A Multiresolution Approach for Real-Time Motion Planning under Differential Constraints

In this paper we present a complex approach to resolve a contradiction in planning between space discretization based algorithms and sampling RRT-like algorithms in a way, appropriate for a real time motion planning with limited computation resources. The former type planning algorithms have advantage that they can systematically explore discretized space, and are less vulnerable to the problem of navigation in narrow passages, but from the other side, to achieve satisfactory results in terms of path smoothness and traversability, higher resolution and additional dimensions in configuration space might be required, which makes use of such algorithms problematic for real time motion planning, because of curse of dimensionality. RRT-like sampling based algorithms have advantage, that they can explore fast in high dimensional configuration space, but they may spent indefinitely long time if they encounter narrow passages. For the safe motion it is desired that motion planner gives response to the plan execution part in a limited time. The proposed solution implements this feature in a more efficient way than existing solutions. The proposed solution was tested in a real autonomous driving, including parking with backward motion.