ランダム化可能性グラフの確率的完全性：半非構造環境における二足歩行への応用
Probabilistic Completeness of Randomized Possibility Graphs Applied to Bipedal Walking in Semi-unstructured Environments
二足ロボットの準静的歩行計画において、ランダム化可能性グラフ手法が確率的に完全であることを理論的に示し、階層分解により解発見の収束率が向上することをシミュレーションで実証した論文。
著者: Michael X. Grey, Aaron D. Ames, C. Karen Liu
分類: cs.RO
原文アブストラクト
We present a theoretical analysis of a recent whole body motion planning method, the Randomized Possibility Graph, which uses a high-level decomposition of the feasibility constraint manifold in order to rapidly find routes that may lead to a solution. These routes are then examined by lower-level planners to determine feasibility. In this paper, we show that this approach is probabilistically complete for bipedal robots performing quasi-static walking in "semi-unstructured" environments. Furthermore, we show that the decomposition into higher and lower level planners allows for a considerably higher rate of convergence in the probability of finding a solution when one exists. We illustrate this improved convergence with a series of simulated scenarios.
