, collision avoidance is performed by employing a collision cone approach. Within the framework of the collision cone approach, Lyapunov-based methods are employed to determine analytical expressions of nonlinear guidance laws with which cooperative collision avoidance can be achieved. It is shown how these cooperative collision avoidance laws can be made optimal in the sense of minimizing an energy-like performance index. The conditions under which the developed guidance laws are robust to sensor measurement errors are determined. Simulations and experiments are performed to validate the guidance laws.

The latest technological advancements in the development and production of sensors have led to their increased usage in marine science, thus expanding data volume and rates within the field. The extensive data collection efforts to monitor and maintain the health of marine environments supports the efforts in data driven learning, which can help policy makers in making effective decisions. Machine learning techniques show a lot of promise for improving the quality and scope of marine research by detecting implicit patterns and hidden trends, especially in big datasets that are difficult to analyze with traditional methods. Machine learning is extensively used on marine science data collected in various regions, but it has not been applied in a significant way to data generated in the Gulf of Mexico (GOM). Machine learning methods using ocean science data are showing encouraging results and thus are drawing interest from data science researchers and marine scientists to further the research. The purpose of this paper is to review the existing approaches in studying GOM data, the state of the art in machine learning techniques as applied to the GOM, and propose solutions to GOM data problems. We review several issues faced by marine environments in GOM in addition to climate change and its effects. We also present machine learning techniques and methods used elsewhere to address similar problems and propose applications to problems in the GOM. We find that Harmful Algal Blooms (HABs), hypoxia, and sea-level rises have not received as much attention as other climate change problems and within the machine learning literature, the impacts on estuaries and coastal systems, as well as oyster mortality (also major problems for the GOM) have been understudied – we identify those as important areas for improvement. We anticipate this manuscript will act as a baseline for data science researchers and marine scientists to solve problems in the GOM collaboratively and/or independently.

While study of ocean dynamics usually involves modeling deep ocean variables, monitoring and accurate forecasting of nearshore environments is also critical. However, sensor observations often contain artifacts like long stretches of missing data and noise, typically after an extreme event occurrence or some accidental damage to the sensors. Such data artifacts, if not handled diligently prior to modeling, can significantly impact the reliability of any further predictive analysis. Therefore, we present a framework that integrates data reconstruction of key sea state variables and multi-step-ahead forecasting of current speed from the reconstructed time series for 19 depth levels simultaneously. Using multivariate chained regressions, the reconstruction algorithm rigorously tests from an ensemble of tree-based models (fed only with surface characteristics) to impute gaps in the vertical profiles of the sea state variables down to 20 m deep. Subsequently, a deep encoder–decoder model, comprising multi-head convolutional networks, extracts high-level features from each depth level’s multivariate (reconstructed) input and feeds them to a deep long short-term memory network for 24 h ahead forecasts of current speed profiles. In this work, we utilized Viking buoy data, and demonstrated that with limited training data, we could explain an overall 80% variation in the current speed profiles across the forecast period and the depth levels.

Unmanned maritime vehicles (UMVs) are increasingly used in several maritime domain applications including seafloor mapping, mine countermeasures, pipeline and infrastructure inspection, and unexploded ordnance detection. While each application presents unique challenges in terms of target characteristics, platform constraints, and environmental conditions, mission planners often lack quantitative guidance for optimal sensor selection. In this paper, we present a simulation-based comparison of magnetic radiometry, gravity sensing, and synthetic aperture sonar (SAS). We assembled multidomain modeling frameworks coupling COMSOL magnetostatic analysis with analytical ellipsoid approximations, gravity anomaly calculations, and a complete SAS model benchmarked against Kraken Robotics specifications. Comparative simulations across targets ranging from small ordnance to submarines yielded several clear results. Gravity sensors, while appealing because they can detect any mass regardless of material, lack sufficient sensitivity for practical UMV integration given current technology simulations. Magnetic sensing dominates when targets are ferrous and buried, where acoustic penetration is limited, while SAS provides advantages for wide-area search and non-magnetic target detection. Based on these findings, we provide a calibrated, openly available SAS detection-range model, together with supporting magnetic and gravimetric analyses, intended to place these modalities on a common footing for systems design and mission planning. The model components are drawn from established sonar theory; the contribution is an accessible, hardware-referenced tool for multi-modal sensor comparison rather than a new detection method. The framework provides mission planners with quantitative guidance for multi-modal sensor deployment strategies.