Differentially constrained path planning with graduated state space

Complex robotic systems often have to operate in large environments. At the same time, their dynamic is complex enough that path planning algorithms need to reason about the differential constraints of these systems. On the other hand, such robotic systems are typically expected to operate with speed that is commensurate with that of humans. This poses stringent limitation on available planning time. A common approach is to use a two-dimensional(2-D) global planner for long range planning, and a short range higher dimensional planner or controller capable of satisfying all of the constraints on motion. However, this approach is incomplete and can result in oscillations or even the inability to find a path to the goal. In this paper, we present an approach to solve this problem by combining the global and local path planning problem into a single search scheme using a combined 2-D and higher dimensional state space. The proposed approach is demonstrated and validated in simulation and experiment with a significantly asymmetric differential drive robot.