Discrete Search Leading Continuous Exploration for Kinodynamic Motion Planning

This paper presents the Discrete Search Leading continuous eXploration (DSLX) planner, a multi-resolution approach to motion planning that is suitable for challenging problems involving robots with kinodynamic constraints. Initially the method decomposes the workspace to build a graph that encodes the physical adjacency of the decomposed regions. This graph is searched to obtain leads, that is, sequences of regions that can be explored with sampling-based tree methods to generate solution trajectories. Instead of treating the discrete search of the adjacency graph and the exploration of the continuous state space as separate components, DSLX passes information from one to the other in innovative ways. Each lead suggests what regions to explore and the exploration feeds back information to the discrete search to improve the quality of future leads. Information is encoded in edge weights, which indicate the importance of including the regions associated with an edge in the next exploration step. Computation of weights, leads, and the actual exploration make the core loop of the algorithm. Extensive experimentation shows that DSLX is very versatile. The discrete search can drastically change the lead to reflect new information allowing DSLX to find solutions even when sampling-based tree planners get stuck. Experimental results on a variety of challenging kinodynamic motion planning problems show computational speedups of two orders of magnitude over other widely used motion planning methods

Improving the performance of sampling-based planners by using a symmetry-exploiting gap reduction algorithm

Abstract — Although sampling-based planning algorithms have been extensively used to approximately solve motion planning problems with differential constraints, gaps usually appear in their solution trajectories due to various factors. Higher precision may be requested, but as we show in this paper, this dramatically increases the computational cost. In practice, this could mean that a solution will not be found in a reasonable amount of time. In this paper, we substantially improve the performance of an RRT-based algorithm by planning low precision solutions, and then refining their quality by employing a recent gap reduction technique that exploits group symmetries of the system to avoid costly numerical integrations. This technique also allows PRMs to be extended to problems with differential constraints, even when no high-quality steering method exists. I