Multilevel Fuzzy Control Based on Force Information in Robot-Assisted Decompressive Laminectomy

  • Xiaozhi Qi
  • Yu Sun
  • Xiaohang Ma
  • Ying HuEmail author
  • Jianwei Zhang
  • Wei Tian
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 1093)


The lumbar spinal stenosis (LSS) is a kind of orthopedic disease which causes a series of neurological symptom. Vertebral lamina grinding operation is a key procedure in decompressive laminectomy for LSS treatment. With the help of image-guided navigation system, the robot-assisted technology is applied to reduce the burdens on surgeon and improve the accuracy of the operation. This paper proposes a multilevel fuzzy control based on force information in the robot-assisted decompressive laminectomy to improve the quality and the robotic dynamic performance in surgical operation. The controlled grinding path is planned in the medical images after 3D reconstruction, and the mapping between robot and images is realized by navigation registration. Multilevel fuzzy controller is used to adjust the feed rate to keep the grinding force stable. As the vertebral lamina contains different components according to the anatomy, it has different mechanical properties as the main reason causing the fluctuation of force. A feature extraction method for texture recognition of bone is introduced to improve the accuracy of component classification. When the inner cortical bone is reached, the feeding operation needs to stop to avoid penetration into spinal cord and damage to the spinal nerves. Experiments are conducted to evaluate the dynamic stabilities of the control system and state recognition.


Decompressive laminectomy Surgical robot Multilevel fuzzy control State recognition 



This work is financially supported by the National Natural Science Foundation of China (Grant Nos. U1613224, U1713221 and 61573336) and the National Key R&D Program of China (Grant No. 2017YFC0110600), in part by Shenzhen Fundamental Research Funds (Grant Nos. JCYJ20150529143500954, JCYJ20160608153218487, JCYJ20170307170252420 and JCYJ20160229202315086) and Shenzhen Key Laboratory Project (Grant No. ZDSYS201707271637577).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Xiaozhi Qi
    • 1
  • Yu Sun
    • 1
  • Xiaohang Ma
    • 1
  • Ying Hu
    • 1
    Email author
  • Jianwei Zhang
    • 2
  • Wei Tian
    • 3
  1. 1.Shenzhen Key Laboratory of Minimally Invasive Surgical Robotics and SystemShenzhen Institutes of Advanced Technology, Chinese Academy of SciencesShenzhenChina
  2. 2.University of HamburgHamburgGermany
  3. 3.Beijing Jishuitan HospitalBeijingChina

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