Unmanned Vehicle Path Planning for Unknown Off-road Environments with Sparse Waypoints

Unmanned vehicles in urban structured environments have become the main battlefields of related companies all over the world. By relying on high-precision maps established in advance, unmanned vehicles can perform well enough. However, this method is difficult to apply to unknown off-road environment which cannot obtain high-precision maps in advance, such as transportation and target search of dangerous scenes in the wild. In this paper, we introduce a novel and efficient real-time path planning method for autonomous driving in unknown environments with sparse waypoints. The contributions of this paper are as follows. First, we present a goal point extraction algorithm to extract the goal configuration in free space for local path planning. It is extremely suitable for the sparse waypoints situation, in which local perception map may not contain any waypoints. Second, we present a novel path re-planning algorithm for large-scale environment without SLAM(Simultaneous Localization And Mapping) by building a topology map constantly. Our method not only overcame the failure of path planning due to the limited on-board sensing range, but also avoided high computing resource consumption brought by the large-scale construction maintenance of SLAM. A large number of challenging real off-road scenarios show that the proposed method can effectively solve the problem that unmanned vehicles cannot plan to travel due to sparse waypoints, and give real-time effective planning results when unmanned vehicles need to carry out large-scale path re-planning.