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Design of a hybrid controller using genetic algorithm and neural network for path planning of a humanoid robot

Design of a hybrid controller using genetic algorithm and neural network for path planning of a... To navigate humanoid robots in complex arenas, a significant level of intelligence is required which needs proper integration of computational intelligence with the robot's controller. This paper describes the use of a combination of genetic algorithm and neural network for navigational control of a humanoid robot in given cluttered environments.Design/methodology/approachThe experimental work involved in the current study has been done by a NAO humanoid robot in laboratory conditions and simulation work has been done by the help of V-REP software. Here, a genetic algorithm controller is first used to generate an initial turning angle for the robot and then the genetic algorithm controller is hybridized with a neural network controller to generate the final turning angle.FindingsFrom the simulation and experimental results, satisfactory agreements have been observed in terms of navigational parameters with minimal error limits that justify the proper working of the proposed hybrid controller.Originality/valueWith a lack of sufficient literature on humanoid navigation, the proposed hybrid controller is supposed to act as a guiding way towards the design and development of more robust controllers in the near future. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Intelligent Unmanned Systems Emerald Publishing

Design of a hybrid controller using genetic algorithm and neural network for path planning of a humanoid robot

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
2049-6427
DOI
10.1108/ijius-10-2019-0059
Publisher site
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Abstract

To navigate humanoid robots in complex arenas, a significant level of intelligence is required which needs proper integration of computational intelligence with the robot's controller. This paper describes the use of a combination of genetic algorithm and neural network for navigational control of a humanoid robot in given cluttered environments.Design/methodology/approachThe experimental work involved in the current study has been done by a NAO humanoid robot in laboratory conditions and simulation work has been done by the help of V-REP software. Here, a genetic algorithm controller is first used to generate an initial turning angle for the robot and then the genetic algorithm controller is hybridized with a neural network controller to generate the final turning angle.FindingsFrom the simulation and experimental results, satisfactory agreements have been observed in terms of navigational parameters with minimal error limits that justify the proper working of the proposed hybrid controller.Originality/valueWith a lack of sufficient literature on humanoid navigation, the proposed hybrid controller is supposed to act as a guiding way towards the design and development of more robust controllers in the near future.

Journal

International Journal of Intelligent Unmanned Systems – Emerald Publishing

Published: Jun 15, 2021

Keywords: GA; Neural network; Humanoid robot; Navigation

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