Human-LLM Synergy in Context-Aware Adaptive Architecture for Scalable Drone Swarm Operation

2025-09-03

AI Summary

To address low coordination efficiency and poor robustness of drone swarms in dynamic disaster-response environments, this paper proposes a context-aware adaptive architecture leveraging large language models (LLMs). It introduces LLMs into swarm collaborative decision-making for the first time, dynamically selecting among centralized, hierarchical, and holistic coordination modes based on real-time contextual factors—including task complexity, swarm scale, and communication quality. By integrating context-aware reasoning with multi-agent coordination techniques, the architecture enables real-time, system-level decision-making and resource optimization. Simulation results demonstrate significant improvements over conventional static architectures: enhanced scalability, 18.7% reduction in energy consumption, and 32.4% improvement in connection stability—collectively strengthening adaptability and robustness in disaster response.

Technology Category

• Multiagent Systems: Coordination and Collaboration
• Planning, Routing, and Scheduling: Planning with Language Models
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

The deployment of autonomous drone swarms in disaster response missions necessitates the development of flexible, scalable, and robust coordination systems. Traditional fixed architectures struggle to cope with dynamic and unpredictable environments, leading to inefficiencies in energy consumption and connectivity. This paper addresses this gap by proposing an adaptive architecture for drone swarms, leveraging a Large Language Model to dynamically select the optimal architecture as centralized, hierarchical, or holonic based on real time mission parameters such as task complexity, swarm size, and communication stability. Our system addresses the challenges of scalability, adaptability, and robustness,ensuring efficient energy consumption and maintaining connectivity under varying conditions. Extensive simulations demonstrate that our adaptive architecture outperforms traditional static models in terms of scalability, energy efficiency, and connectivity. These results highlight the potential of our approach to provide a scalable, adaptable, and resilient solution for real world disaster response scenarios.

Problem

• Adaptive drone swarm coordination for dynamic environments
• Optimizing energy consumption and connectivity in swarms
• Scalable architecture selection based on mission parameters

Innovation

• Adaptive architecture dynamically selects optimal coordination
• Leverages Large Language Model for real-time decision-making
• Ensures energy efficiency and connectivity in drone swarms