Temporally-Sampled Efficiently Adaptive State Lattices for Autonomous Ground Robot Navigation in Partially Observed Environments

Due to sensor limitations, environments that off-road mobile robots operate in are often only partially observable. As the robots move throughout the environment and towards their goal, the optimal route is continuously revised as the sensors perceive new information. In traditional autonomous navigation architectures, a regional motion planner will consume the environment map and output a trajectory for the local motion planner to use as a reference. Due to the continuous revision of the regional plan guidance as a result of changing map information, the reference trajectories which are passed down to the local planner can differ significantly across sequential planning cycles. This rapidly changing guidance can result in unsafe navigation behavior, often requiring manual safety interventions during autonomous traversals in off-road environments. To remedy this problem, we propose Temporally-Sampled Efficiently Adaptive State Lattices (TSEASL), which is a regional planner arbitration architecture that considers updated and optimized versions of previously generated trajectories against the currently generated trajectory. When tested on a Clearpath Robotics Warthog Unmanned Ground Vehicle as well as real map data collected from the Warthog, results indicate that when running TSEASL, the robot did not require manual interventions in the same locations where the robot was running the baseline planner. Additionally, higher levels of planner stability were recorded with TSEASL over the baseline. The paper concludes with a discussion of further improvements to TSEASL in order to make it more generalizable to various off-road autonomy scenarios.

Terrain-Aware Kinodynamic Planning with Efficiently Adaptive State Lattices for Mobile Robot Navigation in Off-Road Environments

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.