Automatic Control of Wall-following Mobile Robots Based on Machine Learning

Mobile robots have become increasingly popular in various industries and applications due to their versatility and ability to perform tasks safely, effectively, and independently. They can operate in hazardous or inaccessible areas, thus reducing the risk of injury to human workers. Mobile robots ca...

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Published in:IEEE NW Russia Young Researchers in Electrical and Electronic Engineering Conference pp. 166 - 169
Main Authors: Hammoud, Moahmmed, Lupin, Sergey
Format: Conference Proceeding
Language:English
Published: IEEE 29.01.2024
Subjects:
machine learning
Mobile robot
Multi-category classification
SCITOS G5
wall-following robot
ISSN:2376-6565
Online Access:Get full text
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Abstract Mobile robots have become increasingly popular in various industries and applications due to their versatility and ability to perform tasks safely, effectively, and independently. They can operate in hazardous or inaccessible areas, thus reducing the risk of injury to human workers. Mobile robots can also work continuously without breaks, thus increasing productivity and reducing labor costs. Wall-following mobile robots (WMRs) use sensors and algorithms to detect and avoid obstacles while maintaining a constant distance from them. They are instrumental in environments with narrow passages or limited visibility, such as underground tunnels or mines. This study used a benchmark dataset collected by the SCITOS G5 robot to evaluate various Machine Learning (ML) algorithms. This research guides on selecting an ML algorithm to achieve a balance between accuracy and memory consumption. The experiments showed that eXtreme Gradient Boosting (XGB) performed the best with a macro F1-score of 0.9934 on 24 sensor readings.
AbstractList Mobile robots have become increasingly popular in various industries and applications due to their versatility and ability to perform tasks safely, effectively, and independently. They can operate in hazardous or inaccessible areas, thus reducing the risk of injury to human workers. Mobile robots can also work continuously without breaks, thus increasing productivity and reducing labor costs. Wall-following mobile robots (WMRs) use sensors and algorithms to detect and avoid obstacles while maintaining a constant distance from them. They are instrumental in environments with narrow passages or limited visibility, such as underground tunnels or mines. This study used a benchmark dataset collected by the SCITOS G5 robot to evaluate various Machine Learning (ML) algorithms. This research guides on selecting an ML algorithm to achieve a balance between accuracy and memory consumption. The experiments showed that eXtreme Gradient Boosting (XGB) performed the best with a macro F1-score of 0.9934 on 24 sensor readings.
Author Lupin, Sergey
Hammoud, Moahmmed
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  givenname: Sergey
  surname: Lupin
  fullname: Lupin, Sergey
  email: lupin@miee.ru
  organization: National Research University of Electronic Technology,Institute of Microdevices and Control Systems,Moscow,Russia
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Snippet Mobile robots have become increasingly popular in various industries and applications due to their versatility and ability to perform tasks safely,...
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SubjectTerms machine learning
Mobile robot
Multi-category classification
SCITOS G5
wall-following robot
Title Automatic Control of Wall-following Mobile Robots Based on Machine Learning
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