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RL-DOVS: Reinforcement Learning for Autonomous Robot Navigation in Dynamic Environments.

Sensors (Basel, Switzerland)·2022

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Detection and Quantification of Tunneling Nanotubes Using 3D Volume View Images

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Robot Localization in Tunnels: Combining Discrete Features in a Pose Graph Framework.

Teresa Seco¹, María T Lázaro¹, Jesús Espelosín¹

¹Instituto Tecnológico de Aragón, 50018 Zaragoza, Spain.

Sensors (Basel, Switzerland)

|February 26, 2022

Summary

This study introduces a novel robot localization system for GPS-denied tunnels. It effectively uses radio frequency signals and structural features for reliable navigation in challenging underground environments.

Keywords:

RF propagation inspection localization pose-graph robotics tunnel

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Area of Science:

Robotics
Geospatial Intelligence
Signal Processing

Background:

Robot localization in tunnels is difficult due to GPS denial, poor visibility, and lack of distinct features.
Traditional methods using cameras, lasers, or encoders fail in these complex underground environments.

Purpose of the Study:

To develop a robust robot localization system for tunnels.
To leverage unique tunnel characteristics for improved navigation accuracy.

Main Methods:

A graph-based localization approach integrating radio frequency (RF) signal periodic fadings.
Detection of tunnel structural features (e.g., galleries, shelters) as discrete information sources.

Main Results:

Experimental validation in a real tunnel confirmed the system's effectiveness.
The proposed method demonstrated suitability for tunnel inspection applications.

Conclusions:

The integrated approach of RF signals and structural features offers a viable solution for tunnel robot localization.
This system enhances the reliability of robotic operations in challenging subterranean settings.