Harmonious Sampling for Mobile Manipulation Planning

by Mincheul Kang, Donghyuk Kim, and Sung-Eui Yoon
Submitted to IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019

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.


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.



Paper: PDF (3.97MB)
Source Code: ZIP file, Github page
Slides: PPT (10.6MB)

School of Computing
Daehak-ro 291, Guseong-dong, Yuseong-gu, Daejeon, 305-701
South Korea
mincheul.kang (at) kaist.ac.kr