This paper proposes LLM-Centric Agentic AI for UAV Swarms (LAUS) and reviews key enabling technologies such as onboard and edge computing, 5G/6G connectivity, multimodal intelligence, and cybersecurity mechanisms, and analyzes threats such as Priority Manipulation Attacks (PMA) that can distort decision-making and degrade network performance.
Abstract
Uncrewed Aerial Vehicle (UAV) swarms have significant potential for applications such as Search and Rescue (SAR) and environmental monitoring, but their real-world deployment is limited by a lack of situational awareness, intermittent connectivity, and significant cybersecurity risks. Agentic Artificial Intelligence (AI) represents a shift from standalone Large Language Model (LLM) toward closed-loop cognitive architectures that integrate perception, memory, reasoning/planning, and action to enable adaptive, goal-directed swarm behavior. Within this framework, Agentic AI provides a unifying structure for autonomous and adaptive swarm operations while expanding the system attack surface compared to conventional AI systems. This paper proposes LLM-Centric Agentic AI for UAV Swarms (LAUS) and reviews key enabling technologies such as onboard and edge computing, 5G/6G connectivity, multimodal intelligence, and cybersecurity mechanisms, and analyzes threats such as Priority Manipulation Attacks (PMA) that can distort decision-making and degrade network performance. Finally, it identifies open research challenges, including hallucination-resistant reasoning, onboard LLM deployment under SWaP constraints, and standardized security benchmarks for perception-reasoning attacks in agentic UAV systems.
Recent advances in swarm intelligence have instigated remarkable progress in autonomous unmanned aerial vehicle (UAV) systems, steering transformative applications in surveillance, disaster management and environmental monitoring. UAV swarms, inspired by biological collectives, exhibit robust self-organization, adaptability, and resilience in sophisticated environments. Integrating layered architectures, swarm intelligence algorithms span decision-making, path planning, cooperative control, inter, intra-drone communication, and mission-specific applications. Despite significant progress, achieving scalable, energy-efficient, and real-time coordinated behaviour in dynamic, uncertain situations remain wider challenge. Our approach blends fundamental research and cutting-edge innovations, highlighting emergent hybrid techniques that couple reinforcement learning, evolutionary optimization, and distributed consensus. A new hybridized framework is proposed that integrates multi-strategy learning, decentralized consensus, and multiple pheromone-based communication for sturdy swarm operation. Algorithmic efficiency is benchmarked on canonical search, mapping, and disaster mitigation events, disclosing marked improvements in task allocation, collision avoidance, and adaptive mission planning. Mathematical modelling and simulation underscore the approach’s ability to optimize resource utilization while maintaining high fault tolerance and adaptability. The presented framework paves the way for resilient, self-configurable UAV swarms potential of addressing real-world reluctance through scalable and intelligent collaboration.
Srihari Babu Gole, Laxmi Koli, Karnam Sreenu et al.· International journal of com...· 0 citations
Agentic artificial intelligence (AI) is transforming Integrated Sensing and Communication (ISAC) from a function-oriented physical-layer technology into a goal-driven, closed-loop intelligent system, a paradigm we term AISAC. Existing work on learning-based sensing, resource allocation, reconfigurable intelligent surfaces (RIS), edge intelligence, multi-agent coordination, and resilient networking has developed largely in isolation. This survey unifies the literature within a six-stage closed-loop framework comprising observation, contextualization, reasoning and prediction, planning and orchestration, execution and collaboration, and feedback and resilience. It also introduces five levels of agentic maturity, ranging from physical-layer primitives to fully closed-loop agentic ISAC. We use this framework to review advances in multimodal intelligence, large language models, reinforcement learning, federated learning, RIS-assisted control, Unmanned Aerial Vehicle (UAV) and vehicular networks, and AI-native network management, and analyze privacy, security, resilience, and sustainability as cross-cutting requirements of the full perception-reasoning-action loop. An audit of representative studies against nine agentic-specific evaluation criteria shows that no system reports more than one or two of them, exposing a gap between claimed and demonstrated agentic maturity. We identify open challenges in physical-to-semantic grounding, predictive world models, real-time agent-PHY interaction, safe tool use, heterogeneous multi-agent collaboration, benchmarking, and resource-efficient autonomy.
Kai Li, Conggai Li, S. A. Siddiqui et al.· 0 citations
It is argued that agentic AI should be approached as a socio-technical design problem, where interfaces, oversight mechanisms, and evaluation practices are as critical as algorithms.
Timothy Merritt, Alejandro Jarabo-Peñas, Juan Bravo-Arrabal et al.· 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
Unmanned aerial vehicles (UAVs) are progressively evolving from remotely operated platforms into intelligent autonomous systems. This research addresses the role of Artificial Intelligence (AI) in advancing UAV capabilities and expanding their applications across diverse sectors. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 methodology, studies retrieved from Scopus and relevant academic books and book chapters were screened, resulting in 217 publications retained for final analysis. The analysis introduces a three-layer framework linking AI techniques, UAV functional capabilities, and application domains. The first layer covers the list of adopted AI and ML approaches in the UAV applications. The second layer maps these approaches to key UAV capabilities, including perception, autonomous navigation, control and stability, swarm coordination, communication, and energy optimization. The third layer examines applications in agriculture, logistics, disaster response, environmental monitoring, surveillance, defense, and wireless network systems. The findings show that deep learning enhances aerial perception, reinforcement learning supports adaptive navigation and control, federated learning improves distributed intelligence, and swarm intelligence enables cooperative multi-UAV missions. Despite these advances, AI-enabled UAVs still face challenges related to energy consumption, onboard computation, data availability, communication reliability, safety, ethics, privacy, and regulation. Future progress is expected to be driven by edge AI, Tiny Machine Learning (TinyML), quantum-inspired optimization, explainable artificial intelligence (XAI), human-AI collaboration, and robust swarm coordination. Overall, this review provides a structured synthesis of AI-enabled UAV research and identifies key directions for future innovation.
Binz A. Aziz, Mostafa A. Rushdi, Shigeo Yoshida et al.· Applied Sciences· 0 citations
The rapid expansion of real-time Internet of Things (IoT) applications has positioned uncrewed aerial vehicles (UAVs) as a promising solution for flexible and timely data collection in areas lacking robust infrastructure. This paper investigates a UAV-assisted secure status updating system, where a UAV serves as a mobile relay to forward status updating packets from ground devices (GDs) under the threat of a potential eavesdropper. To ensure information freshness and operational sustainability, we formulate a long-term stochastic optimization problem to minimize the cumulative average age-of-information (AoI) of all GDs and energy consumption of the UAV. The formulated optimization problem is an online mixed-integer non-linear programming problem, which involves the joint optimization of the flight speed, direction, and transmission power of the UAV as well as the binary scheduling indicator of GDs. To tackle the inherent non-convexity and complex spatial-temporal coupling, we propose an agentic artificial intelligence (AI)-enabled deep reinforcement learning (DRL) approach, named adaptive truncated quantile critics with large language models (LLM)-enabled state representation and reward function design (ATQC-L). Specifically, an adaptive truncated quantile mechanism is incorporated to mitigate distributional overestimation in dynamic environments. Furthermore, we leverage the reasoning capability of LLMs as an offline design-time agent to generate task-aware state representation and intrinsic reward functions. Simulation results demonstrate that the proposed ATQC-L algorithm outperforms representative DRL baselines in balancing information freshness and energy consumption of the UAV, while maintaining stable performance under different network scales, LLM backbones, truncation-parameter settings, imperfect eavesdropping channel state information, and mobile eavesdropping scenarios.
Chuang Zhang, Geng Sun, Jiahui Li et al.· IEEE Transactions on Cogniti...· 0 citations
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