We present RENEW, a novel global path planning framework for Autonomous Surface Vehicle (ASV) operating in dynamic environments with external disturbances (e.g., water currents). These disturbances significantly affect both the risk and energy cost of navigation, particularly in constrained coastal waterways, by dynamically reshaping the navigable area. RENEW addresses this challenging scenario through a unified, risk- and energy-aware planning strategy that guarantees safety by explicitly identifying states at risk of entering non-navigable regions and enforcing adaptive safety constraints. Our planner incorporates a best-effort strategy under worst-case scenarios, inspired by contingency planning concepts from maritime domains, to ensure feasible control actions even under adverse conditions. RENEW employs a hierarchical architecture: a high-level planner explores topologically distinct paths via constrained triangulation, while a low-level planner selects an energy-efficient and kinematically feasible trajectory within a safe corridor. We validate our approach through extensive simulations using both custom realistic scenarios and real-world ocean current data. To our knowledge, this is the first global planning framework to jointly address the adaptive identification of non-navigable areas and topological diversity within a risk-aware paradigm, enabling robust navigation in maritime environments.

This paper proposes a novel decision-making framework for planning "when" and "where" to deploy robots based on prior data with the goal of persistently monitoring a spatio-temporal phenomenon in an environment. We specifically focus on large lake monitoring, where remote sensors, such as satellites, can provide a snapshot of the target phenomenon at regular cycles. Between these cycles, Autonomous Surface Vehicles (ASVs) can be deployed to maintain an up-to-date model of the phenomenon. However, deploying ASVs has a significant logistical overhead in terms of time and cost. It requires a team of people to go on site and spend typically a day to monitor the deployment. It is vital to not only be intentional about where to sample in the environment on a given day, but also determine the worth of deploying the ASVs that day at all. Therefore, we propose a persistent monitoring strategy that provides the days and locations of when and where to sample with the robots by leveraging Gaussian Process model estimates of future trends based on collected remote sensing and point measurement data. Our approach minimizes the number of days and locations for sampling, while preserving the quality of estimates. Through simulation experiments using realistic spatio-temporal datasets, we demonstrate the benefits of our approach over traditional deployment strategies, including significant savings on the effort and operational cost of deploying the ASVs.

This paper enhances the obstacle avoidance of Autonomous Surface Vehicles (ASVs) for safe navigation in high-traffic waters with an active state estimation of obstacle’s passing intention and reducing its uncertainty. We introduce a topological modeling of passing intention of obstacles, which can be applied to varying encounter situations based on the inherent embedding of topological concepts in COLREGs. With a Long Short-Term Memory (LSTM) neural network, we classify the passing intention of obstacles. Then, for determining the ASV maneuver, we propose a multi-objective optimization framework including information gain about the passing obstacle intention and safety. We validate the proposed approach under extensive Monte Carlo simulations (2,400 runs) with a varying number of obstacles, dynamic properties, encounter situations, and different behavioral patterns of obstacles (cooperative, non-cooperative). We also present the results from a real marine accident case study as well as real-world experiments of a real ASV with environmental disturbances, showing successful collision avoidance with our strategy in real-time.

This paper proposes a fully integrated dual-output continuously scalable-conversion-ratio (CSCR) switched-capacitor (SC) converter that increases the overall power conversion efficiency (PCE) beyond that of the conventional dual-output SC converters. The structure employs proposed dual-output CSCR SC stage and channel SC stage to transfer charges to two output load voltages ($V_{\mathrm{OUT}}$s) with high PCE. Also, the converter is controlled by analog switching frequency modulation (ASFM) and digital flying capacitance modulation (DFCM) loops to regulate both$V_{\mathrm{OUT}}$s simultaneously. The proposed converter is fabricated using a 180 nm CMOS process, and regulates$V_{\mathrm{OUT}}$of 1.1–1.6 V and 0.55–0.95 V with an input voltage of 1.5–1.9 V. In measurement, the proposed converter achieves a maximum PCE of 85%, and an average PCE of 78.6% for the available$V_{\mathrm{OUT}}$ranges. Moreover, the converter exhibits the maximum$I_{\mathrm{OUT}}$s of 21 mA and 4 mA, respectively.

There are many applications where an autonomous agent can perform many sets of actions. It must choose one set of actions based on some behavioral constraints on the agent. Past work has used deontic logic to declaratively express such constraints in logic, and developed the concept of a feasible status set (FSS), a set of actions that satisfy these constraints. However, multiple FSSs may exist and an agent needs to choose one in order to act. As there may be many different objective functions to evaluate status sets, we propose the novel concept of Pareto-optimal FSSs or POSS. We show that checking if a status set is a POSS is co-NP-hard. We develop an algorithm to find a POSS and in special cases when the objective functions are monotonic (or anti-monotonic), we further develop more efficient algorithms. Finally, we conduct experiments to show the efficacy of our approach and we discuss possible ways to handle multiple Pareto-optimal Status Sets.

Drone based terrorist attacks are increasing daily. It is not expected to be long before drones are used to carry out terror attacks in urban areas. We have developed the DUCK multi-agent testbed that security agencies can use to simulate drone-based attacks by diverse actors and develop a combination of surveillance camera, drone, and cyber defenses against them.

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.

This paper proposes a modification of the Gaussian mixture probability hypothesis density (GM-PHD) filter to compute online the probability of detection$(P_{D})$and probability of survival$(P_{S})$of targets. This eliminates the need for predetermined and/or constant$P_{D}$and$P_{S}$values, that may degrade the estimation. The proposed filter estimates the$P_{D}$and$P_{S}$values for each individual target based on newly introduced parameters, which are updated during the measurement update process. The effectiveness of the proposed filter was validated through an in-lab experiment using four unmanned ground robots with varying$P_{D}$values and a real-world lidar-based obstacle tracking system implemented on an Automated Surface Vehicle operating in a lake with real-time boat traffic. The results of the experiments demonstrate that the proposed filter outperforms the standard PHD filter with incorrect$P_{D}$and$P_{S}$values. These findings highlight the potential benefits of the proposed filter in improving target tracking performance in complex environments.

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 exper-iments 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.

Identifying in-water obstacles is fundamental for safe navigation of Autonomous Surface Vehicles (ASVs). This paper presents a model-free method for segmenting individual in-water objects (e.g., swimmers, buoys, boats) and shorelines from LiDAR sensor data. To reduce the computational requirement, our method first converts the 3D point cloud into a 2D spherical projection image. Then, an algorithm based on the integration of a breadth-first search and a variant of a hierarchical agglomerative clustering segments the points according to different objects. Our method addresses the sparsity and instability of the point cloud in the aquatic domain – a characteristic that makes the methods developed for self-driving cars not directly applicable for in-water obstacle segmentation, as demonstrated in our experiments. Our method is compared with other state-of-the-art approaches and is validated both in simulation and in real-world ASV deployments, with different objects and encountering scenarios. The proposed method is effective in segmenting in-water obstacles not known a priori, in real-time, outperforming other state-of-the art methods.

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.