The paper characterizes the localization performance of an Autonomous Underwater Vehicle (AUV) when it moves in environments where floating drifters or surface vessels are present and can be used for relative localization. In particular, we study how localization performance is affected by parameters e.g. AUV mobility, surface objects density, the available measurements (ranging and/or bearing) and their visibility range. We refer to known techniques for estimation performance evaluation and probabilistic mobility models, and we bring them together to provide a solid numerical analysis for the considered problem. We perform an extensive simulations in different scenarios, and, as a proof of concept, we show how an AUV, equipped with an upward looking sonar, can improve its localization estimate by detecting a surface vessel.

Oil skimmers towed by two vehicles have been widely used for skimming of oil on the water surface. In this paper, we address the cooperative control of two autonomous surface vehicles for oil skimming and cleanings. We model the skimmer as a flexible, floating rope of constant length as well as discrete segmented model. We derive the equations governing the rope dynamics from first principles and demonstrate their application through simulations. We have performed field experiments with two autonomous surface vehicles that substantiate the proposed model and provides estimates of constants underlying the model. We propose a method for controlling the shape of the rope, and derive the conditions that maximize skimming efficiency.

We present an experimental study of a mechanically scanned profiling sonar for Autonomous Surface Vehicle (ASV) obstacle detection and avoidance. We extract potential obstacles from echo returns and suggest a scanning strategy for sonar in this application. We demonstrate with simulations (driven by data collected in the field) the potential for an ASV to rely solely on sonar data to navigate and avoid obstacles in a lake and harbor environment.

We describe a technique for an Autonomous Surface Vehicle (ASV) to learn an obstacle map by classifying overhead imagery. Classification labels are supplied by a front-facing sonar, mounted under the water line on the ASV. We use aerial imagery from two online sources for each of two water bodies (a small lake and a harbor) and train classifiers using features generated from each image source separately, followed by combining their output. Data collected using a sonar mounted on the ASV were used to generate the labels in the experimental study. The results show that we are able to generate accurate obstacle maps well-suited for ASV navigation.

We present a study on the use of cooperative robots to execute a caging mission on the water's surface. In particular, we consider the problem of using two robotic boats (under-actuated autonomous surface vessels) connected with a floating rope, to `capture' a floating object from a known location on the water's surface and 'shepherd' it to a designated position. This paper focuses on the cooperative control strategy of the two vessels. Each vessel's behavior is governed by a supervisor software module that handles the communication with the other vessel and controls all elementary tasks that compose the overall mission. The elementary tasks, specifically developed for under-actuated vessels, are arranged by priority, and merged using a behavior-based approach, namely the Null-Space based Behavioral control. The proposed technique is validated by field experiments with two autonomous robotic boats on the surface of a lake.

We study the collective transport of robots (CTR) problem. A large number of commodity mobile robots are to be moved from one location to another by a single operator. Joysticking each one or carrying them physically is impractical. None of the robots are particularly sophisticated in their ability to plan or reason. Prior work on flocking and formation control has addressed the transport of a robot group that maintains its integrity by explicitly controlling coherence. We show how flocking emerges as a consequence of each robot contending for space near the human operator. A coherent flock can be made to follow a leader in this manner thereby solving the CTR problem. We also present the design of a hand-worn IMU-based gesture interface which allows the human operator to issue simple commands to the group. A preliminary experimental evaluation of the system shows robust CTR with different leader behaviors.