We present field experiment results of ocean plume surveys conducted with a robotic platform. In our experiments, Rhodamine dye was used to generate a chemical plume in a coastal environment, and an unmanned surface vessel equipped with fluorometer sensors was deployed to conduct plume surveys. We present plume front and cross sectional survey results, where the robot is driven along visible plume boundaries and plume cross sections, respectively. Datasets collected during the experiments are shown together with statistical parameters that quantify the fine-scale plume structure. Experimental evidence shows that the assumption of the existence of a continuous and fixed-value concentration curve used in existing theoretical work for plume front tracking appears too strong for real ocean plumes. The datasets provide characteristic parameters that can be used for further development and verification of simulation models of ocean pollution plumes.

Using autonomous mobile robots to dynamically track oil plume propagation in ocean environments is challenging. Based on a model of advection-diffusion equation that describes point-source pollution propagation in marine environments, we have previously proposed a model-based estimator-controller design for autonomous robots to dynamically track plume front propagation. In this paper we study the robustness of the controller in a robot simulator, and evaluate it's performance in a realistic environmental model setup. A probabilistic Lagrangian environmental model is used that can capture both the time-averaged, idealized structure and the instantaneous, realistic structure of a dynamic plume. The controller is implemented on the Field Robotics Lab Vehicle Software, which uses Lightweight Communication and Marshalling library to facilitate process communication and for easy transit to field testing. Simulation results are shown with discussions and lessons learned to guide future field experiments.

We study robotic tracking of dynamic plume front modeled by the advection-diffusion equation in this paper. Different from existing work purely relying on gradient measurement, the transport model of pollution source is explicitly considered in tracking control design. We first study the problem using a single robot and solve the problem in an estimation and control framework. We then extend it to the multi-robot case in a nearest-neighbor communication structure, and have the robots take formation along the plume front. The distributed control is scalable to a large number of robots. Simulation results show satisfactory performances of the proposed method.

In this paper we present a general framework for predicting the positioning uncertainty of underwater vehicles. We apply this framework to common examples from marine robotics: standalone long baseline (LBL) positioning and integrated LBL reference and Doppler velocity log (DVL) dead-reckoning. The approach is based on formulating positioning as an estimation problem. Using simple sensor models for the most common information sources, we show how the the Cramer Rao lower bound can be used to predict the system-level navigation performance. The resulting three dimensional covariance matrix is then summarized using scalar performance metrics based on the notion of dilution of precision (DOP), a well-known concept from the global positioning system (GPS) community. To illustrate this general tool, we present the answers to a few particular questions: · How does the baseline length affect the solution for standalone LBL positioning? · When using DVL and heading odometry, how precise is the combined DVL/LBL solution? · For an integrated DVL/LBL solution, what is the required update rate from the absolute reference to constrain odometry drift? · What is the relative importance of heading, odometry and range precision for overall performance? We substantiate our estimation framework through experimental evidence which shows that the analytical predictions are consistent with performance in the field.