ポッシビリティグラフによるヒューマノイドロボットの環境踏破
Traversing Environments Using Possibility Graphs for Humanoid Robots
歩行・這い・跳躍など複数の移動様式を組み合わせて複雑な環境を踏破するため、制約多様体の高レベル近似で行動の可能性を高速探索するポッシビリティグラフを提案した。
著者: Michael X. Grey, Aaron D. Ames, C. Karen Liu
分類: cs.RO, cs.AI
原文アブストラクト
Locomotion for legged robots poses considerable challenges when confronted by obstacles and adverse environments. Footstep planners are typically only designed for one mode of locomotion, but traversing unfavorable environments may require several forms of locomotion to be sequenced together, such as walking, crawling, and jumping. Multi-modal motion planners can be used to address some of these problems, but existing implementations tend to be time-consuming and are limited to quasi-static actions. This paper presents a motion planning method to traverse complex environments using multiple categories of actions. We introduce the concept of the "Possibility Graph", which uses high-level approximations of constraint manifolds to rapidly explore the "possibility" of actions, thereby allowing lower-level single-action motion planners to be utilized more efficiently. We show that the Possibility Graph can quickly find paths through several different challenging environments which require various combinations of actions in order to traverse.
