分散型地図分類のための局所観測を用いたロボット群制御
Distributed Map Classification using Local Observations
通信可能なロボット群が局所的な視覚情報を交換し、グラフ分解に基づくオフライン学習で効率的に地図を分類する手法を提案した。
著者: Guangyi Liu, Arash Amini, Martin Takáč, Héctor Muñoz-Avila, Nader Motee
分類: cs.RO, cs.LG, cs.MA, cs.SY, eess.SY
原文アブストラクト
We consider the problem of classifying a map using a team of communicating robots. It is assumed that all robots have localized visual sensing capabilities and can exchange their information with neighboring robots. Using a graph decomposition technique, we proposed an offline learning structure that makes every robot capable of communicating with and fusing information from its neighbors to plan its next move towards the most informative parts of the environment for map classification purposes. The main idea is to decompose a given undirected graph into a union of directed star graphs and train robots w.r.t a bounded number of star graphs. This will significantly reduce the computational cost of offline training and makes learning scalable (independent of the number of robots). Our approach is particularly useful for fast map classification in large environments using a large number of communicating robots. We validate the usefulness of our proposed methodology through extensive simulations.
