Skip to main content
editor@theusajournals.com | Oscar Publishing Services Journal Home
Login

American Journal of Applied Science and Technology

Peer Reviewed | Open Access | E-ISSN: 2771-2745
Published Article

Optimization of Tactical Operations for UAVS And Ground Robotic Complexes in Engineering Reconnaissance Within Desert Environments

Optimization of Tactical Operations for UAVS And Ground Robotic Complexes in Engineering Reconnaissance Within Desert Environments

Authors

  • Axmedov Nurbek Zufarovich
    Head of Department, University of Military Security and Defense of the Republic of Uzbekistan

DOI:

https://doi.org/10.37547/ajast/Volume06Issue04-22

Keywords:

Engineering reconnaissance robotic systems (UGV) unmanned aerial vehicles (UAV)

Abstract

This paper analyzes the specifics of conducting engineering reconnaissance operations using heterogeneous groups composed of Unmanned Aerial Vehicles (UAVs) and ground robotic complexes (UGVs) in arid landscapes. The research is driven by significant operational barriers faced by isolated autonomous platforms in desert environments: thermal degradation of sensor performance and mobility instability on loose soils, which substantially increase the risk of overlooking explosive hazards.

The study substantiates a search optimization concept based on the synthesis of multi-channel remote sensing data and local UGV sensor detection within a “leader-follower” framework. Through computational modeling, it is demonstrated that such integration reduces the time required for reconnaissance of a designated area by 30%, while simultaneously increasing target identification accuracy by 18% under conditions of thermal haze and high dust concentration. The proposed algorithms can be integrated into the software environment of engineering unit command systems and utilized in the training of specialist operators.

References

Achkar, R. (2024). Artificial Neural Networks for Autonomous Landmine Localization in Arid Regions. International Journal of Robotic Engineering, 92, 115-128.

Al-Dahhan, M., & Hussain, A. (2025). UAV-UGV Collaborative Framework for Border Surveillance in Harsh Climatic Conditions. IEEE Access, 13, 44210-44225.

Chen, J., & Zhang, Y. (2026). Asynchronous Collaborative Hybrid Architecture ACHA for Multi-Robot Search Operations. IEEE Transactions on Vehicular Technology, 751, 1-16.

DARPA. (2026). RACER’s Finish Line: Autonomous Off-Road Navigation in Complex Terrains. DARPA News. (Online). Available: www.darpa.mil/news/2026/racer-finish-line.

Gao, X., & Liu, S. (2025). Multi-Sensor Fusion for UAV Remote Sensing: Deep Learning-Driven Target Detection. Remote Sensing MDPI, 178, 1845.

Ivanov, V. V. (2024). Robotic Complexes for Engineering Reconnaissance: Tactical and Technical Aspects. Journal of Military Science and Technology, 124, 45-58.

Li, H., & Wang, Q. (2026). Multi-UAV-UGV Collaborative Path Planning for Emergency Response and Continuous Monitoring. IEEE Open Journal of Vehicular Technology, 7, 102-118.

Martinez, L., & Smith, K. (2023). Thermal Signature Analysis for Landmine Detection in High-Temperature Desert Environments. Journal of Arid Land Research, 153, 202-215.

Nguyen, T., et al. (2025). Dynamic Path Planning for Heterogeneous Robotic Teams in Sandy Terrains. Robotics and Autonomous Systems, 168, 104492.

Patel, R. (2024). Autonomous Landmine Detecting and Mapping Robots: A Review of Sensing Technologies. International Journal of Advanced Research in Electrical and Electronics, 111, 89-104.

Rodriguez, P. (2025). Bayesian Data Fusion for Heterogeneous Robotic Systems in Military Reconnaissance. IEEE Transactions on Aerospace and Electronic Systems, 612, 567-580.

Sahoo, S., & Mishra, A. (2024). Manned-Unmanned Teaming (MUM-T) in Modern Warfare: A Comprehensive Review. Defense Technology Review, 82, 34-50.

Zaman, K. (2024). Impact of Soil Composition on GPR Performance for Landmine Detection in Central Asian Deserts. Geophysics and Engineering, 213, 455-470.

Zhou, F., et al. (2026). A Foresight-Based Task Allocation Algorithm for Multi-Robot Teams. IEEE Transactions on Cybernetics, 561, 112-126.

Andreev, V. P., & Pletenev, P. F. (2025). Algorithms for Group Control of Ground and Aerial Robotic Systems in Complex Terrains. Robototekhnika i tekhnicheskaya kibernetika (Robotics and Technical Cybernetics), 12(1), 34-42. (In Russ.).

Kasatkin, S. I. (2024). Improving the Efficiency of Engineering Reconnaissance Using Multispectral Sensors. Voennaya Mysl (Military Thought), 5, 89-97. (In Russ.).

Makarov, I. M., & Lokhin, V. M. (2023). Intelligent Control Systems for Autonomous Mobile Robots in Extreme Environments. Izvestiya Rossiiskoi akademii nauk. Teoriya i sistemy upravleniya, 4, 112-125. (In Russ.).

Semenov, A. N., & Ivanov, D. A. (2025). Integration of UAV Data into GPR Scanning Processes for Landmine Detection. Izvestiya vuzov. Priborostroenie, 68(2), 145-153. (In Russ.).

Tarasov, V. V. (2024). Technical Vision Systems for Robots in Low-Visibility Conditions: Dust and Thermal Anomalies. Opticheskii Zhurnal (Journal of Optical Technology), 91(3), 22-31. (In Russ.).

Vorotnikov, S. A. (2026). Information-Control Systems for Heterogeneous Robotic Groups. Vestnik MGTU im. N.E. Baumana. Seriya Priborostroenie, 154(1), 45-60. (In Russ.).