This work focuses on the observation of environmental phenomena that occur as spatial distributions in two and three dimensions, using sensor-enabled mobile vehicle (ground,air or undersea). Algorithms to guide an adaptive exploration of a given region through systematic choice of sampling locations under the constraints imposed by vehicles are presented. Variation sensitive multiresolution sample distributions are achieved through an iterative variation sensitive estimation of the unknown process.

Efficient adaptive strategies are required to facilitate the role of robotic vehicles as mobile platforms supporting sensing, monitoring, and tracking capabilities. Such strategies utilize a representation of sensor variable fields as a basis for the selection of sample points. In this paper a curvature based criterion for sample selection is presented. The curvature-sensitive sampling algorithm (CSS) utilizes the estimated second-derivative of an intermediate variable field to select sample points of interest for complex process models, such as used in oceanographic sampling with AUVs. For processes for which little or no prior knowledge base exists, an iterative curvature-based adaptive sampling algorithm (ICASA) is presented. The ICASA algorithm iteratively selects sets of sample locations based on non-parametric field representations. These algorithms are evaluated with respect to simulated data, experimental data, and data from oceanographic models. The performance is shown to be significantly better than the conventional uniform grid methodology. The selected iterative samples are used to create a path plan for a robotic vehicle sampling in the region of interest

The integration of mobile robotic vehicles with distributed sensor networks requires the development of methods for vehicle navigation to achieve sample selection and effectively estimate distributed task variables. In this paper, singular value decomposition (SVD) of the parameter variance space is introduced as a basis for optimal sample selection. Simulation results are used to evaluate the algorithm performance, and significant reduction in field prediction variance are achieved over more conventional incremental rectangular measurement grids. An example of field estimation sensors on an autonomous underwater vehicle (AUV) is described.

There are various kinds of autonomous vehicles (AV's) which can operate with varying levels of autonomy. This paper is concerned with underwater, ground and aerial vehicles operating in a fully autonomous (nonteleoperated) mode. Further this paper deals with AV's as a special kind of device, rather than full-scale manned vehicles operating unmanned. The distinction is one in which the AV is likely to be designed for autonomous operation rather than being adapted for it as would be the case for manned vehicles. We provide a survey of the technological progress that has been made in AV's, the current research issues and approaches that are continuing that progress, and the applications which motivate this work. It should be noted that issues of control are pervasive regardless of the kind of AV being considered, but that there are special considerations in the design and operation of AV's depending on whether the focus is on vehicles underwater on the ground, or in the air. We have separated the discussion into sections treating each of these categories.

Practical lamps for undersea illumination are closely approximated by point sources, the light from which diverges and also attenuates due to scattering and absorption by the water. The objective is to determine the error introduced by these departures from the usual lighting assumptions of photometric stereo and to find an approach to solution of the brightness equations. It is demonstrated that an iterative approach to photometric stereo, plus a sparse range map containing as little as one range datum for each object in the scene, is sufficient to permit determination of the surface gradients. Simulation indicated that the method improves accuracy and was robust with respect to measurement error in both sparse range data and attenuation length.>