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Compact Reachability Map for Excavator Motion Planning

  • Yajue Yang
  • , Liangjun Zhang
  • , Xinjing Cheng
  • , Jia Pan
  • , Ruigang Yang

Producción científica: Conference contributionrevisión exhaustiva

25 Citas (Scopus)

Resumen

In this paper, we propose a novel compact reachability map representation for excavator motion planning. The constructed reachability map can concisely encode the bucket's reachable pose and the translation capability limited by excavator's kinematic structure. By explicitly exploiting the property that the basic excavation motion lies on the excavation plane determined by excavator links, we further reduce the construction of the map from 3D Euclidean space to 2D excavation plane. We show the pre-computed reachability map can be used to develop new excavator motion planning approach. By indexing on the pre-computed reachability map, we can efficiently compute the feasible full-bucket trajectory for single step excavation operation. We highlight the results of the reachability map construction and demonstrate the simulation results of motion planning using a commercial dynamic simulator.

Idioma originalEnglish
Título de la publicación alojada2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
Páginas2308-2313
Número de páginas6
ISBN (versión digital)9781728140049
DOI
EstadoPublished - nov 2019
Evento2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019 - Macau, China
Duración: nov 3 2019nov 8 2019

Serie de la publicación

NombreIEEE International Conference on Intelligent Robots and Systems
ISSN (versión impresa)2153-0858
ISSN (versión digital)2153-0866

Conference

Conference2019 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2019
País/TerritorioChina
CiudadMacau
Período11/3/1911/8/19

Nota bibliográfica

Publisher Copyright:
© 2019 IEEE.

Financiación

*This work was supported by Baidu Inc. 1Y. Yang is with the City University of Hong Kong. [email protected] 2L. Zhang, X. Cheng, R. Yang are with the Robotics and Auto-Driving Lab, Baidu Research. liangjunzhang, chengxinjing, [email protected] 3J. Pan is with the University of Hong Kong. [email protected]

Financiadores
Baidu Inc

    ASJC Scopus subject areas

    • Control and Systems Engineering
    • Software
    • Computer Vision and Pattern Recognition
    • Computer Science Applications

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