空間グラフニューラルネットワークを用いたマルチロボットの被覆と探索
Multi-Robot Coverage and Exploration using Spatial Graph Neural Networks
被覆問題を空間グラフとして離散化し、グラフニューラルネットワークで専門家の経路計画を模倣する制御器を学習。大規模な地図や多数のロボットへ汎化でき、探索タスクでは計画ベース手法を上回ることを示した。
著者: Ekaterina Tolstaya, James Paulos, Vijay Kumar, Alejandro Ribeiro
分類: cs.RO
原文アブストラクト
The multi-robot coverage problem is an essential building block for systems that perform tasks like inspection or search and rescue. We discretize the coverage problem to induce a spatial graph of locations and represent robots as nodes in the graph. Then, we train a Graph Neural Network controller that leverages the spatial equivariance of the task to imitate an expert open-loop routing solution. This approach generalizes well to much larger maps and larger teams that are intractable for the expert. In particular, the model generalizes effectively to a simulation of ten quadrotors and dozens of buildings. We also demonstrate the GNN controller can surpass planning-based approaches in an exploration task.
