Global Off-Road Path Planning of Unmanned Ground Vehicles Based on the Raw Remote Sensing Map

Abstract

<div class="section abstract"><div class="htmlview paragraph">Unmanned Ground Vehicle (UGV) has a wide range of applications in the military, agriculture, firefighting and other fields. Path planning, as a key aspect of autonomous driving technology, plays an essential role for UGV to accomplish the established driving tasks. At present, there are many global path planning algorithms in grid maps on unstructured roads, while general grid maps do not consider the specific elevation or ground type difference of each grid, and unstructured roads are generally considered as flat and open roads. On the contrary, the unmanned off-road is always a bumpy road with undulating terrain, and meanwhile, the landform is complex and the types of features are diverse. In order to ensure the safety and improve the efficiency of autonomous driving of UGV in off-road environment, this paper proposes a global off-road path planning method for UGV based on the raw image of remote sensing map. Firstly, the raw image is gridded. The map elevation information is assigned based on the digital elevation model (DEM) and the terrain is classified and labeled in the grid map based on the back propagation neural network (BPNN). Based on the reconstructed off-road grid map, a modified A* algorithm considering the safety and efficiency of UGV passage is designed for global path planning on off-road environment. Simulation results based on real off-road environment show that the proposed global planning algorithm can avoid impassable areas and make UGVs drive on high traffic efficiency roads as much as possible.</div></div>