Patient–Robot Co-Navigation of Crowded Hospital Environments
Patient–Robot Co-Navigation of Crowded Hospital Environments
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DOI：
10.3390/app13074576
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发表时间：
2023-04
期刊：
影响因子：
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通讯作者：
K. Kodur;Maria Kyrarini
中科院分区：
文献类型：
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作者：
K. Kodur;Maria Kyrarini
智能多用途机器人助手有潜力协助护士完成各种非关键任务，如取物、消毒区域或支持患者护理。本文的重点是实现一种多用途机器人，用于指导患者行走。提出的机器人框架旨在使机器人能够学习如何在拥挤的医院环境中导航，同时保持与患者的接触。建立了两种深度强化学习模型；第一个模型只考虑动态障碍（例如，人类），而第二个模型考虑环境中的静态和动态障碍。模型根据以下输入输出机器人的速度；病人的步态速度，这是基于腿部检测方法计算的，来自环境、场景中的人类和机器人的时空信息。所提出的模型显示出令人满意的结果。最后，在Gazebo仿真环境中成功地部署了考虑静态和动态障碍物的模型。
Intelligent multi-purpose robotic assistants have the potential to assist nurses with a variety of non-critical tasks, such as object fetching, disinfecting areas, or supporting patient care. This paper focuses on enabling a multi-purpose robot to guide patients while walking. The proposed robotic framework aims at enabling a robot to learn how to navigate a crowded hospital environment while maintaining contact with the patient. Two deep reinforcement learning models are developed; the first model considers only dynamic obstacles (e.g., humans), while the second model considers static and dynamic obstacles in the environment. The models output the robot’s velocity based on the following inputs; the patient’s gait velocity, which is computed based on a leg detection method, spatial and temporal information from the environment, the humans in the scene, and the robot. The proposed models demonstrate promising results. Finally, the model that considers both static and dynamic obstacles is successfully deployed in the Gazebo simulation environment.