iDRM: Humanoid motion planning with realtime end-pose selection in complex environments

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.