Harmonious Sampling for Mobile Manipulation Planning

TL;DR

This paper introduces harmonious sampling, a novel approach for mobile manipulation planning that efficiently balances coupled and decoupled strategies, significantly improving planning speed and solution quality in complex environments.

Contribution

It proposes a simple, effective sampling method that selectively performs coupled planning in difficult regions, enhancing efficiency and solution optimality over traditional methods.

Findings

Up to 5.6 times faster initial solution discovery.

Up to 17% lower final solution cost.

Significant improvements in complex, narrow environments.

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.