This paper addresses the design of mobile sensor networks for optimal data collection. The development is strongly motivated by the application to adaptive ocean sampling for an autonomous ocean observing and prediction system. A performance metric, used to derive optimal paths for the network of mobile sensors, defines the optimal data set as one which minimizes error in a model estimate of the sampled field. Feedback control laws are presented that stably coordinate sensors on structured tracks that have been optimized over a minimal set of parameters. Optimal, closed-loop solutions are computed in a number of low-dimensional cases to illustrate the methodology. Robustness of the performance to the influence of a steady flow field on relatively slow-moving mobile sensors is also explored

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Collective Motion, Sensor Networks, and Ocean Sampling

A small (50-kg, 2-m long) underwater vehicle with operating speeds of 20-30 cm/s and ranges up to 6000 km has been developed and field tested. The vehicle is essentially an autonomous profiling float that uses a buoyancy engine to cycle vertically and wings to glide horizontally while moving up and down. Operational control and data relay is provided by GPS navigation and two-way communication through ORBCOMM low-Earth-orbit satellites. Missions are envisioned with profile measurements repeated at a station or spaced along a transect. The initial instrument complement of temperature, conductivity, and pressure sensors was used to observe internal waves and tides in the Monterey underwater canyon.

The autonomous underwater glider "Spray"

Operations with multiple autonomous underwater vehicles (AUVs) have a variety of underwater applications. For example, a coordinated group of vehicles with environmental sensors can perform adaptive ocean sampling at the appropriate spatial and temporal scales. We describe a methodology for cooperative control of multiple vehicles based on virtual bodies and artificial potentials (VBAP). This methodology allows for adaptable formation control and can be used for missions such as gradient climbing and feature tracking in an uncertain environment. We discuss our implementation on a fleet of autonomous underwater gliders and present results from sea trials in Monterey Bay in August, 2003. These at-sea demonstrations were performed as part of the Autonomous Ocean Sampling Network (AOSN) II project

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Multi-AUV Control and Adaptive Sampling in Monterey Bay

We describe the development of feedback control for autonomous underwater gliders. Feedback is introduced to make the glider motion robust to disturbances and uncertainty. Our focus is on buoyancy-propelled, fixed-wing gliders with attitude controlled by means of active internal mass redistribution. We derive a nonlinear dynamic model of a nominal glider complete with hydrodynamic forces and coupling between the vehicle and the movable internal mass. We use this model to study stability and controllability of glide paths and to derive feedback control laws. For our analysis, we restrict to motion in the vertical plane and consider linear control laws. For illustration, we apply our methodology to a model of our own laboratory-scale underwater glider.

Model-based feedback control of autonomous underwater gliders

Self-localization of an underwater vehicle is particularly challenging due to the absence of Global Positioning System (GPS) reception or features at known positions that could otherwise have been used for position computation. Thus Autonomous Underwater Vehicle (AUV) applications typically require the pre-deployment of a set of beacons. 
This thesis examines the scenario in which the members of a group of AUVs exchange navigation information with one another so as to improve their individual position estimates. 
We describe how the underwater environment poses unique challenges to vehicle navigation not encountered in other environments in which robots operate and how cooperation can improve the performance of self-localization. As intra-vehicle cornmunication is crucial to cooperation, we also address the constraints of the communication channel and the effect that these constraints have on the design of cooperation strategies. 
The classical approaches to underwater self-localization of a single vehicle, as well as more recently developed techniques are presented. We then examine how methods used for cooperating land-vehicles can be transferred to the underwater domain. An algorithm for distributed self-localization, which is designed to take the specific characteristics of the environment into account, is proposed. 
We also address how correlated position estimates of cooperating vehicles can lead to overconfidence in individual position estimates. 
Finally, key to any successful cooperative navigation strategy is the incorporation of the relative positioning between vehicles. The performance of localization algorithms with different geometries is analyzed and a distributed algorithm for the dynamic positioning of vehicles, which serve as dedicated navigation beacons for a fleet of AUVs, is proposed. (Copies available exclusively from MIT Libraries, Rm. 14-0551, Cambridge, MA 02139-4307. Ph. 617-253-5668; Fax 617-253-1690.)

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Cooperative localization for autonomous underwater vehicles

Seagliders are small, reusable autonomous underwater vehicles designed to glide from the ocean surface to a programmed depth and back while measuring temperature, salinity, depth-averaged current, and other quantities along a sawtooth trajectory through the water. Their low hydrodynamic drag and wide pitch control range allow glide slopes in the range 0.2 to 3. They are designed for missions in a range of several thousand kilometers and durations of many months. Seagliders are commanded remotely and report their measurements in near real time via wireless telemetry. The development and operation of Seagliders and the results of field trials in Puget Sound are reported.

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T. Wen

Papers

Seaglider: a long-range autonomous underwater vehicle for oceanographic research