Human-robot interaction (HRI) has become a crucial enabler in houses and industries for facilitating operational flexibility. When it comes to mobile collaborative robots, this flexibility can be further increased due to the autonomous mobility and navigation capacity of the robotic agents, expanding their workspace and consequently, the personalizable assistance they can provide to the human operators. This however requires that the robot is capable of detecting and identifying the human counterpart in all stages of the collaborative task, and in particular while following a human in crowded workplaces. To respond to this need, we developed a unified perception and navigation framework, which enables the robot to identify and follow a target person using a combination of visual Re-Identification (Re-ID), hand gestures detection, and collision-free navigation. The Re-ID module can autonomously learn the features of a target person and use the acquired knowledge to visually re-identify the target. The navigation stack is used to follow the target avoiding obstacles and other individuals in the environment. Experiments are conducted with few subjects in a laboratory setting where some unknown dynamic obstacles are introduced.

翻译：人机交互已成为提升家庭和工业场景操作灵活性的关键手段。对于移动协作机器人而言，其自主移动与导航能力可进一步扩展机器人工作空间，从而增强为操作人员提供的个性化辅助。然而，这要求机器人在协作任务的所有阶段（特别是拥挤工作场所中跟随人员时）具备检测与识别人类对象的能力。为应对这一需求，我们开发了统一的感知与导航框架，该框架通过结合视觉重识别（Re-ID）、手势检测及无碰撞导航技术，使机器人能够识别并跟随目标人员。其中，重识别模块可自主学习目标人员的特征，并利用获取的知识进行视觉重识别；导航堆栈则用于在跟随目标时规避环境中障碍物及其他个体。我们在实验室环境中对少数受试者进行了实验，并引入了未知动态障碍物。