• DocumentCode
    2295565
  • Title

    Research on Obstacle-Avoidance Control Algorithm of Rehabilitation Robot Combined with Neural Network

  • Author

    Wang Tong ; Cao Cheng ; Guo Hongche

  • Author_Institution
    Shenyang Univ. of Technol., Shenyang, China
  • Volume
    3
  • fYear
    2010
  • fDate
    13-14 March 2010
  • Firstpage
    128
  • Lastpage
    131
  • Abstract
    This paper proposes an obstacle-avoidance algorithm which can be applied to omnidirectional Lower Limbs Rehabilitation Robot. The method generates the attribute of obstacle scene using detected angle information of obstacle boundary, and fuzzy matching with the obstacle scene knowledgebase to realize the obstacle-avoidance control of robot. The implementation of fuzzy matching depends on fuzzy neural network. The neural network enhances self learning ability, greatly simplifies data calculation. Obstacle-avoidance algorithm makes the rehabilitation robot more safety for human using and reduces environmental using demand. This paper proves the feasibility and effectiveness of the obstacle-avoidance algorithm through theoretical derivation and simulation results.
  • Keywords
    collision avoidance; fuzzy neural nets; fuzzy set theory; medical robotics; neurocontrollers; patient rehabilitation; service robots; data calculation; fuzzy matching; neural network; obstacle-avoidance control algorithm; omnidirectional lower limbs rehabilitation robot; rehabilitation robot; Fuzzy control; Fuzzy neural networks; Layout; Medical robotics; Mobile robots; Neural networks; Paper technology; Rehabilitation robotics; Robotics and automation; Wheels; fuzzy matching; neural network; obstacle-avoidance control; omnidirectional Lower Limbs Rehabilitation Robot;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Measuring Technology and Mechatronics Automation (ICMTMA), 2010 International Conference on
  • Conference_Location
    Changsha City
  • Print_ISBN
    978-1-4244-5001-5
  • Electronic_ISBN
    978-1-4244-5739-7
  • Type

    conf

  • DOI
    10.1109/ICMTMA.2010.204
  • Filename
    5459590