Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.1
2.0
Journals
Automation
Special Issues
Inventory Monitoring and Control Through High-Level Coordination of Drone Swarms
share announcement

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

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

Deadline for manuscript submissions: closed (31 March 2026) | Viewed by 5214

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 semimonthly 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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

23 pages, 5350 KB  
Article
Target Tracking-Based Online Calibration of UAV Electro-Optical Pod Installation Errors
by Yong Xu, Jin Liu, Hongtao Yan, An Wang, Haihang Xu, Yue Ma and Tian Yao
Automation 2026, 7(2), 59; https://doi.org/10.3390/automation7020059 - 1 Apr 2026
Viewed by 606
Abstract
As the “visual perception hub” of unmanned aerial vehicles (UAVs), electro-optical (EO) pods play an increasingly critical role in tasks such as intelligence gathering, situational awareness, target tracking, and localization. With the expanding scope and depth of UAV applications, higher demands are placed [...] Read more.
As the “visual perception hub” of unmanned aerial vehicles (UAVs), electro-optical (EO) pods play an increasingly critical role in tasks such as intelligence gathering, situational awareness, target tracking, and localization. With the expanding scope and depth of UAV applications, higher demands are placed on the precision and adaptability of installation error calibration techniques for EO pods. Current mainstream calibration methods typically require specialized procedures under constrained conditions, while few approaches integrate existing UAV system capabilities and mission requirements, which leads to cumbersome, time-consuming processes and suboptimal alignment between calibration outcomes and task objectives. This paper proposes an online calibration method for UAV EO pod installation errors based on target tracking, which can rapidly compute the optimal closed-form solution for installation errors by leveraging UAV tracking missions. First, an observation equation for pod installation errors is established using tracking results. Second, multi-temporal observations are combined to model the calibration problem as an optimal rotation matrix estimation task, and then the optimal closed-form solution for installation errors is derived. Concurrently, a statistics-based approximate calibration method is introduced specifically for tracking missions. Furthermore, an online calibration system compatible with diverse UAV platforms is designed, along with different rapid calibration schemes for emergency response scenarios, fully incorporating existing system capabilities and mission needs. Finally, a fixed-wing UAV experimental platform is developed, with calibration tests conducted under various flight regimes. Experimental results validate the feasibility and robustness of the proposed methodology. Full article
(This article belongs to the Special Issue Inventory Monitoring and Control Through High-Level Coordination of Drone Swarms)
►▼ Show Figures

Figure 1

16 pages, 5863 KB  
Article
A Rapid Aerial Image Mosaic Method for Multiple Drones Based on Key Frames
by Xiuzhen Wu, Yahui Qi, Liang Qin, Shi Yan and Jianxiu Zhang
Automation 2026, 7(2), 43; https://doi.org/10.3390/automation7020043 - 5 Mar 2026
Viewed by 474
Abstract
Due to their advantages of being low-cost, lightweight and flexible, and having wide shooting coverage, UAVs have played an important role in situational awareness in the fields of disaster prevention and mitigation, urban planning and management, etc. In these applications, UAV aerial photography [...] Read more.
Due to their advantages of being low-cost, lightweight and flexible, and having wide shooting coverage, UAVs have played an important role in situational awareness in the fields of disaster prevention and mitigation, urban planning and management, etc. In these applications, UAV aerial photography is limited by the field of view, and high-definition panoramic images of the complete target area cannot be obtained. Image mosaic technology is essential, but an image mosaic using only a single UAV cannot meet the high real-time requirements for situational awareness. In response to the above problems, this paper proposes a multi-UAV fast aerial image mosaic method based on key frames. First, the multi-UAV area coverage flight strategy is determined according to the size of the task area and the UAV flight parameters; then, the field of view of the pod, the flight speed, and the flight altitude are used to determine the key frame extraction time period during the UAV aerial photography process. The image matching-rate calculation method is designed and the key frames are extracted during the extraction time period, and the key frames are returned to the ground visual puzzle system; in the ground visual puzzle system, the improved Laplacian pyramid method is used to quickly fuse and stitch the key frames extracted by each UAV to obtain a panoramic stitched map. The experiment shows that the method can quickly obtain high-precision real-scene map information of the task area. Compared with the single-UAV method and the multi-UAV full video stream-splicing method, this method greatly reduces the consumption of computing power and the requirements of communication bandwidth and improves the efficiency and real-time performance of panoramic map acquisition. Full article
(This article belongs to the Special Issue Inventory Monitoring and Control Through High-Level Coordination of Drone Swarms)
►▼ Show Figures

Figure 1

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 - 3 Dec 2025
Cited by 1 | Viewed by 1800
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)
►▼ Show Figures

Figure 1

21 pages, 15263 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 1209
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-4
Back to TopTop