iDRM: 複雑環境でのリアルタイム終端姿勢選択を伴うヒューマノイド動作計画
iDRM: Humanoid Motion Planning with Real-Time End-Pose Selection in Complex Environments
逆動的到達可能性マップ(iDRM)を提案し、複雑で動的に変化する環境において浮遊ベース系が衝突のない有効な終端姿勢をリアルタイムで選択できるようにした。38自由度ヒューマノイドValkyrieでの評価により、瞬時に有効姿勢を発見でき、動作計画の効率も向上することを示した。
著者: Yiming Yang, Vladimir Ivan, Zhibin Li, Maurice Fallon, Sethu Vijayakumar
分類: cs.RO
原文アブストラクト
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.
