Simultaneous Robot-World, Sensor-Tip, and Kinematics Calibration of an Underactuated Robotic Hand with Soft Fingers
Ning Tan, Xiaoyi Gu, and Hongliang Ren Senior Member, IEEE
Abstract—Soft robotics is a research field growing rapidly with primary focuses on the prototype design, development of soft robots and their applications. Due to their highly deformable features, it is difficult to model and control such robots in a very precise compared with conventional rigid structured robots. Hence, the calibration and parameter identification problems of an underactuated robotic hand with soft fingers are important, but have not been investigated intensively. In this paper, we present a comparative study on the calibration of a soft robotic hand. The calibration problem is framed as an AX=YB problem with the partially known matrix A. The identifiability of the parameters is analyzed, and calibration methods based on nonlinear optimization (i.e., L-M method and interior-point method) and evolutionary computation (i.e., differential evolution) are presented. Extensive simulation tests are performed to examine the parameter identification using the three methods in a comparative way. The experiments are conducted on the real soft robotic-hand setup. The fitting, interpolating, and extrapolating errors are presented as well.
Index Terms—Soft robotics, calibration and identification, robotic hand, AX=YB, hand-eye calibration, tendon-driven robot
We presents a 5-DOF manipulator which consists of three parts, 1-DOF translational joint, a bendable skeleton (2-DOF for Omni-directional bending motion), and a rotatable forceps gripper (1-DOF for rotation, 1-DOF for opening/closing). The bendable segment in the manipulator achieves two orthogonal bending DOFs by pulling or pushing three parallel universal-joint-based shaft chains. Forward and inverse kinematics of the bendable skeleton is analyzed. The workspace calculation illustrates that the structure of the three parallel shaft chains can reach a bending angle of 90 degree in arbitrarily direction. The reachability of the manipulator is simulated in Adams. According to the surgical requirements, the manipulator is actuated to draw circle during the tests while the end effector is kept bending at 60 degree. The results show that the end effector can precisely track the planning trajectory (precision within 1 mm).
Q. Liu; J. CHEN; S. Shen; B. Zhang; M. G. Fujie; C. M. Lim & H. REN Design, Kinematics, Simulation of Omni-directional Bending Reachability for a Parallel Structure Forceps Manipulator BioRob2016, 6th IEEE RAS/EMBS International Conference on Biomedical Robotics and Biomechatronics, June 26-29, 2016, Singapore, 201
Automate Surgical Tasks for A Flexible Serpentine Manipulator via Learning Actuation Space Trajectory from Demonstration
Background: Accurate motion control of flexible surgical manipulators is crucial in tissue manipulation tasks. Tendon-driven serpentine manipulator (TSM) is one of the most widely adopted flexible mechanisms in MIS for its enhanced maneuverability in torturous environment. 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 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 state to the control inputs. Results: The performance of the three algorithms were evaluated both in simulation and physical trajectory tracking experiments. KNNR performs the best in the tracking experiments with the lowest RMSE of 2.1275mm. Conclusions: The proposed inverse kinematics learning methods provide an alternative and efficient way to accurately model the challenging tendon driven flexible manipulator. Keywords: Tendon-driven serpentine manipulator; surgical robotics; Inverse kinematics; Heuristic Methods
Demo video at:
W. Xu; J. Chen; H. Y. Lau & H. Ren Data-driven Methods towards Learning the Highly Nonlinear Inverse Kinematics of Tendon-driven Surgical Manipulators International Journal of Medical Robotics and Computer Assisted Surgery , 2016, 1-13
W. Xu; J. Chen; H. Y. Lau & H. Ren Automate Surgical Tasks for A Flexible Serpentine Manipulator via Learning Actuation Space Trajectory from Demonstration ICRA2016, IEEE International Conference on Robotics and Automation, 2016, –