• DocumentCode
    2116195
  • Title

    Statistical modeling and design issues of a crossbeam sensor

  • Author

    Wang, Xiao-Gang ; Shen, H.C. ; Moallem, M.

  • Volume
    3
  • fYear
    2001
  • fDate
    2001
  • Firstpage
    1452
  • Abstract
    The basic idea of RISC (reduced intricacy sensing and control) robotics is an attempt to perform challenging industrial manufacturing tasks by using a combination of simple hardware and sophisticated algorithms. Many effective strategies and algorithms have been explored. However, the issue of optimal design of RISC sensor has not been solved. The main reason is the shortage of good models. In this paper, we propose a statistical model for one of the typical RISC sensors, i.e. the crossbeam sensor. Based on this statistical model we employ the multiple hypotheses-testing method as optimal design technique and present its applied strategies. It is believed that this work will lead to development of new RISC sensors because a new principle and a pertinent model are introduced into this area
  • Keywords
    industrial robots; optical sensors; optimisation; statistical analysis; RISC robotics; crossbeam sensor design issues; industrial manufacturing tasks; multiple hypotheses-testing method; optimal design technique; reduced intricacy control; reduced intricacy sensing; statistical modeling; Density functional theory; Hardware; Manufacturing industries; Object recognition; Optical arrays; Reduced instruction set computing; Robot control; Robot sensing systems; Sensor arrays; Service robots;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Robots and Systems, 2001. Proceedings. 2001 IEEE/RSJ International Conference on
  • Conference_Location
    Maui, HI
  • Print_ISBN
    0-7803-6612-3
  • Type

    conf

  • DOI
    10.1109/IROS.2001.977185
  • Filename
    977185