Terrain-aware Low Altitude Path Planning

In this paper, we study the problem of generating low altitude path plans for nap-of-the-earth (NOE) flight in real time with only RGB images from onboard cameras and the vehicle pose. We propose a novel training method that combines behavior cloning and self-supervised learning that enables the learned policy to outperform the policy trained with standard behavior cloning approach on this task. Simulation studies are performed on a custom canyon terrain.

@article{jia2025_2505.07141,
  title={ Terrain-aware Low Altitude Path Planning },
  author={ Yixuan Jia and Andrea Tagliabue and Navid Dadkhah Tehrani and Jonathan P. How },
  journal={arXiv preprint arXiv:2505.07141},
  year={ 2025 }
}