連合学習を用いたUAVの近接制御：人とロボットの協調領域に向けて
Proximal Control of UAVs with Federated Learning for Human-Robot Collaborative Domains
複数ドローンにLSTMと連合学習を組み込み、分散学習で人間の動作を認識・制御し、遮蔽を防ぎつつ96%以上の精度を実現した。
著者: Lucas Nogueira Nobrega, Ewerton de Oliveira, Martin Saska, Tiago Nascimento
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
The human-robot interaction (HRI) is a growing area of research. In HRI, complex command (action) classification is still an open problem that usually prevents the real applicability of such a technique. The literature presents some works that use neural networks to detect these actions. However, occlusion is still a major issue in HRI, especially when using uncrewed aerial vehicles (UAVs), since, during the robot's movement, the human operator is often out of the robot's field of view. Furthermore, in multi-robot scenarios, distributed training is also an open problem. In this sense, this work proposes an action recognition and control approach based on Long Short-Term Memory (LSTM) Deep Neural Networks with two layers in association with three densely connected layers and Federated Learning (FL) embedded in multiple drones. The FL enabled our approach to be trained in a distributed fashion, i.e., access to data without the need for cloud or other repositories, which facilitates the multi-robot system's learning. Furthermore, our multi-robot approach results also prevented occlusion situations, with experiments with real robots achieving an accuracy greater than 96%.
