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

TL;DR

This work tackles navigation under perception failure in unstructured forests by introducing Blind-Wayfarer, a compass-based probing-driven framework inspired by the Pledge algorithm. It replaces reliance on exteroceptive sensing with a minimal sensing loop that uses a cumulative turning angle $C$, a fixed turn magnitude $\theta$, and anomaly-driven escape maneuvers, enabling robust entrapment recovery. The approach is validated through 1,000 abstract forest simulations (achieving $99.7\%$ success) and real-world experiments on two rover platforms across two forest scenarios, demonstrating reliable escapes and competitive path efficiency. The study offers a lightweight, explainable, sensor-efficient alternative for navigation in perception-degraded settings with potential extensions to adaptive turning strategies and swarm deployments.

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

Figures (6)

• Figure 1: ForestRover, a portable 4WD robot in a dense obstruction trial scenario at the Southampton Common Woodlands.
• Figure 2: Two trial scenarios in Southampton Common: Extended Maze Trial, featuring large fallen branches that form barrier-like obstructions (Left); Dense Obstruction Trial, characterized by the rover being surrounded by tightly cluttered fallen branches (Right). In both scenarios, obstacles do not fully block potential paths; ForestRover can navigate some of these gaps, while the more compact MiniRover can traverse even more. The blue dot and arrow marks the starting point and initial orientation of the robot, while the yellow line indicates the destination line. The inset image highlights the main obstacles with red lines. Both scenarios evaluate the rover’s ability to navigate cluttered, perception-degraded forest environments.
• Figure 3: Representative examples from the 500 abstract forest simulation environments used in our study. Each environment spans a $50\times50\,\text{m}^2$ area with a tree density of $80\,\text{trees/hectare}$, where each tree has a radius of $0.25\,\text{m}$. In the larger panel (Left), black lines indicate fallen branches, green circles represent standing trees, and red dots mark locations where the robot becomes immobilized or slips on rough terrains accidentally. The green line shows the robot's trajectory. The red star indicates the starting point and the black arrow denotes the designated heading. The smaller panels (right) illustrate additional scenarios that capture diverse obstacle layouts.
• Figure 4: Box plots of path length (left vertical axis) for each algorithm, with outliers shown as circles. The orange bars represent the proportion of simulations in which the robot fails to escape from the maze-like scenario (right vertical axis), and the red dashed line marks the shortest distance to the goal. The algorithms are grouped into Pledge-Inspired, Stochastic, and Reactive categories (separated by vertical dashed lines). The proposed Pledge-Inspired approaches achieve generally shorter path lengths and lower entrapment proportions, indicating more reliable navigation performance in perception-degraded forest simulations compared to purely stochastic or reactive baselines.
• Figure 5: Box plots of escape time (left vertical axis) for the ForestRover (left) and MiniRover (right) in two challenging forest scenarios: Dense Obstruction Trial and Extended Maze Trial. The blue boxes correspond to ForestPledge and the red boxes to LevyWalk. Gray bars (right vertical axis) show the number of trials in which the robot became entrapped (ForestPledge experienced no entrapment). Results indicate that ForestPledge generally yields lower escape times and fewer entrapment occurrences than LevyWalk, underscoring its robustness in complex, perception-degraded environments.