Advanced UAV Technologies: Autonomy, Sensing, and Integration

Dear Colleagues,

Unmanned aerial vehicles (UAVs), once restricted to military reconnaissance or basic recreational photography, have emerged as sophisticated, multi-functional tools integral to the modern industrial and scientific fabric. The demand for UAVs to operate in complex, dynamic, and often GPS-denied environments—ranging from deep-forest ecological monitoring to autonomous urban infrastructure inspection—has never been higher. This collection of research addresses the three fundamental pillars required to meet these demands: the "brain" (Autonomy), the "senses" (Sensing), and the "connectivity" (Integration).

The field of control systems is undergoing a profound transformation driven by the integration of artificial intelligence (AI) technologies and the increasing complexity of physical and cyber–physical applications. This summary explores the crucial advances of the last decade and the research perspectives that will shape the future of control engineering, highlighting the evolution towards more autonomous, adaptive, and intelligent systems.

Current research is characterized by the consolidation of advanced control approaches in systems with increasingly demanding performance and safety requirements.

• Intelligent and AI-Based Control: There is a significant drive in the development of software-defined control systems, enabling close collaboration between industrial AI, the Industrial Internet, and new generations of information technologies. These systems are fundamental for the design of autonomous vehicles, where advanced control systems are responsible for optimal trajectory planning and real-time command implementation, interpreting complex sensor data.
• Robust and Adaptive Control: Developments in Sliding-Mode Control, including second-order control, are being applied to guarantee robustness and safety in uncertain systems. This type of control is essential for critical systems where fast and precise convergence is required despite external disturbances.
• Control Applied to Unmanned Platforms and Robotics: Unmanned aerial vehicles (UAVs) and terrestrial robotics, such as exoskeletons, are cutting-edge application areas. In social assistance robotics, adaptive control strategies have been proposed to achieve precise trajectory tracking, even on platforms with complex steering, enabling interaction with humans.
• Advanced Control Theory: Theory is increasingly focusing on the optimization and sub-optimization of control policies for linear systems perturbed by random noise, encompassing problems in stochastic control and adaptive control.

We are pleased to invite researchers and practitioners to contribute their latest findings, whether they are novel algorithms, innovative sensor hardware, or comprehensive case studies on system integration. Through this collaborative effort, we aim to provide a definitive resource that not only reflects the current state of the field but also charts the course for the next generation of autonomous aerial systems.

The goal of this Special Issue is to collect and present a comprehensive collection of papers, including original research articles and review papers, that provide in-depth insights into the latest advancements in the autonomy of advanced UAV vehicles. This Special Issue seeks to showcase the state-of-the-art in aerial vehicle autonomy, explore emerging trends, and identify future research directions. Through these collected works, we hope to provide a valuable resource for academics, engineers, and industry professionals involved in aerial robotics and advanced UAV technology, fostering further innovation and collaboration in this rapidly evolving area.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Cyber–Physical Systems and End-Edge-Cloud Collaboration: End-edge-cloud collaboration will be a main direction for the development of software-defined intelligent control systems. This will allow for scalable and distributed control orchestration in large network systems.
• Safe Autonomy and Real-Time Decisions: In autonomous vehicles and robotic systems, the focus will continue to be on developing robust decision-making algorithms that can handle sensor measurement uncertainty and dynamic environment modeling, ensuring safety in real time.
• Diversification of Applications: The impact of control will expand beyond traditional engineering. Applications are already visible in power systems (such as harmonic mitigation in inverters) and recommendation systems in e-learning, which promises an even broader role for control in the management and optimization of systems across various disciplines.
• Autonomy and Control Theory: The transition to full autonomy (Level 4 and 5) is driven by breakthroughs in Non-linear Control Systems and Path Planning. Traditional PID loops are being augmented or replaced by Model Predictive Control (MPC) and Reinforcement Learning (RL), allowing aircraft to adapt to unpredictable aerodynamic disturbances and obstacle-rich environments.
• Perception and State Estimation: At the heart of sensing lies the challenge of SLAM (Simultaneous Localization and Mapping). By fusing data from LiDAR, IMUs, and visual odometry, UAVs can now build high-fidelity 3D maps of their surroundings while simultaneously tracking their own position within those maps. This is supported by the scientific advancement of Kalman Filters and Graph-based Optimization for sensor fusion.
• Computational Intelligence: The integration of Edge AI allows for real-time processing of massive datasets. Instead of raw data transmission, onboard neural networks perform semantic segmentation and object detection, enabling immediate decision-making as a requirement for high-speed collision avoidance and autonomous "swarming" behaviors.

In summary, control systems are evolving from disciplines focused on regulation to tools of intelligence and autonomy, positioning themselves as the technological core for the next wave of industrial and robotic automation. The key challenge will be the convergence of theoretical robustness with the practical complexity of cyber–physical and intelligent systems.

Dr. Iván González-Hernández
Dr. Sergio Salazar
Guest Editors

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• real-time operating systems
• autopilot integration
• sensor data fusion techniques
• intelligent sensor integration
• UAV remote sensing
• autonomous UAV systems
• UAV navigation autopilot
• robust flight control systems
• AI and machine learning navigation
• obstacle avoidance algorithms