Aug 2026· SPE Nigeria Annual International Conference and Exhibition· 0 citations· 16 references
Abstract
The increasing use of Unmanned Aerial Vehicles (UAVs) in energy-sector operations- such as pipeline inspection, infrastructure monitoring, and remote asset surveillance- has highlighted critical limitations in predominantly manual and semi-automated drone systems. While current UAV deployments offer improved safety and efficiency over traditional inspection methods, their dependence on continuous human control and stable communication links restricts scalability, resilience, and operational autonomy in complex or hazardous environments. This paper presents a conceptual framework for (state) adaptive autonomous UAV systems designed to address these limitations. The proposed approach emphasizes the integration of intelligent sensing, perception, decision-making, control, and communication as coordinated layers capable of adjusting to changing operational conditions. Rather than focusing on specific implementations, the framework outlines how autonomy-driven design principles can enhance UAV reliability, reduce human intervention, and improve operational continuity in energy-sector applications. By positioning autonomy as a critical enabler rather than an optional feature, this work aligns with ongoing digital transformation and energy transition efforts. The paper discusses potential application scenarios within oil and gas, power infrastructure, and renewable energy systems, and highlights key challenges related to regulation, system validation, and future deployment. The proposed framework provides a foundation for further research and development toward resilient, intelligent UAV operations in the evolving global energy landscape.
Background. The rapid evolution of unmanned aerial vehicles (UAVs) from remote surveillance tools to complex autonomous cyber-physical systems necessitates systematization of their application and control architecture. The purpose of this work is to provide a comprehensive overview of the tasks solved by autonomous UAVs in civil and special spheres, and to develop a conceptual multi-level architecture that ensures the achievement of true autonomy. Materials and methods. An analysis of modern tasks of civil (infrastructure monitoring, precision agriculture, cargo delivery, cartography, search and rescue operations) and special applications (reconnaissance, electronic warfare, target designation) was conducted. Based on this analysis, a hierarchical management model is proposed, divided into five levels: strategic, tactical, operational, functional and fundamental. Results. It has been established that effective autonomy is achieved through a clear decomposition of goals into levels. The strategic level integrates UAVs into business processes, the tactical level plans and adapts routes, the operational level ensures navigation and safety, the functional level solves applied problems (image analysis, detection), and the fundamental level maintains the system’s functionality. Using a search and rescue operation as an example, we demonstrate how the interaction of layers enables the creation of complex, adaptive, and scalable system behavior. The developed multilayer architecture represents a universal framework for designing autonomous UAV systems, transforming them from simple performers into intelligent agents of cyber-physical ecosystems. Conclusions. The key finding is that complex behavior arises from the integration of relatively simple, specialized algorithms operating at different levels of abstraction. Future research focuses on the development of cross-layer protocols, artificial intelligence algorithms for tactical and strategic planning, and the creation of standardized platforms.
Aleksey P. Golovin, Maksim A. Mitrokhin· University proceedings Volga...· 0 citations
This study proposes an AI-enabled autonomous drone framework for infrastructure inspection that integrates intelligent flight planning, automated data collection, computer vision-based defect detection, and condition assessment that enhances inspection accuracy, operational safety, and scalability compared to traditional methods.
Yuki Nakamura· International Journal of Mod...· 0 citations
Unmanned aerial vehicles (UAVs) have become important components of modern systems for monitoring, inspection, mapping, and autonomous intervention. Their performance directly depends on the efficiency of the control systems and the architecture used for data processing, decision-making, and mission coordination. The paper presents a comparative analysis of the main control architectures used in UAV platforms: control with on-board processing (On-Board Control), hierarchical Master-Swarm architecture, and centralized systems based on a Ground Control System (GCS). It describes the operating principles of each architecture, the hardware and software components involved, advantages, limitations, and specific areas of application. The analysis highlights the impact of each solution on autonomy, scalability, resilience to communication loss, and real-time control performance. The results obtained show that On-Board Control systems offer superior autonomy and robustness, Master-Swarm architectures are intended particularly for the collaborative coordination of drone swarms, and GCS systems provide advanced capabilities for monitoring and centralized processing. Furthermore, current trends oriented towards hybrid architectures are highlighted, which combine the advantages of the three models to increase the level of autonomy, safety, and operational efficiency of modern UAV systems.
A. Ursu, Igor Calmîcov, Viorel Cărbune· Journal of Engineering Scien...· 0 citations
This paper proposes a conceptual approach to the autonomous takeoff and landing control of autonomous mobile nodes (AMNs) based on the Total Energy Control System (TECS) methodology. The study aims to enhance the operational efficiency and survivability of unmanned aviation by utilizing the aircraft's energy state as a fundamental control invariant. The proposed approach overcomes the limitations of traditional systems, specifically their high sensitivity to stochastic disturbances and degraded stabilization accuracy under atmospheric turbulence. The control algorithm is optimized based on two criteria: minimizing the deviation of the AMN's total energy from the reference flight trajectory and minimizing the imbalance between kinetic and potential energies to stabilize the rate of descent (or climb). Simulation results demonstrate that the implementation of this method provides robust resilience to external dynamic disturbances, mitigates the human factor, and enables mission execution under adverse weather conditions without rigid dependence on ground-based airfield infrastructure. Future research will focus on scaling the approach to multi-agent systems, integrating adaptation methods based on random projections, and field testing on fixed-wing platforms.
O. Volkov, I. Popov· Information Technologies and...· 0 citations
UAVforRail is a fully automated docking and charging station designed to support autonomous monitoring of railway infrastructure using unmanned aerial vehicles (UAVs). The system addresses the limitations of traditional railway inspections, which are labor-intensive, time-consuming, and expose personnel to risks near active tracks. Developed for the operational needs of PKP PLK S.A., the solution is based on a modular, scalable architecture that enables repeatable inspections with minimal human intervention. The UAVforRail ecosystem integrates a multi-sensor UAV platform, an autonomous docking station, and a cloud-based control and data processing center. The primary focus of this work is the design, modeling, and validation of the docking station as a key element enabling long-term autonomous UAV operation. The station automates landing, positioning, charging, environmental protection, and data transfer. A vision-based landing subsystem using fiducial markers enables precise, repeatable docking operations. Mechanical components were developed and verified using CAD/CAE methodologies, FEM structural analysis, CFD simulations, and energy optimization studies. The UAV platform integrates RGB, LiDAR, and thermal sensors for multimodal inspection and defect detection. The system was validated through laboratory and field experiments, confirming reliable docking, charging, and operation under varying environmental condi - tions. The results demonstrate the feasibility of autonomous and persistent railway infrastructure monitoring using integrated UAV docking technologies.
Antoni Kopyt, Dawid Florczak· Advances in Science and Tech...· 0 citations
Unmanned aerial vehicles have become indispensable components of modern defense systems by conducting Intelligence, Surveillance, and Reconnaissance (ISR) missions to collect critical operational information through onboard sensing technologies, supporting secure communication and information sharing among distributed military assets, and enhancing battlefield situational awareness through real-time sensing and data fusion. In addition, UAVs are increasingly capable of executing a wide range of defense missions, including target search and tracking, electronic warfare, search-and-rescue, and combat support. The integration of AI into UAV-based systems has the potential to enhance these operational capabilities by enabling intelligent perception, autonomous decision-making, adaptive mission planning, autonomous navigation, resilient communications, and cooperative multi-UAV coordination, thereby enabling the autonomous and collaborative execution of complex defense missions. This paper presents an up-to-date review of AI-enabled UAV-based defense systems, focusing on major operational domains including autonomous air combat and cooperative UAV operations, path planning and autonomous navigation, target tracking/detection/classification, cybersecurity, electronic warfare protection, and resilient UAV operation. In addition to surveying the recent literature, this paper provides an integrated system architecture, a functional classification framework, and an analysis of the AI paradigms enabling next-generation UAV-based defense systems. Furthermore, this review synthesizes the key technological trends, lessons learned, and cross-domain research challenges identified across the reviewed studies, providing a unified perspective on the current state of the field. Finally, this paper highlights promising future research directions for resilient, scalable, secure, and intelligent next-generation AI-enabled UAV-based defense systems.
E. T. Michailidis, Irene S. Karanasiou· Drones· 0 citations
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