Autonomous vehicles are very useful for collecting data in a plethora of environments of scientific interest (land, air, and sea) . However, the platform being used to collect the sensing data is limited by the amount of stored energy that is carried aboard the vehicle. The sensing platform can only travel and sense as far as its stored energy capacity will allow it to. The question then becomes how one obtains the most accurate estimation of the scalar field while being limited by the stored energy constraints of the vehicle being used to sample the scalar field.
We have experimentally evaluated various sampling strategies based upon their relative estimation accuracy and energy consumption with an AUV (pictured above). Initially we are evaluating offline sampling path strategies.The sampling path strategies that have been evaluated are systematic sampling and random lawnmower and spiral sample paths.
Real world data collected by an AUV was used to generate the data used in the experimental evaluation.
Through the results of the estimation error evaluation, we hypothesize that the systematic sampling strategies provide better estimation errors for both isotropic and anisotropic scalar fields for sparse sampling densities, and the random stratified sampling strategies provide better estimation errors for dense sampling densities.