Risk-Inspired Aerial Active Exploration for Enhancing Autonomous Driving of UGV in Unknown Off-Road Environments

Unknown area exploration is a crucial but challeng- ing task for autonomous driving of unmanned ground vehicles (UGV) in unknown off-road environments. However, the explo- ration efficiency of a single UGV is low due to its limited sensing range. To solve this problem, this paper proposes a risk-inspired aerial active exploration system, which utilizes the flexibility and field of view advantages of Unmanned Aerial Vehicles (UAV) to guide the UGV in unknown off-road environments. Firstly, a fast terrain risk mapping method that can be used for both UAV and UGV is developed. This method efficiently combines quadtree and hash table data structure to enable UAV to analyze large scale terrain point cloud in real time. Based on the risk mapping result, a risk-inspired active exploration method is proposed to actively search a safe reference path for the UGV, which introduces terrain risk information into the process of travel point selection. Finally, the reference path is gradually generated and optimized, so that the UGV can safely and smoothly follow the path to the target location. Compared with single UGV exploration system, our approach reduces the overall path risk by 26.8% in simulated experiments, showing that the proposed system can enhance autonomous driving of the UGV and help it effectively avoid high-risk areas in unknown off-road environments.