Concurrent controller and Simulator Neural Network development for a snake-like robot in Evolutionary Robotics.
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| Title: | Concurrent controller and Simulator Neural Network development for a snake-like robot in Evolutionary Robotics. |
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| Authors: | Woodford, Grant W.1 grant.woodford@nmmu.ac.za, du Plessis, Mathys C.1 mc.duplessis@nmmu.ac.za, Pretorius, Christiaan J.2 cpretorius@nmmu.ac.za |
| Source: | Robotics & Autonomous Systems. Feb2017, Vol. 88, p37-50. 14p. |
| Subjects: | Artificial neural networks, Robot design & construction, Evolutionary robotics, Robot control systems, Acquisition of data |
| Abstract: | Evolutionary Robotics (ER) is a field of study that has shown much promise in automating the development of robotic controllers and morphologies. The use of simulators as an alternative to real-world robots is often employed to reduce the time required to develop effective controllers in the ER process. However, the development of adequate simulators is often time-consuming and complex. Simulators are usually constructed from physics models and/or are based on empirically collected data. The vast majority of simulation approaches are based on physics models which can become complex and require specialised knowledge. Alternative simulation approaches that simplify and automate the modelling of real-world phenomena can provide certain advantages over traditional approaches. An alternative simulation approach, such as Artificial Neural Networks (ANNs) that model the real-world phenomena based on empirical data are relatively simple to construct and requires little specialised knowledge. ANN-based simulators are traditionally constructed before the ER process can begin and require the sampling of real-world experimental data. Disadvantages to the traditional approach to ANN-based simulator construction are that the simulator must be created before the ER process can be initiated and a large amount of behavioural data must be collected in order to accurately predict future behaviours. Previous research has successfully demonstrated that ANN-based simulators and controllers can be developed concurrently during the ER process for a simple differentially-steered wheeled robot, while the current research has demonstrated the concurrent approach using a complex snake-like robot. The viability of the concurrent approach was demonstrated on a real-world snake-like robot by performing trajectory planning tasks. Influential factors related to the success of the concurrent approach were also studied by investigating the effects various parameter settings had on success. [ABSTRACT FROM AUTHOR] |
| Copyright of Robotics & Autonomous Systems is the property of Elsevier B.V. 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 |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 120446312 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Concurrent controller and Simulator Neural Network development for a snake-like robot in Evolutionary Robotics. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Woodford%2C+Grant+W%2E%22">Woodford, Grant W.</searchLink><relatesTo>1</relatesTo><i> grant.woodford@nmmu.ac.za</i><br /><searchLink fieldCode="AR" term="%22du+Plessis%2C+Mathys+C%2E%22">du Plessis, Mathys C.</searchLink><relatesTo>1</relatesTo><i> mc.duplessis@nmmu.ac.za</i><br /><searchLink fieldCode="AR" term="%22Pretorius%2C+Christiaan+J%2E%22">Pretorius, Christiaan J.</searchLink><relatesTo>2</relatesTo><i> cpretorius@nmmu.ac.za</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Robotics+%26+Autonomous+Systems%22">Robotics & Autonomous Systems</searchLink>. Feb2017, Vol. 88, p37-50. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Robot+design+%26+construction%22">Robot design & construction</searchLink><br /><searchLink fieldCode="DE" term="%22Evolutionary+robotics%22">Evolutionary robotics</searchLink><br /><searchLink fieldCode="DE" term="%22Robot+control+systems%22">Robot control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Acquisition+of+data%22">Acquisition of data</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Evolutionary Robotics (ER) is a field of study that has shown much promise in automating the development of robotic controllers and morphologies. The use of simulators as an alternative to real-world robots is often employed to reduce the time required to develop effective controllers in the ER process. However, the development of adequate simulators is often time-consuming and complex. Simulators are usually constructed from physics models and/or are based on empirically collected data. The vast majority of simulation approaches are based on physics models which can become complex and require specialised knowledge. Alternative simulation approaches that simplify and automate the modelling of real-world phenomena can provide certain advantages over traditional approaches. An alternative simulation approach, such as Artificial Neural Networks (ANNs) that model the real-world phenomena based on empirical data are relatively simple to construct and requires little specialised knowledge. ANN-based simulators are traditionally constructed before the ER process can begin and require the sampling of real-world experimental data. Disadvantages to the traditional approach to ANN-based simulator construction are that the simulator must be created before the ER process can be initiated and a large amount of behavioural data must be collected in order to accurately predict future behaviours. Previous research has successfully demonstrated that ANN-based simulators and controllers can be developed concurrently during the ER process for a simple differentially-steered wheeled robot, while the current research has demonstrated the concurrent approach using a complex snake-like robot. The viability of the concurrent approach was demonstrated on a real-world snake-like robot by performing trajectory planning tasks. Influential factors related to the success of the concurrent approach were also studied by investigating the effects various parameter settings had on success. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Robotics & Autonomous Systems is the property of Elsevier B.V. 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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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.robot.2016.11.018 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 37 Subjects: – SubjectFull: Artificial neural networks Type: general – SubjectFull: Robot design & construction Type: general – SubjectFull: Evolutionary robotics Type: general – SubjectFull: Robot control systems Type: general – SubjectFull: Acquisition of data Type: general Titles: – TitleFull: Concurrent controller and Simulator Neural Network development for a snake-like robot in Evolutionary Robotics. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Woodford, Grant W. – PersonEntity: Name: NameFull: du Plessis, Mathys C. – PersonEntity: Name: NameFull: Pretorius, Christiaan J. IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2017 Type: published Y: 2017 Identifiers: – Type: issn-print Value: 09218890 Numbering: – Type: volume Value: 88 Titles: – TitleFull: Robotics & Autonomous Systems Type: main |
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