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Related Experiment Video

Updated: Jul 20, 2025

In Situ Soil Moisture Sensors in Undisturbed Soils

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Published on: November 18, 2022

RUDE-AL: Roped UGV Deployment Algorithm of an MCDPR for Sinkhole Exploration.

David Orbea¹, Christyan Cruz Ulloa¹, Jaime Del Cerro¹

¹Centro de Automática y Robótica (UPM-CSIC), Universidad Politécnica de Madrid-Consejo Superior de Investigaciones Científicas, 28006 Madrid, Spain.

Sensors (Basel, Switzerland)

|July 29, 2023

Summary

This summary is machine-generated.

This study introduces a novel robotic system for sinkhole exploration and rescue. The Roped UGV DEployment ALgorithm (RUDE-AL) uses a fleet of mobile robots to deploy a cable robot for safer and more efficient disaster site assessment.

Keywords:

CDPR MCDPR ROS genetic algorithms multi-robot system navigation roped fleet

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Area of Science:

Robotics and Geotechnical Engineering
Disaster Response and Urban Safety

Background:

Sinkholes pose significant risks to infrastructure and human safety in urban areas.
Traditional geotechnical and geophysical methods have limitations in assessing sinkhole-prone zones.
Robotics offers advanced solutions for inspection, environmental exploration, and post-disaster assistance.

Purpose of the Study:

To propose a robotic methodology for sinkhole exploration and victim assistance.
To develop a deployment algorithm for a fleet of mobile robots and a cable-driven parallel robot.
To address challenges in cable collision avoidance and optimize exploration coverage.

Main Methods:

Introduction of the Roped UGV DEployment ALgorithm (RUDE-AL) for deploying a Mobile Cable-Driven Parallel Robot (MCDPR).
Utilizing a fleet of four mobile robots as anchors for a cable robot system.
Employing genetic algorithms to optimize anchor placement, maximizing exploration area and minimizing route distance while avoiding obstacles.

Main Results:

Demonstration of a robust deployment methodology for sinkhole exploration.
Identification of factors influencing the system's performance, including obstacle density and sinkhole geometry.
Successful generation of feasible target routes for mobile robots within defined constraints.

Conclusions:

The RUDE-AL methodology provides an effective robotic solution for sinkhole exploration and rescue operations.
Optimization of mobile robot deployment is crucial for maximizing mission efficiency and safety.
The system's adaptability to environmental factors like obstacles and sinkhole shape is a key finding.