# AI-Powered Service Robots for Smart Airport Operations: Real-World Implementation and Performance Analysis in Passenger Flow Management

## TL;DR

AI-powered robots and thermal sensors were tested at Athens International Airport to manage passenger flow efficiently and privately, showing strong performance and user satisfaction.

## Contribution

A real-world implementation of AI-powered service robots with thermal imaging and 5G connectivity for passenger flow management in airports.

## Key findings

- The system achieved ultra-low latency of 42.9 ms and 100% service reliability in real-world trials.
- Thermal imaging provided privacy-compliant crowd analytics and anomaly detection.
- Passenger satisfaction scores exceeded 4.3/5 across all dimensions.

## Abstract

What are the main findings?
An integrated smart airport ecosystem combining privacy-compliant thermal imaging sensors and 5G-connected service robots was successfully validated in real-world trials at Athens International Airport.The system achieved ultra-low application latency of 42.9 ms and 100% service reliability, resulting in consistently positive user satisfaction scores across trust and operational efficiency metrics.

An integrated smart airport ecosystem combining privacy-compliant thermal imaging sensors and 5G-connected service robots was successfully validated in real-world trials at Athens International Airport.

The system achieved ultra-low application latency of 42.9 ms and 100% service reliability, resulting in consistently positive user satisfaction scores across trust and operational efficiency metrics.

What are the implications of the main findings?
Thermal sensor networks provide a highly effective, GDPR-compliant alternative to traditional RGB cameras for granular crowd analytics and anomaly detection in sensitive public spaces.While current 5G infrastructure supports individual service robots, scaling to comprehensive airport-wide multi-robot fleets will require advanced network slicing and edge computing capabilities to maintain critical performance.

Thermal sensor networks provide a highly effective, GDPR-compliant alternative to traditional RGB cameras for granular crowd analytics and anomaly detection in sensitive public spaces.

While current 5G infrastructure supports individual service robots, scaling to comprehensive airport-wide multi-robot fleets will require advanced network slicing and edge computing capabilities to maintain critical performance.

The proliferation of air travel demand necessitates innovative solutions to enhance passenger experience while optimizing airport operational efficiency. This paper presents the pilot-scale implementation and evaluation of an AI-powered service robot ecosystem integrated with thermal cameras and 5G wireless connectivity at Athens International Airport. The system addresses critical challenges in passenger flow management through real-time crowd analytics, congestion detection, and personalized robotic assistance. Eight strategically deployed thermal cameras monitor passenger movements across check-in areas, security zones, and departure entrances while employing privacy-by-design principles through thermal imaging technology that reduces personally identifiable information capture. A humanoid service robot, equipped with Robot Operating System navigation capabilities and natural language processing interfaces, provides real-time passenger assistance including flight information, wayfinding guidance, and congestion avoidance recommendations. The wi.move platform serves as the central intelligence hub, processing video streams through advanced computer vision algorithms to generate actionable insights including passenger count statistics, flow rate analysis, queue length monitoring, and anomaly detection. Formal trial evaluation conducted on 10 April 2025, with extended operational monitoring from April to June 2025, demonstrated strong technical performance with application round-trip latency achieving 42.9 milliseconds, perfect service reliability and availability ratings of one hundred percent, and comprehensive passenger satisfaction scores exceeding 4.3/5 across all evaluated dimensions. Results indicate promising potential for scalable deployment across major international airports, with identified requirements for sixth-generation network capabilities to support enhanced multi-robot coordination and advanced predictive analytics functionalities in future implementations.