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

2025-03-10

AI Summary

Autonomous navigation in severely degraded perceptual environments—such as dense forests and rugged terrains—suffers from occlusion, low illumination, and sensor noise, causing failure of vision- and LiDAR-based systems. Method: This paper proposes a lightweight, perception-free navigation framework relying solely on an electronic compass and a mechanical probe. It replaces conventional visual sensing with active physical probing, establishing a closed-loop control paradigm comprising directional guidance, finite-state-machine decision-making, compass-based orientation, and motion feedback. Contribution/Results: The approach achieves zero-visual-input, low-compute, and high-robustness autonomous recovery from entrapment. In simulated forest environments, it attains a 99.7% task success rate; in real-world forest trials (20 runs), it achieved 100% success, autonomously navigating up to 45 meters from deep within the forest to hardened roadside surfaces. This significantly enhances navigation reliability and deployment feasibility under weak-perception conditions.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Search and Optimization: Learning to Search
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Abstract

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

Problem

• Navigating robots in dense forests with sensor failures.
• Developing a compass-based framework for resilient navigation.
• Achieving high success rates in perception-degraded environments.

Innovation

• Probing-driven navigation framework for resilient traversal
• Relies primarily on compass in perception-degraded environments
• Achieved 99.7% success rate in simulated forest experiments