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Cooperative AI Infrastructure Can Provide Surveillance and Epidemic Services



A team of scientists at Incheon National University in South Korea has designed a cooperative infrastructure for artificial intelligence (AI)-assisted aerial and ground operations by using UAVs and mobile robots. This type of AI infrastructure could be used to provide surveillance and epidemic prevention services and activities to smart cities. 

Experts have been increasingly looking at the potential of mobile robots and unmanned aerial vehicles, or UAVs. These technologies can provide many benefits, and the rise of 5G technology is expected to enable UAVs, drones, and mobile robots to efficiently and safely provide various services throughout smart cities. Some of these services could include surveillance and epidemic prevention, where robots could be deployed in different environments to perform activities like rescue operations. 

Realizing Full Potential of UAVs and Mobile Robots

As of now, these types of operations are independent from each other. According to the team, the technologies must be used together to realize the full potential of UAVs and mobile robots as they can support each other and augment mutual functions. 

The team of researchers that designed that AI-assisted cooperative infrastructure was led by Associate Professor Hyunbum Kim from Incheon National University. 

The paper was published in IEEE Network

The team outlines the entire structure that can use UAVs and mobile robots in public and private areas for multiple services, including patrolling, epidemic prevention, and accident detection and rescue. 

“It is critical to look at surveillance and unprecedented epidemic spread such as COVID-19 together. This is why we designed the next generation system to focus on aerial-ground surveillance and epidemic prevention supported by intelligent mobile robots and smart UAVs,” Dr. Kim said. 

New System Design

The newly designed system consists of two subsystems. One is for public areas while the other is for private areas. Both of the systems have a Centralized Administrator Center (CAC), which is connected to various Unified Rendezvous Stations (URS) that are located in public areas. 

The URSs enable the UAVs and mobile robots to receive replenishment and share data, and the mobile robots also have charging facilities to recharge airborne docking UAVs. While the public system focuses on patrolling public areas, detecting accidents, providing aid, and performing epidemic prevention activities, the private system can provide rapid medical deliveries and at-home screening tests. 

When it comes to privacy, Dr. Kim notes that it is a major concern. 

“Privacy is indeed a major concern for any surveillance mechanism,” Dr. Kim says. “Therefore, we have created different privacy settings for different systems. For the public system, there are restricted districts where only authorized public UAVs can enter. For the private system, there are permanent private zones where no UAVs can enter except in emergencies and temporal access zones where permitted UAVs can enter with legal permission from the owners.” 


Alex McFarland is a tech writer who covers the latest developments in artificial intelligence. He has worked with AI startups and publications across the globe.