SLM-A*: Compact Language-Guided Autonomous Agentic Planner for Risk-Aware Multi-UAV Path Planning
Multi-UAV path planning in threat-aware environments requires generating trajectories that reach mission objectives while maintaining obstacle clearance, inter-agent separation, and computational efficiency. This paper presents SLM-A*, a hybrid language-to-waypoints planning framework that fine-tunes compact transformer models to generate discrete multi-UAV waypoint sequences from structured mission descriptions. The generated trajectories are treated as candidate plans and are subsequently evaluated using a deterministic geometric safety checker that verifies obstacle avoidance, risk exposure, inter-UAV separation, edge-level conflicts, and motion feasibility. We construct a dataset of 3,000 static threat-aware scenarios consisting of three UAVs, four threats, one objective, and a $40\times 40$ grid using a prioritized time-expanded A* planner with clearance and turn penalties. Six small language models, including Gemma-2B, Fox-1-1.6B, Qwen2.5-1.5B, Phi-2-2.7B, StableLM-3B, and TinyLlama-1.1B, are adapted using QLoRA with 4-bit NF4 quantization. Among these models, TinyLlama-1.1B achieves the strongest overall text-level performance and inference throughput and is selected as the SLM-A* planning module. On a 315-scenario held-out benchmark, SLM-A* achieves the best average values on four of five evaluated trajectory metrics, including makespan, sum of costs, conflict count, and risk exposure, while CBS maintains the largest minimum-separation margin. Gazebo-ArduPilot-MAVROS simulations further demonstrate that the generated waypoint sequences can be executed by a realistic UAV control stack. The results suggest that compact language models, when combined with explicit geometric validation, can serve as efficient candidate planners for multi-UAV waypoint generation under resource-constrained deployment settings. Beyond trajectory performance, SLM-A* is designed as a language-native planning agent that can interoperate with multi-agent LLM frameworks as a callable planning sub-module, thus providing an architectural capability not available to conventional planners that operate on numerical graph representations.