将室内环境与健康协议监控系统集成到智能机器人中,促进大学校园安全

Analene Montesines Nagayo, S. Sangeetha, Mahmood Zayid K. Al Ajmi, Abdullah Yousuf M. Al Bulushi, Mohammed Said A. Al Hinaai, Loay Yahia T. Al Hamadani
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摘要

本文讨论了在大学校园中用于监测室内环境、健康协议和卫生的智能机器人系统的设计和部署。设计的VEX自主机器人系统执行以下任务:(a)在大学教室周围移动,扫描学生和教职员工的体温,并跟踪教室内的环境参数;(b)对教室内的物品进行消毒,履行卫生职能;及(c)履行保安功能,如有发烧的学生或教职员进入教室,或教职员或学生在室内未戴口罩,便会向卫生及安全主任发出警报信号。粒子光子微控制器连接到传感器和执行器,用于检测和管理室内环境条件,并从远处跟踪个人的体温,数据存储在ThingSpeak和Particle云平台中,并显示在智能手机应用程序上。通过MIT App Inventor的个人图像分类器进行迁移学习,检测健康协议违规行为,准确率为93.33%。机器人原型的最大穿越距离为38米,平均时间为220秒,平均速度为0.17米/秒。机器人沿着黑线路线的成功率为88.89%。这种智能机器人系统可以限制教职员工和学生接触传染病,并在校园实施“新常态”健康和安全措施,作为covid -19后的预防措施。
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Indoor Environment and Health Protocol Monitoring and Control System Integrated into a Smart Robot to Promote Safety on University Campuses
This article discusses about the design and deployment of a smart robotic system on university campuses for monitoring the indoor environment, health protocols, and sanitation. The designed VEX autonomous robotic system performed the following tasks: (a) moving around the university classrooms and scanning the body temperature of students and staff, as well as tracking environmental parameters in classrooms; (b) executing sanitation function by disinfecting objects in classrooms; and (c) performing security function by sending an alert signal to health and safety officer if a student or staff with fever enters the classroom, or if staff or student is not wearing face mask indoors. Particle Photon microcontrollers linked to sensors and actuators were used to detect and manage indoor environmental conditions as well as track individuals' body temperatures from a distance, with the data being stored in the ThingSpeak and Particle cloud platforms and displayed on smartphone apps. Transfer learning through MIT App Inventor's Personal Image Classifier was used to detect health protocol violations with 93.33% accuracy. The maximum distance traversed by the robot prototype was 38 meters, with an average time of 220 seconds and an average speed of 0.17 meters per second. The robot had an 88.89% success rate in following the black-lined course. This intelligent robotic system can limit staff and student exposure to infectious diseases and implement "new normal" health and safety practices on campus as post-COVID-19 precautions.
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