統計幾何学を用いた形状認識のための倹約的ランダム探索戦略
Frugal random exploration strategy for shape recognition using statistical geometry
向きや観測システムを持たない単純なロボットがランダムに探索するだけで、面積や周長といった環境の大域的情報を取得し、形状認識や文字読み取りが可能であることを統計幾何学の不変特徴量を用いて示した研究。
著者: Samuel Hidalgo-Caballero, Alvaro Cassinelli, Emmanuel Fort, Matthieu Labousse
分類: cs.RO, math-ph, math.MP
原文アブストラクト
Very distinct strategies can be deployed to recognize and characterize an unknown environment or a shape. A recent and promising approach, especially in robotics, is to reduce the complexity of the exploratory units to a minimum. Here, we show that this frugal strategy can be taken to the extreme by exploiting the power of statistical geometry and introducing new invariant features. We show that an elementary robot devoid of any orientation or observation system, exploring randomly, can access global information about an environment such as the values of the explored area and perimeter. The explored shapes are of arbitrary geometry and may even non-connected. From a dictionary, this most simple robot can thus identify various shapes such as famous monuments and even read a text.