Intelligent Control, Navigation, and Safety for Ground, Surface and Aerial Autonomous Vehicles in Smart Environments

Dear Colleagues,

The use of autonomous vehicles has exponentially grown in the last few decades, both in military and civil applications. In particular for the latter case, Unmanned Aerial Vehicles (UAVs), Unmanned Ground Vehicles (UGVs) and Unmanned Surface Vehicles (USV) play key roles in the smart cities context thanks to the benefits of carrying out autonomous operations, low maintenance costs, and ease of deployment even in remote locations.

The rapid expansion of autonomous vehicles makes them essential in a wide range of smart city applications, including aerial photography, real-time monitoring, disaster management, security, infrastructure inspection, and goods delivery. Many such tasks involve collecting and processing vast amounts of data, posing challenges due to the limited computational resources of autonomous agents and necessitating efficient data offloading strategies. Additionally, high-precision navigation is crucial to guarantee situational awareness for the swarm of agents, requiring advanced sensor fusion solutions.

This considered, the advancement obtained in urban infrastructure solution to support autonomous vehicle has greatly altered the possible activities available for these devices, which, by delegating heavy computational task to the urban infrastructure (relying in most of the cases on Compute Continuum approaches), may be charged with multiple and complex tasks that can be supported by digital twins.

As the adoption of this technology accelerates, addressing these challenges is imperative to ensure safe, efficient, and intelligent autonomous operations in smart environments.

This Special Issue will encourage the submission of original research and review articles to share the progress and efforts useful for guaranteeing the efficient and intelligent use of a swarm of autonomous agents, focusing on technologies for control, navigation, and artificial intelligence applied within the context of smart cities.

In the rapidly evolving landscape of smart cities and autonomous systems, the integration of advanced technologies, such as path planning and trajectory planning, control and decision-making in robotic systems, and game theory, plays a pivotal role in optimizing urban mobility and sustainability. Multi-objective optimization techniques are employed to balance competing goals, such as minimizing energy consumption while maximizing efficiency in unmanned systems, including multi-drone systems and Unmanned Aerial Vehicles (UAVs). These systems rely on modeling and simulation to predict outcomes and refine strategies, supported by operational research and control theory to ensure robust performance.

Adaptive control and predictive control mechanisms enable systems to respond dynamically to changing environments, while sensors and system identification provide critical data for real-time adjustments. Self-learning capabilities, such as pattern discovery and auto-configuration, enhance the autonomy of these systems, while data processing, whether on nodes, distributed, or aggregated, ensures the efficient handling of big data generated by Internet of Things (IoT) networks.

The adaptability of these systems to environmental changes, energy constraints, and faults is crucial for their resilience. Secure and low-energy communication protocols are essential for maintaining the integrity of cyber–physical systems, particularly in applications such as the Internet of Vehicles (IoV) and urban air traffic management. Crowdsourcing, crowdsensing, and participatory sensing further enrich data collection, enabling smarter decision-making in sustainable urban transport and mobility in smart cities.

Guidance, navigation, and control systems ensure precise target tracking and coordination in swarms of autonomous vehicles, while digital twins provide virtual replicas for testing and optimization. These technologies are underpinned by artificial intelligence, which drives innovation in computing and sensing infrastructures supporting autonomous vehicles.

From unmanned aerial vehicles to cyber–physical systems, the convergence of these technologies is transforming urban landscapes, enabling smarter, more sustainable, and resilient cities. Whether through applications in traffic management or the deployment of multi-drone systems, the future of urban mobility lies in the seamless integration of these advanced systems, ensuring efficiency, safety, and adaptability in an increasingly connected world.

Dr. Salvatore Rosario Bassolillo
Dr. Vito Antonio Nardi
Dr. Giuseppe Tricomi
Guest Editors

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• path and trajectory planning of autonomous robots (i.e., platooning)
• modelling and simulation
• control and decision in robotic systems (i.e., control theory, adaptive control, predictive control)
• system theory and system identification collision avoidance
• applied math in autonomous robots (i.e., game theory, operational research, multi-objective optimization)
• self-learning (i.e., pattern discovery, prediction, auto-configuration)
• data processing for autonomous robots (i.e., on nodes, distributed, aggregation, discovery, big data)
• communication (i.e., security, resilience)
• sensing (i.e., crowdsourcing, crowdsensing, participatory sensing)
• computing and sensing infrastructures supporting autonomous vehicles. internet of vehicles
• air traffic management (i.e., unmanned aerial vehicles, swarm of autonomous vehicles, multi-drone systems, unmanned systems)
• guidance, navigation and control
• target tracking
• digital twins
• cross-disciplinary elements of interest for autonomous robots and vehicles: cyber–physical systems, internet of things, artificial intelligence
• mobility in smart cities