用指南针+探查策略，让机器人在视觉失效时也能安全穿越森林。
Blind-Wayfarer: A Minimalist, Probing-Driven Framework for Resilient Navigation in Perception-Degraded Environments
- 基于迷宫求解思路，仅依赖指南针进行导航决策。
- 仿真中99.7%成功率，实测20次全成功逃出森林。
- 适合感知严重退化的野外环境，如密林、低光区域。
在密集森林和崎岖地形中，自主机器人常因摄像头、激光雷达等外部传感器在遮挡、低光照或噪声下失效而难以导航。我们提出盲行者（Blind-Wayfarer），一种受迷宫求解算法启发的探查驱动导航框架，主要依赖指南针在复杂非结构化环境中实现鲁棒行走。在1000次仿真森林实验中，该方法取得99.7%的成功率；在两种不同尺寸的火星车平台的真实测试中，所有20次试验均成功摆脱森林困局。尤为突出的是，该系统使机器人从林内45米深处成功抵达边缘铺装道路。结果表明，探查驱动方法在感知退化场景下具有高度可靠性。视频与代码已公开于https://sites.google.com/view/blind-wayfarer。
原文摘要 · Abstract (English)
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 website https://sites.google.com/view/blind-wayfarer
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