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Improved Motion Planning of Humanoid Robots Using Bacterial Foraging Optimization

Published online by Cambridge University Press:  07 May 2020

Manoj Kumar Muni Open the ORCID record for Manoj Kumar Muni [Opens in a new window] ,
Dayal R. Parhi  and
Priyadarshi Biplab Kumar Open the ORCID record for Priyadarshi Biplab Kumar [Opens in a new window]
Manoj Kumar Muni*
Affiliation:
Robotics Laboratory, Mechanical Engineering Department, National Institute of Technology Rourkela, Rourkela769008, Odisha, India. E-mails: manoj1986nitr@gmail.com, dayaldoc@yahoo.com
Dayal R. Parhi
Affiliation:
Robotics Laboratory, Mechanical Engineering Department, National Institute of Technology Rourkela, Rourkela769008, Odisha, India. E-mails: manoj1986nitr@gmail.com, dayaldoc@yahoo.com
Priyadarshi Biplab Kumar
Affiliation:
Mechanical Engineering Department, National Institute of Technology Hamirpur, Hamirpur177005, Himachal Pradesh, India. E-mail: p.biplabkumar@gmail.com
*
*Corresponding author. E-mail: manoj1986nitr@gmail.com
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Summary

This paper emphasizes on Bacterial Foraging Optimization Algorithm for effective and efficient navigation of humanoid NAO, which uses the foraging quality of bacteria Escherichia coli for getting shortest path between two locations in minimum time. The Gaussian cost function assigned to both attractant and repellent profile of bacterium performs a major role in obtaining the best path between any two locations. Mathematical formulations have been performed to design the control architecture for humanoid navigation using the proposed methodology. The developed approach has been tested in a simulation platform, and the simulation results have been validated in an experimental platform. Here, motion planning for both single and multiple humanoid robots on a common platform has been performed by integrating a petri-net architecture for multiple humanoid navigation. Finally, the results obtained from both the platforms are compared in terms of suitable navigational parameters, and proper agreements have been observed with minimal amount of error limits.

Keywords

Humanoid NAOBFOAPath planningPetri-net
Type
Articles
Information
Robotica , Volume 39 , Issue 1 , January 2021 , pp. 123 - 136
Copyright
Copyright © The Author(s) 2020. Published by Cambridge University Press

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