Incorporating terrain uncertainties in autonomous vehicle path planning

While inherent uncertainty in obstacle location for autonomous vehicle path planning has been investigated previously, terrain map uncertainty has largely been ignored. The terrain map is constructed via a priori data that in general, is incorrectly utilized as "perfect information". In this paper a probabilistic approach using empirical loss functions is developed to incorporate terrain map uncertainties into the path planning process. A new graph search method is introduced that allows the inclusion of negative weight edges on a optimally planned path. Representative results from forty simulations are presented which indicate that the incorporation of terrain map uncertainty can affect the planned paths that are generated for an autonomous robotic vehicle.