OBSERVABILITY-AWARE PATH PLANNING FOR AUTONOMOUS NAVIGATION
Raymond Neistat · 2024
Terrain-Aided Navigation (TAN) is a popular method of localization for GPS-denied vehicles, particularly in the marine domain. There are many ways to perform TAN in a marine setting, such as Bathymetric SLAM (BSLAM) and Bayesian Filtering with the aid of an a priori map. These techniques have been studied extensively, but show an overall lack of rigorous observability analyses. Without nonlinear observability analyses, TAN practitioners do not have an analytical indicator to know which areas of terrain will provide opportunities for the best localization performance. This thesis reviews current developments in the endeavors of nonlinear observability analyses as well as TAN and presents a potential solution for converting a scalar field for vehicle traversal into an observability map. This observability map is passed into a variation of Dijkstra's algorithm for path planning with the goal of navigating through observable zones, thus to minimize state error. This path planner is then tested with a series of MATLAB simulations, featuring custom-generated maps as well as a well-studied portion of the seafloor that tests the technique's viability on naturally-occurring terrain.