TY - GEN
T1 - Optimization of Warehouse Management Using Drones, Artificial Intelligence and RFID
AU - Stasa, Pavel
AU - Benes, Filip
AU - Svub, Jiri
AU - Holusa, Veroslav
AU - Obrusnikova, Miroslava
AU - Dulovec, Jan
AU - Hollesch, Lukas
AU - Unucka, Jakub
AU - Rhee, Jongtae
AU - Jung, Jin Woo
N1 - Publisher Copyright:
© 2025 IEEE.
PY - 2025
Y1 - 2025
N2 - This poster presents a novel system for inventory automation in large outdoor warehouses using a lightweight drone-mounted UHF RFID reader. The system leverages autonomous aerial platforms equipped with custom-developed RFID readers operating in the 865-868 MHz band, combined with intelligent software modules for tag readability evaluation. The proposed solution addresses major limitations of traditional stationary or handheld readers by enabling efficient scanning from a distance, including hard-to-reach areas. Two prototypes of RFID readers were developed, tested and optimized for use on commercial drones, and validated in real-world environments in both Czech and Korean warehouse facilities. The results confirm the reader’s ability to reliably detect tags at distances up to 28 meters and demonstrate its applicability for regular inventory processes. In addition to the hardware, two supporting methodologies and a software tool were developed for integration into existing information systems. The project was carried out within an international research consortium, where the Korean partners focused on AI-based image processing for drone navigation and risk avoidance. The presented solution contributes to the development of autonomous inventory systems, improves safety, and significantly reduces human effort and inventory time in logistics operations.
AB - This poster presents a novel system for inventory automation in large outdoor warehouses using a lightweight drone-mounted UHF RFID reader. The system leverages autonomous aerial platforms equipped with custom-developed RFID readers operating in the 865-868 MHz band, combined with intelligent software modules for tag readability evaluation. The proposed solution addresses major limitations of traditional stationary or handheld readers by enabling efficient scanning from a distance, including hard-to-reach areas. Two prototypes of RFID readers were developed, tested and optimized for use on commercial drones, and validated in real-world environments in both Czech and Korean warehouse facilities. The results confirm the reader’s ability to reliably detect tags at distances up to 28 meters and demonstrate its applicability for regular inventory processes. In addition to the hardware, two supporting methodologies and a software tool were developed for integration into existing information systems. The project was carried out within an international research consortium, where the Korean partners focused on AI-based image processing for drone navigation and risk avoidance. The presented solution contributes to the development of autonomous inventory systems, improves safety, and significantly reduces human effort and inventory time in logistics operations.
KW - AI integration
KW - RFID
KW - UHF reader
KW - autonomous logistics
KW - drone inventory
KW - warehouse automation
UR - https://www.scopus.com/pages/publications/105026334405
U2 - 10.1109/MASS66014.2025.00077
DO - 10.1109/MASS66014.2025.00077
M3 - Conference contribution
AN - SCOPUS:105026334405
T3 - Proceedings - 2025 IEEE 22nd International Conference on Mobile Ad-Hoc and Smart Systems, MASS 2025
SP - 502
EP - 503
BT - Proceedings - 2025 IEEE 22nd International Conference on Mobile Ad-Hoc and Smart Systems, MASS 2025
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 22nd IEEE International Conference on Mobile Ad-Hoc and Smart Systems, MASS 2025
Y2 - 6 October 2025 through 8 October 2025
ER -