ヒューマノイドロボットへのサンプリングベース運動計画の拡張
Scaling Sampling-based Motion Planning to Humanoid Robots
カスタム状態空間表現とバイアス付きサンプリング、ロバスト逆運動学に基づくステアリング関数を用いて、既存のサンプリングベース計画法を38自由度ヒューマノイド(NASA Valkyrie)の到達運動に適用可能にした研究。
著者: Yiming Yang, Vladimir Ivan, Wolfgang Merkt, Sethu Vijayakumar
分類: cs.RO
原文アブストラクト
Planning balanced and collision-free motion for humanoid robots is non-trivial, especially when they are operated in complex environments, such as reaching targets behind obstacles or through narrow passages. We propose a method that allows us to apply existing sampling--based algorithms to plan trajectories for humanoids by utilizing a customized state space representation, biased sampling strategies, and a steering function based on a robust inverse kinematics solver. Our approach requires no prior offline computation, thus one can easily transfer the work to new robot platforms. We tested the proposed method solving practical reaching tasks on a 38 degrees-of-freedom humanoid robot, NASA Valkyrie, showing that our method is able to generate valid motion plans that can be executed on advanced full-size humanoid robots. We also present a benchmark between different motion planning algorithms evaluated on a variety of reaching motion problems. This allows us to find suitable algorithms for solving humanoid motion planning problems, and to identify the limitations of these algorithms.
