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Hybrid IWD-GA: An Approach for Path Optimization and Control of Multiple Mobile Robot in Obscure Static and Dynamic Environments

Published online by Cambridge University Press:  24 February 2021

Saroj Kumar Open the ORCID record for Saroj Kumar [Opens in a new window] ,
Dayal Ramakrushna Parhi ,
Krishna Kant Pandey Open the ORCID record for Krishna Kant Pandey [Opens in a new window]  and
Manoj Kumar Muni Open the ORCID record for Manoj Kumar Muni [Opens in a new window]
Saroj Kumar*
Affiliation:
Robotics Laboratory, Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha, India 769008
Dayal Ramakrushna Parhi
Affiliation:
Robotics Laboratory, Department of Mechanical Engineering, National Institute of Technology, Rourkela, Odisha, India 769008
Krishna Kant Pandey
Affiliation:
Department of Mechanical Engineering, G.H. Raisoni Institute of Engineering and Technology, Pune, Maharashtra, India 412207
Manoj Kumar Muni
Affiliation:
Department of Mechanical Engineering, Indira Gandhi Institute of Technology, Sarang, Odisha, India 759146
*
*Corresponding author. E-mail: saroj4sks@gmail.com
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Summary

In this article, hybridization of IWD (intelligent water drop) and GA (genetic algorithm) technique is developed and executed in order to obtain global optimal path by replacing local optimal points. Sensors of mobile robots are used for mapping and detecting the environment and obstacles present. The developed technique is tested in MATLAB simulation platform, and experimental analysis is performed in real-time environments to observe the effectiveness of IWD-GA technique. Furthermore, statistical analysis of obtained results is also performed for testing their linearity and normality. A significant improvement of about 13.14% in terms of path length is reported when the proposed technique is tested against other existing techniques.

Keywords

Path optimizationNavigationIWD-GAMobile robotArtificial intelligence

Information

Type
Article
Information
Robotica , Volume 39 , Issue 11 , November 2021 , pp. 2033 - 2060
Copyright
© The Author(s), 2021. Published by Cambridge University Press

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