LLMQDelivery: An LLM-Guided Autonomous Quadcopter Delivery System for Minimizing Human Contact in Quarantine Zone Logistics

Disease outbreaks and quarantine enforcement demand contact-free logistics solutions that eliminate human exposure while maintaining reliable supply delivery. This paper presents LLMQDelivery, an LLM-driven autonomous quadcopter logistics framework designed to minimize human contact during quarantine or biohazard situations through fully automated, language-guided delivery and retrieval of essential supplies. The system combines persistent spatial awareness, physicsaccurate manipulation, and natural language reasoning within a unified control architecture implemented in CoppeliaSim via a Python-based controller. A Large Language Model (LLM) serves as the cognitive interface, interpreting free-form instructions such as “Deliver a red medical kit to zone two” into executable multistage flight plans, eliminating the need for operators to physically interact with contaminated environments. Experimental validation involving eighteen supply objects achieved a 100% delivery success rate with centimeter-level placement accuracy and 98.5% natural language command success across 67 diverse prompts. The results demonstrate that fusion of deterministic motion control and stochastic language reasoning enables practical, cognitively autonomous UAV systems-representing a new paradigm for safe, scalable, and AI-augmented quarantine logistics.