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Continuous trajectory planning based on learning optimization in high dimensional input space for serial manipulators.

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Title: Continuous trajectory planning based on learning optimization in high dimensional input space for serial manipulators.
Authors: Zhang, Shiyu1 (AUTHOR), Dai, Shuling1 (AUTHOR), Zhao, Yongjia1 (AUTHOR) zhaoyongjia@buaa.edu.cn
Source: Engineering Optimization. Oct2022, Vol. 54 Issue 10, p1724-1742. 19p.
Subjects: Haptic devices, Machine learning, Degrees of freedom, Virtual reality, Human-robot interaction
Abstract: In order to generate trajectories continuously for serial manipulators with high dimensional degrees of freedom (DOFs) in a dynamic environment, a real-time trajectory planning method based on optimization and machine learning aimed at high dimensional inputs is presented. A learning optimization (LO) framework is established. Multiple criteria are defined to evaluate the performance quantitatively, and implementations with different sub-methods are discussed. In particular, a database generation method based on input space mapping is proposed for generating valid and representative samples. The methods presented are applied on a practical application—haptic interaction in virtual reality systems. The results show that the input space mapping method significantly elevates the efficiency and quality of database generation and consequently improves the performance of the LO. With the LO method, real-time trajectory generation with high dimensional inputs is achieved, which lays the foundation for robots with high dimensional DOFs to execute complex tasks in dynamic environments. [ABSTRACT FROM AUTHOR]
Copyright of Engineering Optimization is the property of Taylor & Francis Ltd 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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DbLabel: Engineering Source
An: 159023043
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PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Continuous trajectory planning based on learning optimization in high dimensional input space for serial manipulators.
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  Data: <searchLink fieldCode="AR" term="%22Zhang%2C+Shiyu%22">Zhang, Shiyu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Dai%2C+Shuling%22">Dai, Shuling</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhao%2C+Yongjia%22">Zhao, Yongjia</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> zhaoyongjia@buaa.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Engineering+Optimization%22">Engineering Optimization</searchLink>. Oct2022, Vol. 54 Issue 10, p1724-1742. 19p.
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  Data: <searchLink fieldCode="DE" term="%22Haptic+devices%22">Haptic devices</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Degrees+of+freedom%22">Degrees of freedom</searchLink><br /><searchLink fieldCode="DE" term="%22Virtual+reality%22">Virtual reality</searchLink><br /><searchLink fieldCode="DE" term="%22Human-robot+interaction%22">Human-robot interaction</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: In order to generate trajectories continuously for serial manipulators with high dimensional degrees of freedom (DOFs) in a dynamic environment, a real-time trajectory planning method based on optimization and machine learning aimed at high dimensional inputs is presented. A learning optimization (LO) framework is established. Multiple criteria are defined to evaluate the performance quantitatively, and implementations with different sub-methods are discussed. In particular, a database generation method based on input space mapping is proposed for generating valid and representative samples. The methods presented are applied on a practical application—haptic interaction in virtual reality systems. The results show that the input space mapping method significantly elevates the efficiency and quality of database generation and consequently improves the performance of the LO. With the LO method, real-time trajectory generation with high dimensional inputs is achieved, which lays the foundation for robots with high dimensional DOFs to execute complex tasks in dynamic environments. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Engineering Optimization is the property of Taylor & Francis Ltd 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:
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      – Type: doi
        Value: 10.1080/0305215X.2021.1958210
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      – Code: eng
        Text: English
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        PageCount: 19
        StartPage: 1724
    Subjects:
      – SubjectFull: Haptic devices
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Degrees of freedom
        Type: general
      – SubjectFull: Virtual reality
        Type: general
      – SubjectFull: Human-robot interaction
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      – TitleFull: Continuous trajectory planning based on learning optimization in high dimensional input space for serial manipulators.
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            NameFull: Zhang, Shiyu
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            NameFull: Dai, Shuling
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            NameFull: Zhao, Yongjia
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            – D: 01
              M: 10
              Text: Oct2022
              Type: published
              Y: 2022
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              Value: 54
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            – TitleFull: Engineering Optimization
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