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Passive Landmark Geometry Optimization and Evaluation for Reliable Autonomous Navigation in Mining Tunnels Using 2D

Miguel Torres-Torriti1, Paola Nazate-Burgos1, Fabián Paredes-Lizama1

  • 1Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago 782-0436, Chile.

Sensors (Basel, Switzerland)
|April 23, 2022
PubMed
Summary

Autonomous mining tunnel navigation is improved using optimized artificial landmarks. These passive, low-maintenance landmarks significantly reduce localization errors, enabling reliable pose estimation for mobile robots.

Keywords:
2D lidar navigationGPS-denied environmentlocalization and SLAM in tunnelsscan matchingunderground mining robots

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Area of Science:

  • Robotics and Autonomous Systems
  • Geospatial Navigation
  • Artificial Intelligence

Background:

  • Mining tunnels lack GPS and natural landmarks, complicating autonomous navigation.
  • Existing solutions often require power-intensive beacons, posing safety and maintenance risks.

Purpose of the Study:

  • To develop an autonomous navigation approach for mining tunnels using optimized artificial passive landmarks.
  • To ensure drift-free localization for mobile units equipped with lidar scanners.

Main Methods:

  • Optimizing artificial landmark geometry and positioning using a genetic algorithm.
  • Employing scan matching techniques for pose estimation.
  • Validating the approach through simulation and real-world experiments with a robotic excavator.

Main Results:

  • Optimized triangular and symmetrical landmarks improved positioning accuracy by 87.5% per 100m traveled.
  • Demonstrated feasibility with inexpensive, low-maintenance passive landmarks.
  • Showcased the inadequacy of natural features alone for unambiguous localization.

Conclusions:

  • Optimized artificial landmarks are crucial for reliable autonomous navigation in smoothly bored mining tunnels.
  • The proposed method enables the implementation of grid-based SLAM (Simultaneous Localization and Mapping) techniques.
  • This approach offers a practical and effective solution for challenging underground environments.