title: Data-driven methods towards learning the highly nonlinear inverse kinematics of tendon-driven surgical manipulators.
abstract: BACKGROUND
  Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. The tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in minimally invasive surgery because of its enhanced maneuverability in torturous environments. TSM, however, exhibits high nonlinearities and conventional analytical kinematics model is insufficient to achieve high accuracy.
  
  
  METHODS
  To account for the system nonlinearities, we applied a data driven approach to encode the system inverse kinematics. Three regression methods: extreme learning machine (ELM), Gaussian mixture regression (GMR) and K-nearest neighbors regression (KNNR) were implemented to learn a nonlinear mapping from the robot 3D position states to the control inputs.
  
  
  RESULTS
  The performance of the three algorithms was evaluated both in simulation and physical trajectory tracking experiments. KNNR performed the best in the tracking experiments, with the lowest RMSE of 2.1275 mm.
  
  
  CONCLUSIONS
  The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the tendon driven flexible manipulator.
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore.
authors.forename: Wenjun
authors.initials: W
authors.lastname: Xu
authors.affiliation: Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong SAR.
authors.forename: Jie
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Hong Kong SAR.
authors.forename: Henry Y K
authors.initials: HYK
authors.lastname: Lau
authors.affiliation: Department of Biomedical Engineering, National University of Singapore, Singapore.
authors.forename: Hongliang
authors.initials: H
authors.lastname: Ren
mesh_terms: D000465:Algorithms; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D004867:Equipment Design; D006801:Humans; D000069550:Machine Learning; D019060:Minimally Invasive Surgical Procedures / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data; D009038:Motion; D017711:Nonlinear Dynamics; D016011:Normal Distribution; D012044:Regression Analysis; D065287:Robotic Surgical Procedures / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data; D025321:Surgery, Computer-Assisted / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data; D013710:Tendons
keywords: data-driven methods; inverse kinematics; surgical robotics; tendon-driven serpentine manipulator