9 accepted papers
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
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 b…
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…
This paper proposes a modification of the Gaussian mixture probability hypothesis density (GM-PHD) filter to compute online the probability of detection (P_{D})(P_{D}) and probability of survival (P_{S})(P_{S}) of targets. This eliminates the need for predetermined and/or constant P_{D}P_{D} and P_{…
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 combinati…
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 saf…
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…
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, o…
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 acti…