A Non-Parametric Iterative Algorithm For Adaptive Sampling And Robotic Vehicle Path Planning

Abstract

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