The robot is detecting non-compliance with social norms, categorizing non-conforming pedestrians into groups, and automatically navigating to the static group with the most individuals in it (a 3-person joke about status quo). this state). The robot encourages non-compliant pedestrians to step aside and maintain a social distance of at least 2 meters by displaying a message on the mounted display. The CS-robot also takes thermal images of the scene and transmits them to acceptable medical/safety personnel. Credit Score: Sathyamoorthy et al, 2021, PLOS ONE, CC-BY 4.0
Surveillance robots can assist in cutting down on disease development and likewise aid in contact tracing.
A whole new technique for downsizing COVID-19 uses a mobile robot that detects people in a crowd who are not following social distancing guidelines, navigates to them, and encourages them to stay aside. Adarsh Jagan Sathyamoorthy of the University of Maryland, School Park, and colleagues presented these findings in the December 1, 2021 open access journal PLOS ONE.
Previous analyzes have demonstrated that staying at least two meters away from other places can cut down on the development of COVID-19. Expertise-based strategies — equivalent to methods using WiFi and Bluetooth — hold the promise of helping to detect and prevent social outcast. However, many such approaches require human involvement or existing infrastructure, so robots have emerged as a viable piece of software to tackle social distancing in crowds.
Now, Sathyamoorthy and colleagues have developed a new method to use autonomous mobile robots for this purpose. The robot can detect violations and navigate to them using its personal Crimson Inex Experience Blue-Depth (RGB-D) digital camera and 2-D LiDAR (Distance Detection and Detecting) sensor. gently), and can also be attached to an existing CCTV system, if accessible. As soon as it reaches a violation, the robot encourages people to step aside through text content that appears to be on a mounted show.
The robot uses a novel system to categorize individuals who have violated social distancing guidelines into completely different groups, prioritize them to keep up whether they’re standing or moving, and then navigate to them. The technique uses a machine learning technique commonly known as Deep Augmentation Research and Frozone, an algorithm pre-developed by several identical researchers to assist robots in crowd navigation.
The researchers tested their technique by asking volunteers to perform prosocial situations, while still standing, walking, or swinging. Their robot is capable of detecting and dealing with many violations that have occurred, and CCTV has enhanced its effectiveness.
In addition, the robot uses a thermal digital camera that can detect potentially febrile individuals, assisting in contact tracing efforts, while also incorporating measures to ensure safety. privacy and de-identification.
Additional analysis is required to validate and refine this technique, which equates to discovering how the presence of a robot affects the habits of people in a crowd.
The authors added: “It takes a great deal of health care and safety personnel to put their health at risk to serve the public during the COVID-19 pandemic. The core goal of our work is to provide them with the tools to safely and effectively serve their communities. ”
