<![CDATA[Unmanned Aerial Vehicles (UAVs) require robust exploration strategies to operate effectively in unknown indoor environments. Traditional methods often rely on prior training data or environment-specific models, limiting their adaptability in novel scenarios. In this paper, we propose a Curiosity-Aware Zero-Shot Framework that integrates an Intrinsic Curiosity Module (ICM) with a domain-randomized Zero-Shot planner to enable efficient and autonomous UAV exploration without retraining. Our framework is trained in simulated environments with randomized layouts to promote generalization, and eval- uated in unseen 3D indoor scenes. Experimental results show that our method significantly outperforms baselines such as Random Walk, Greedy Frontier, ICM-only, and Zero-Shot-only planners, achieving 89.7% coverage, 1.6 path efficiency, 328 seconds exploration time, and a 94.5% success rate. The ablation study highlights the complementary role of both ICM and Zero- Shot components. This work presents a scalable solution for real-time UAV navigation and contributes to the development of intelligent aerial systems capable of learning to explore novel environments autonomously.]]>
Copyrights © 2025
