連結性最大化のためのネットワーク配置学習
Learning Connectivity-Maximizing Network Configurations
CNNを用いた教師あり学習で、最適化ベースの専門家からロボット群の通信連結性を最大化する配置を学び、10〜20台規模で従来の一桁速い生成を実現した。
著者: Daniel Mox, Vijay Kumar, Alejandro Ribeiro
分類: cs.RO, cs.LG, cs.MA, cs.NI
原文アブストラクト
In this letter we propose a data-driven approach to optimizing the algebraic connectivity of a team of robots. While a considerable amount of research has been devoted to this problem, we lack a method that scales in a manner suitable for online applications for more than a handful of agents. To that end, we propose a supervised learning approach with a convolutional neural network (CNN) that learns to place communication agents from an expert that uses an optimization-based strategy. We demonstrate the performance of our CNN on canonical line and ring topologies, 105k randomly generated test cases, and larger teams not seen during training. We also show how our system can be applied to dynamic robot teams through a Unity-based simulation. After training, our system produces connected configurations over an order of magnitude faster than the optimization-based scheme for teams of 10-20 agents.
