Prognostics-enhanced Receding Horizon Mission Planning for Field Unmanned Vehicles

This paper presents a preliminary study for using prognostic information to enhance the mission/path planning in a non-uniform environment. Prognostic information is introduced in order to ensure that the mission failure can be minimized even when a fault occurs. This will enhance the performance of autonomous vehicles that often work in harsh environments that cause aging, fatigue, and fracture. When a fault occurs, the proposed path planning scheme predicts the remaining useful life (RUL) of the vehicle. This RUL is then used as a constraint in path planning to minimize the life consumption with other factors such as minimization of energy and travel time. The proposed planning algorithm integrates the prognosis and path planning in a receding horizon planning framework. Like field D* searching algorithm, the map is described by grids while nodes are defined on corners of grids. The planning algorithm divides the map into three areas, implementation area, observation area, and unknown area. We assume that the autonomous vehicle is equipped with onboard sensors that are able to detect and determine the terrain in a certain range, which is observation area. The implementation area consists of the gird next to the current node. The area beyond observation area is the unknown (un-observed) area where the terrain is unknown to vehicle. At a node, the vehicle plans the path from the vehicles’ current location to the destination. Only the path planned in the implementation area is executed. This process is repeated until the destination is reached or it turns out that no route can lead to destination or the vehicle reaches its end of life. The simulation results demonstrate the effectiveness of the proposed approach.