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

2026-08-28 12:33:49
Marin Maletic, Marijana Peti, Tamara Petrovic, Stjepan Bogdan
arXiv_RO
             
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.

Abstract (translated)

URL

https://arxiv.org/abs/2608.28270

PDF

https://arxiv.org/pdf/2608.28270.pdf

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