Blind-Wayfarer: A Minimalist, Probing-Driven Framework for Resilient Navigation in Perception-Degraded Environments

Navigating autonomous robots through dense forests and rugged terrains is especially daunting when exteroceptive sensors -- such as cameras and LiDAR sensors -- fail under occlusions, low-light conditions, or sensor noise. We present Blind-Wayfarer, a probing-driven navigation framework inspired by maze-solving algorithms that relies primarily on a compass to robustly traverse complex, unstructured environments. In 1,000 simulated forest experiments, Blind-Wayfarer achieved a 99.7% success rate. In real-world tests in two distinct scenarios -- with rover platforms of different sizes -- our approach successfully escaped forest entrapments in all 20 trials. Remarkably, our framework also enabled a robot to escape a dense woodland, traveling from 45 m inside the forest to a paved pathway at its edge. These findings highlight the potential of probing-based methods for reliable navigation in challenging perception-degraded field conditions. Videos and code are available on our websitethis https URL

@article{xu2025_2503.07492,
  title={ Blind-Wayfarer: A Minimalist, Probing-Driven Framework for Resilient Navigation in Perception-Degraded Environments },
  author={ Yanran Xu and Klaus-Peter Zauner and Danesh Tarapore },
  journal={arXiv preprint arXiv:2503.07492},
  year={ 2025 }
}