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.

翻译：在这项工作中,我们介绍了关于人类-群温互动(HSI)新颖方法的初步工作,这种方法可用于改变分散感应和控制的机器人群的宏观行为。通过将一个小而但又有能力的手势识别神经共振网络(CNN)与下一代机器人操作系统(emph{ros2})相结合,使多机器人应用的机器人软件得以分散应用,我们展示了编程大批机器人以识别和响应一系列能够与不同类型群温行为相对应的手势的可行性。我们用一系列手势测试了我们的方法,这些手势将改变三组Turtlebot3 Burger机器人的目标间机器人距离,以防止机器人与障碍发生碰撞。这种方法在三种不同的Gazebo模拟环境中和再现一个模拟环境的物理试验室中得到验证。