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Automation
Special Issues
Inventory Monitoring and Control Through High-Level Coordination of Drone Swarms
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Inventory Monitoring and Control Through High-Level Coordination of Drone Swarms

A special issue of Automation (ISSN 2673-4052).

Deadline for manuscript submissions: 31 March 2026 | Viewed by 1056

Special Issue Editor

Dr. Simon Tomažič

E-Mail Website
Guest Editor
Laboratory of Control Systems and Cybernetics, University of Ljubljana, 1000 Ljubljana, Slovenia
Interests: soft sensors; Raman spectroscopy; fuzzy model identification; machine learning with big data; predictive control of dynamic systems; sensor fusion; data mining; indoor positioning; autonomous mobile systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleague,

The Special Issue titled "Inventory Monitoring and Control through High-Level Coordination of Drone Swarms" aims to explore advanced methodologies and technologies for leveraging drone swarms in process control and automation. This issue focuses on innovative solutions for inventory monitoring in warehouses, both indoors and in open environments, using synchronized drone operations. It emphasizes the integration of advanced predictive control algorithms to optimize swarm behavior, enabling precise path planning and efficient task allocation. Key topics include environmental mapping using camera and LIDAR systems, real-time anomaly detection, and adaptive scheduling of time-sensitive aerial surveys for monitoring dynamic changes, such as environmental shifts or stock fluctuations. Additionally, this issue invites contributions on cooperative localization strategies for enhanced positional accuracy, energy-efficient mission planning for extended operation, and secure communication protocols for real-time data exchange among drones. Further, studies on swarm resilience, self-healing capabilities, and the integration of AI-driven decision-making processes to improve coordination and adaptability are highly encouraged. The issue aims to provide a comprehensive overview of the state of the art in drone swarm applications, offering solutions for industries ranging from logistics and agriculture to environmental monitoring and disaster management.

Dr. Simon Tomažič
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Automation is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • drone swarms
  • inventory monitoring
  • predictive control algorithms
  • path planning
  • environmental mapping
  • anomaly detection
  • cooperative localization
  • energy-efficient mission planning
  • secure communication protocols
  • AI-driven decision-making

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Published Papers (2 papers)

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13 pages, 2245 KB  
Article
Swarm Drones with QR Code Formation for Real-Time Vehicle Detection and Fusion Using Unreal Engine
by Alaa H. Ahmed and Henrietta Tomán
Automation 2025, 6(4), 87; https://doi.org/10.3390/automation6040087 (registering DOI) - 3 Dec 2025
Viewed by 121
Abstract
A single drone collects data, but a fleet builds a complete picture, and this is the primary objective of this study. To address this goal, a swarm-based drone system has been designed in which multiple drones follow one another to collect data from [...] Read more.
A single drone collects data, but a fleet builds a complete picture, and this is the primary objective of this study. To address this goal, a swarm-based drone system has been designed in which multiple drones follow one another to collect data from diverse perspectives. Such a strategy demonstrates strong potential for use in critical fields such as search and rescue operations. This study introduces the first unified framework that integrates autonomous formation control, real-time object detection, and multi-source data fusion within a single operational UAV-swarm system. A high-fidelity simulation environment was built using Unreal Engine with the AirSim plugin, featuring a lightweight QR code tracking algorithm for inter-drone coordination. The drones were employed to detect vehicles from various angles in real time. Two types of experiments were conducted: the first used a pretrained YOLO model, and the second used a custom-trained YOLOv8-nano model, which outperformed the baseline by achieving an average detection confidence of 90%. Finally, the results from multiple drones were fused using various techniques including temporal, probabilistic, and geometric fusion methods to produce more reliable and robust detection results. Full article
(This article belongs to the Special Issue Inventory Monitoring and Control Through High-Level Coordination of Drone Swarms)
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21 pages, 15262 KB  
Article
An Air-to-Ground Visual Target Persistent Tracking Framework for Swarm Drones
by Yong Xu, Shuai Guo, Hongtao Yan, An Wang, Yue Ma, Tian Yao and Hongchuan Song
Automation 2025, 6(4), 81; https://doi.org/10.3390/automation6040081 - 2 Dec 2025
Viewed by 172
Abstract
Air-to-ground visual target persistent tracking technology for swarm drones, as a crucial interdisciplinary research area integrating computer vision, autonomous systems, and swarm collaboration, has gained increasing prominence in anti-terrorism operations, disaster relief, and other emergency response applications. While recent advancements have predominantly concentrated [...] Read more.
Air-to-ground visual target persistent tracking technology for swarm drones, as a crucial interdisciplinary research area integrating computer vision, autonomous systems, and swarm collaboration, has gained increasing prominence in anti-terrorism operations, disaster relief, and other emergency response applications. While recent advancements have predominantly concentrated on improving long-term visual tracking through image algorithmic optimizations, insufficient exploration has been conducted on developing system-level persistent tracking architectures, leading to a high target loss rate and limited tracking endurance in complex scenarios. This paper designs an asynchronous multi-task parallel architecture for drone-based long-term tracking in air-to-ground scenarios, and improves the persistent tracking capability from three levels. At the image algorithm level, a long-term tracking system is constructed by integrating existing object detection YOLOv10, multi-object tracking DeepSort, and single-object tracking ECO algorithms. By leveraging their complementary strengths, the system enhances the performance of the detection and multi-object tracking while mitigating model drift in single-object tracking. At the drone system level, ground target absolute localization and geolocation-based drone spiral tracking strategies are conducted to improve target reacquisition rates after tracking loss. At the swarm collaboration level, an autonomous task allocation algorithm and relay tracking handover protocol are proposed, further enhancing the long-term tracking capability of swarm drones while boosting their autonomy. Finally, a practical swarm drone system for persistent air-to-ground visual tracking is developed and validated through extensive flight experiments under diverse scenarios. Results demonstrate the feasibility and robustness of the proposed persistent tracking framework and its adaptability to wild real-world applications. Full article
(This article belongs to the Special Issue Inventory Monitoring and Control Through High-Level Coordination of Drone Swarms)
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