An Intelligent Approach to Hysteresis Compensation while Sampling using a Fleet of Autonomous Watercraft
This paper addresses the problem of using a fleet of autonomous watercraft to create models of various water quality parameters in complex environments using intelligent sampling algorithms. Maps depicting the spatial variation of these parameters can help researchers understand how certain ecological processes work and in turn help reduce the negative impact of human activities on the environment. In our domain of interest, it is infeasible to exhaustively sample the field to obtain statistically significant results. This problem is pertinent to autonomous water sampling where hysteresis in sensors causes delay in obtaining accurate measurements across a large field. In this paper, we present several different approaches to sampling with cooperative vehicles to quickly build accurate models of the environment. In addition, we describe a novel filter and a specialized planner that uses the gradient of sensor measurements to compensate for hysteresis while ensuring a fast sampling process. We validate the algorithms using results from both simulation and field experiments with four autonomous airboats measuring temperature and dissolved oxygen in a lake.