LLM-Guided Task Planning with A*-Based Navigation for Autonomous UAVs

Abstract

Dynamic unmanned aerial vehicle (UAV) missions require online adaptation not only to geometric changes but also to evolving task-level constraints, such as energy depletion, facility unavailability, and newly imposed visits. This paper presents an event-driven hierarchical planning framework that integrates large language models (LLMs) with deterministic A*-based navigation. Upon detecting a mission event, an LLM revises only the remaining sequence of symbolic facility visits, while a layered verifier independently checks response structure, task semantics, energy and precedence constraints, and geometric executability. Each accepted task sequence is subsequently converted into collision-free flight segments by A*, thereby isolating language-based task reasoning from safety-critical path execution. Twelve LLMs are evaluated on a common urban map under four controlled conditions: recharge-triggered recovery, temporary no-fly-zone activation, mandatory waypoint insertion, and a joint event combining energy, visitation, and airspace constraints. An exhaustive task-sequence oracle provides a scenario-specific lower bound for evaluating route optimality. Across 48 model–scenario trials, 33 produced mission-valid plans, with the best-performing models reaching or closely approaching the oracle route length in all individual-event scenarios. Performance declined markedly under the joint event, where only 41.7% of the models generated valid plans, highlighting the difficulty of satisfying interacting mission constraints.