This figure shows a sequence of the mobile Hubo robot grasping
the yellow beverage. The heat map on the bottom right represents a sample
density in terms of the 2D floor projected from samples generated from our
harmonious sampling in the joint C-space consisting of the base and the
manipulator. Samples are distributed intensively near the goal configuration,
resulting in efficient and effective exploration of solution paths.
Abstract
Mobile manipulation planning commonly adopts
a decoupled approach that performs planning separately on
the base and the manipulator. While this approach is fast, it
can generate sub-optimal paths. Another direction is a coupled
approach jointly adjusting the base and manipulator in a
high-dimensional configuration space. This coupled approach
addresses sub-optimality and incompleteness of the decoupled
approach, but has not been widely used due to its excessive
computational overhead. Given this trade-off space, we present
a simple, yet effective mobile manipulation sampling method,
harmonious sampling, to perform the coupled approach mainly
in difficult regions, where we need to simultaneously maneuver
the base and the manipulator. Our method identifies such
difficult regions through a low-dimensional base space by
utilizing a reachability map given the target end-effector pose
and narrow passage detected by generalized Voronoi diagram.
For the rest of simple regions, we perform sampling mainly on
the base configurations with a predefined joint configuration,
accelerating the planning process. We compare our method
with the decoupled and coupled approaches in six different
problems with varying difficulty. Our method shows meaningful
improvements experimentally in terms of time to find an initial
solution (up to 5.6 times faster) and final solution cost (up to
17% lower) over the decoupled approach, especially in difficult
scenes with narrow space. We also demonstrate these benefits
with a real, mobile Hubo robot.
