連続ハンドジェスチャ認識による分散型ヒューマン・スワーム・インタラクション
Towards Decentralized Human-Swarm Interaction by Means of Sequential Hand Gesture Recognition
小型のハンドジェスチャ認識CNNとROS2を組み合わせ、ロボット群がジェスチャの連続入力に応じて群れの振る舞いを変える分散型ヒューマン・スワーム・インタラクション手法を提案し、3台のTurtlebot3で衝突回避のための目標間隔変更を実機・シミュレーションで検証した。
著者: Zahi Kakish, Sritanay Vedartham, Spring Berman
分類: cs.RO, cs.MA
原文アブストラクト
In this work, we present preliminary work on a novel method for Human-Swarm Interaction (HSI) that can be used to change the macroscopic behavior of a swarm of robots with decentralized sensing and control. By integrating a small yet capable hand gesture recognition convolutional neural network (CNN) with the next-generation Robot Operating System \emph{ros2}, which enables decentralized implementation of robot software for multi-robot applications, we demonstrate the feasibility of programming a swarm of robots to recognize and respond to a sequence of hand gestures that capable of correspond to different types of swarm behaviors. We test our approach using a sequence of gestures that modifies the target inter-robot distance in a group of three Turtlebot3 Burger robots in order to prevent robot collisions with obstacles. The approach is validated in three different Gazebo simulation environments and in a physical testbed that reproduces one of the simulated environments.
