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Data‐driven methods towards learning the highly nonlinear inverse kinematics of tendon‐driven surgical manipulators
Author(s) -
Xu Wenjun,
Chen Jie,
Lau Henry Y.K.,
Ren Hongliang
Publication year - 2017
Publication title -
the international journal of medical robotics and computer assisted surgery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.556
H-Index - 53
eISSN - 1478-596X
pISSN - 1478-5951
DOI - 10.1002/rcs.1774
Subject(s) - inverse kinematics , kinematics , control theory (sociology) , computer science , nonlinear system , inverse dynamics , forward kinematics , trajectory , inverse , artificial intelligence , position (finance) , robot , mathematics , control (management) , physics , geometry , classical mechanics , quantum mechanics , finance , astronomy , economics
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.