Authors: Changmin Park, Wooyong Jung, Hwangnam Kim
Organizations: School of Electrical Engineering Korea University Seoul 02841, Korea
Abstract
Autonomous UAV swarms require scalable coordination mechanisms that maintain mission performance under limited communication, environmental uncertainty, and component failures. Centralized approaches provide global coordination but suffer from communication bottlenecks and single-node vulnerabilities, whereas fully decentralized methods often lack mission-level consistency. This paper presents Layered Autonomous Edge Intelligence (LAEI), a UAV-swarm framework that combines onboard learned policies with lightweight mission-level supervision. Each UAV performs local perception, obstacle avoidance, and action selection onboard, while the supervisory layer provides adaptive goal reassignment, fault-aware recovery, and context-dependent policy guidance without directly controlling low-level actions. LAEI further incorporates recovery strategies, including dynamic reassociation, backup supervisory support, and fallback local autonomy, to maintain mission continuity under representative failure scenarios. We evaluate LAEI in simulated UAV-swarm scenarios using mission completion time, collision rate, and coverage efficiency. The results show that LAEI reduces mission completion time and improves operational efficiency while maintaining collision-aware distributed UAV-level decision-making.
Reliable autonomous UAV swarms in Search and Rescue (SAR) missions require fault-tolerant coordination capable of sustaining operations despite agent degradation. This paper introduces the Intelligent Replanning Drone Swarm (IRDS), a distributed coordination architecture designed for resource-constrained environments. The proposed framework employs a Reverse-Auction market mechanism where agents bid to service search sectors based on a distance-weighted cost function, coupled with a geometric consensus protocol for target verification. We evaluate the approach through physics-based simulations (N=8 agents, 8x8 grid) subjected to stochastic fault injection. Results indicate that the swarm autonomously reallocates tasks from failed agents with low latency relative to the total mission duration, maintaining a mission success rate of 93% under 25% workforce degradation. The proposed framework demonstrates a robust, empirically tested method for self-healing aerial robotic coordination.
Luiz Giacomossi, Andrea Haglund, Claire Namatovu +6
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.
Yousef Emami, Rahim Taheri, Mohammadhossein Homaei +2
This letter proposes the Predictive Lightweight Multi-Agent Reinforcement Learning (PL-MARL) framework to ensure resilient coverage in bandwidth-constrained UAV swarms. To counter coordination collapse caused by sparse signaling and information aging, we introduce a Kinematic-Aware Inference Engine that proactively reconstructs neighbor trajectories via physical priors. This approach enables an efficient computation-for-communication trade-off, decoupling structural resilience from signaling frequency. Simulations confirm that PL-MARL maintains superior coverage and mission continuity under extreme signaling scarcity and node failure. Our results validate proactive inference as a scalable, low-latency solution for robust aerial coordination, effectively minimizing control overhead to preserve spectrum for payload services while ensuring resilience against interference.