iDRM: Humanoid Motion Planning with Real-Time End-Pose Selection in Complex Environments

TL;DR

This paper introduces iDRM, a real-time inverse reachability map for humanoid robots that efficiently finds collision-free end-poses in complex, changing environments, improving motion planning effectiveness.

Contribution

The paper presents a novel iDRM approach that enables real-time, collision-free end-pose selection for humanoids in complex environments, enhancing planning efficiency.

Findings

iDRM finds valid end-poses in under a second

Motion planning with iDRM is more efficient

Demonstrated on Valkyrie robot with 38 DoF

Abstract

In this paper, we propose a novel inverse Dynamic Reachability Map (iDRM) that allows a floating base system to find valid end-poses in complex and dynamically changing environments in real-time. End-pose planning for valid stance pose and collision-free configuration is an essential problem for humanoid applications, such as providing goal states for walking and motion planners. However, this is non-trivial in complex environments, where standing locations and reaching postures are restricted by obstacles. Our proposed iDRM customizes the robot-to-workspace occupation list and uses an online update algorithm to enable efficient reconstruction of the reachability map to guarantee that the selected end-poses are always collision-free. The iDRM was evaluated in a variety of reaching tasks using the 38 degree-of-freedom (DoF) humanoid robot Valkyrie. Our results show that the approach is capable of finding valid end-poses in a fraction of a second. Significantly, we also demonstrate that motion planning algorithms integrating our end-pose planning method are more efficient than those not utilizing this technique.