Motion Planning With Differential Constraints as Guided Search Over Continuous and Discrete Spaces

Abstract

To compute a motion trajectory that avoids collisions, reaches a goal
region, and satisfies differential constraints imposed by robot
dynamics, this paper proposes an approach that conducts a guided
search over the continuous space of motions and over a discrete space
obtained by a workspace decomposition.  A tree of feasible motions and
a frontier of workspace regions are expanded simultaneously by first
determining the next region along which to expand the search and then
using sampling-based motion planning to add trajectories to the tree
to reach the selected region.  When motion planning is not able to
reach the selected region, its cost is increased so
that the approach has the flexibility to expand the search along new
regions.  Comparisons to related work show significant computational
speedups.