casa 22(1): e2

Research Article

An Algorithmic Approach to Adapting Edge-based Devices for Autonomous Robotic Navigation

Download318 downloads
Cite
BibTeX Plain Text
  • @ARTICLE{10.4108/eai.5-8-2021.170559,
    author={Mbadiwe S. Benyeogor and Kosisochukwu P. Nnoli and Oladayo O. Olakanmi and Olusegun I. Lawal and Eric J. Gratton and Sushant Kumar and Kenneth A. Akpado and Piyal Saha},
    title={An Algorithmic Approach to Adapting Edge-based Devices for Autonomous Robotic Navigation},
    journal={EAI Endorsed Transactions on Context-aware Systems and Applications},
    volume={8},
    number={1},
    publisher={EAI},
    journal_a={CASA},
    year={2021},
    month={8},
    keywords={autonomous navigation, edge computing, intelligence schema, localization, obstacle avoidance},
    doi={10.4108/eai.5-8-2021.170559}
}
  • Mbadiwe S. Benyeogor
    Kosisochukwu P. Nnoli
    Oladayo O. Olakanmi
    Olusegun I. Lawal
    Eric J. Gratton
    Sushant Kumar
    Kenneth A. Akpado
    Piyal Saha
    Year: 2021
    An Algorithmic Approach to Adapting Edge-based Devices for Autonomous Robotic Navigation
    CASA
    EAI
    DOI: 10.4108/eai.5-8-2021.170559
Mbadiwe S. Benyeogor1,*, Kosisochukwu P. Nnoli2, Oladayo O. Olakanmi1, Olusegun I. Lawal3, Eric J. Gratton1, Sushant Kumar1, Kenneth A. Akpado4, Piyal Saha1
  • 1: Automata Research Group (ARG), OEMA Tools and Automation Ltd., Ibadan, Nigeria
  • 2: Jacobs University
  • 3: National Space Research and Development Agency
  • 4: Nnamdi Azikiwe University
*Contact email: samrexbenzil@gmail.com

Abstract

A recent significant progress has been made in development of intelligent mobile robots that is capable of autonomous navigation using an edge-computing system. This could sense changes in its environment to control its mechanical behavior towards accomplishing preprogrammed motions. Several algorithms were used in developing the robot’s control software. These include the moving average filter, the extended Kalman filter, and the covariance algorithm. Using these algorithms, the robot could learn from its sensors to estimate and control its position, velocity, and the proximity of obstacles along its path, while autonomously navigating to a predetermined location on the earth’s surface. Results show that our algorithmic approach to developing software systems for autonomous robots using edge-computing devices is viable, cost-efficient, and robust. Hence, our work is a proof of concept for the further development of edge-based intelligence and autonomous robots.

Keywords
autonomous navigation, edge computing, intelligence schema, localization, obstacle avoidance
Received
2021-06-04
Accepted
2021-06-26
Published
2021-08-05
Publisher
EAI
http://dx.doi.org/10.4108/eai.5-8-2021.170559

Copyright © 2021 M. S. Benyeogor et al., licensed to EAI. This is an open access article distributed under the terms of the Creative Commons Attribution license, which permits unlimited use, distribution and reproduction in any medium so long as the original work is properly cited.