Harmonious Sampling for Mobile Manipulation Planning 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 suboptimal paths. Another direction is a coupled approach jointly adjusting the base and manipulator in a highdimensional configuration space. This coupled approach addresses suboptimality and incompleteness of the decoupled approach, but has not been widely used due to its excessive computational overhead. Given this tradeoff 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 lowdimensional base space by utilizing a reachability map given the target endeffector 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.

Samplingbased motion planning of 3D solid objects guided by multiple approximate solutions Samplingbased motion planners are often used to solve motion planning problems for robots with many degrees of freedom. These planners explore the related configuration space by random sampling. The wellknown issue of the samplingbased planners is the narrow passage problem. Narrow passages are small collisionfree regions in the configuration space that are, due to their volume, difficult to cover by the random samples. The volume of the narrow passages can be artificially increased by reducing the size of the robot, e.g., by scalingdown its geometry, which increases the probability of placing the random samples into the narrow passages. This allows us to find an approximate solution (trajectory) and use it as a guide to find the solution for a larger robot. Guiding along an approximate solution may, however, fail if this solution leads through such parts of the configuration space that are not reachable or traversable by a larger robot. To improve this guiding process, we propose to compute several approximate solutions leading through different parts of the configuration space, and use all of them to guide the search for a larger robot. We introduce the concept of disabled regions that are prohibited from the exploration using the sampling process. The disabled regions are defined using trajectories already found in the space being searched. The proposed method can solve planning problems with narrow passages with higher success rate than other stateoftheart planners.