Spatial-Semantic Reasoning using Large Language Models for Efficient UAV Search Operations

• URL: http://arxiv.org/abs/2608.28270v1
• Date: Fri, 28 Aug 2026 12:33:49 GMT
• Title: Spatial-Semantic Reasoning using Large Language Models for Efficient UAV Search Operations
• Abstract summary: We present a real-time semantic navigation framework for Unmanned Aerial Vehicles (UAVs) focused on improving time efficiency in the Object Goal Navigation (ObjectNav) task.<n>A Large Language Model (LLM) interprets user-provided natural language instructions and performs semantic reasoning over detected objects.<n>Experiments in both simulated and real-world settings demonstrate reductions in mission duration while maintaining high search accuracy.
• Score: 2.012425476229879
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present a real-time semantic navigation framework for Unmanned Aerial Vehicles (UAVs) focused on improving time efficiency in the Object Goal Navigation (ObjectNav) task. Central to our approach is a Large Language Model (LLM) that interprets user-provided natural language instructions and performs semantic reasoning over detected objects and spatial context to prioritize high-probability search regions. The system combines real-time object detection, 3D spatial mapping, and polynomial spline interpolation for smooth and feasible UAV trajectory planning. Unlike prior methods that rely on offline reasoning or simulator-constrained action spaces, our framework can operate in real time, continuously updating semantic relevance based on new observations. Experiments in both simulated and real-world settings demonstrate reductions in mission duration while maintaining high search accuracy, underscoring the effectiveness of LLM-guided reasoning for time- efficient UAV-based ObjectNav.

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