让机器人在复杂地形中更安全高效地规划路径。

To safely traverse non-flat terrain, robots must account for the influence of terrain shape in their planned motions. Terrain-aware motion planners use an estimate of the vehicle roll and pitch as a function of pose, vehicle suspension, and ground elevation map to weigh the cost of edges in the search space. Encoding such information in a traditional two-dimensional cost map is limiting because it is unable to capture the influence of orientation on the roll and pitch estimates from sloped terrain. The research presented herein addresses this problem by encoding kinodynamic information in the edges of a recombinant motion planning search space based on the Efficiently Adaptive State Lattice (EASL). This approach, which we describe as a Kinodynamic Efficiently Adaptive State Lattice (KEASL), differs from the prior representation in two ways. First, this method uses a novel encoding of velocity and acceleration constraints and vehicle direction at expanded nodes in the motion planning graph. Second, this approach describes additional steps for evaluating the roll, pitch, constraints, and velocities associated with poses along each edge during search in a manner that still enables the graph to remain recombinant. Velocities are computed using an iterative bidirectional method using Eulerian integration that more accurately estimates the duration of edges that are subject to terrain-dependent velocity limits. Real-world experiments on a Clearpath Robotics Warthog Unmanned Ground Vehicle were performed in a non-flat, unstructured environment. Results from 2093 planning queries from these experiments showed that KEASL provided a more efficient route than EASL in 83.72% of cases when EASL plans were adjusted to satisfy terrain-dependent velocity constraints. An analysis of relative runtimes and differences between planned routes is additionally presented.