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Complex Morphology Neural Network Simulation in Evolutionary Robotics.

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Title: Complex Morphology Neural Network Simulation in Evolutionary Robotics.
Authors: Woodford, Grant W.1 (AUTHOR) mc.duplessis@mandela.ac.za, du Plessis, Mathys C.1 (AUTHOR)
Source: Robotica. May2020, Vol. 38 Issue 5, p886-902. 17p.
Subjects: Robotics, Robot dynamics, Artificial neural networks, Robot kinematics, Morphology
Abstract: SUMMARY: This paper investigates artificial neural network (ANN)-based simulators as an alternative to physics-based approaches for evolving controllers in simulation for a complex snake-like robot. Prior research has been limited to robots or controllers that are relatively simple. Benchmarks are performed in order to identify effective simulator topologies. Additionally, various controller evolution strategies are proposed, investigated and compared. Using ANN-based simulators for controller fitness estimation during controller evolution is demonstrated to be a viable approach for the high-dimensional problem specified in this work. [ABSTRACT FROM AUTHOR]
Copyright of Robotica is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 142473077
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: Complex Morphology Neural Network Simulation in Evolutionary Robotics.
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  Data: <searchLink fieldCode="AR" term="%22Woodford%2C+Grant+W%2E%22">Woodford, Grant W.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mc.duplessis@mandela.ac.za</i><br /><searchLink fieldCode="AR" term="%22du+Plessis%2C+Mathys+C%2E%22">du Plessis, Mathys C.</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Robotica%22">Robotica</searchLink>. May2020, Vol. 38 Issue 5, p886-902. 17p.
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  Data: <searchLink fieldCode="DE" term="%22Robotics%22">Robotics</searchLink><br /><searchLink fieldCode="DE" term="%22Robot+dynamics%22">Robot dynamics</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Robot+kinematics%22">Robot kinematics</searchLink><br /><searchLink fieldCode="DE" term="%22Morphology%22">Morphology</searchLink>
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  Label: Abstract
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  Data: SUMMARY: This paper investigates artificial neural network (ANN)-based simulators as an alternative to physics-based approaches for evolving controllers in simulation for a complex snake-like robot. Prior research has been limited to robots or controllers that are relatively simple. Benchmarks are performed in order to identify effective simulator topologies. Additionally, various controller evolution strategies are proposed, investigated and compared. Using ANN-based simulators for controller fitness estimation during controller evolution is demonstrated to be a viable approach for the high-dimensional problem specified in this work. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Robotica is the property of Cambridge University Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1017/S0263574719001140
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      – Code: eng
        Text: English
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        PageCount: 17
        StartPage: 886
    Subjects:
      – SubjectFull: Robotics
        Type: general
      – SubjectFull: Robot dynamics
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Robot kinematics
        Type: general
      – SubjectFull: Morphology
        Type: general
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      – TitleFull: Complex Morphology Neural Network Simulation in Evolutionary Robotics.
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            NameFull: Woodford, Grant W.
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            NameFull: du Plessis, Mathys C.
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              Text: May2020
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