Abstract
In teleoperated surgery, the transmission of force feedback from the remote environment to the surgeon at the local site requires the availability of reliable force information in the system. In general, a force sensor is mounted between the slave end-effector and the tool for measuring the interaction forces generated at the remote sites. Such as the acquired force value includes not only the interaction force but also the tool gravity. This paper presents a neural network (NN) enhanced robot tool identification and calibration for bilateral teleoperation. The goal of this experimental study is to implement and validate two different techniques for tool gravity identification using Curve Fitting (CF) and Artificial Neural Networks (ANNs), separately. After tool identification, calibration of multi-axis force sensor based on Singular Value Decomposition (SVD) approach is introduced for alignment of the forces acquired from the force sensor and acquired from the robot. Finally, a bilateral teleoperation experiment is demonstrated using a serial robot (LWR4+, KUKA, Germany) and a haptic manipulator (SIGMA 7, Force Dimension, Switzerland). Results demonstrated that the calibration of the force sensor after identifying tool gravity component by using ANN shows promising performance than using CF. Additionally, the transparency of the system was demonstrated using the force and position tracking between the master and slave manipulators.
Highlights
Teleoperation indicates the remote control of a slave manipulator by a human operator at a remote site [1]
WORK In this paper, the Artificial Neural Networks (ANNs) model enhanced method is presented for surgical tool gravity identification and force sensor calibration in bilateral teleoperation with force sensing
The tool gravity force was identified by Curve Fitting (CF) and ANNs, separately
Summary
Teleoperation indicates the remote control of a slave manipulator by a human operator at a remote site [1]. Its application has been popular in various areas, and a substantial recent advantage is provided by medical applications such as Robot-assisted Minimally Invasive Surgery (RA-MIS). Bilateral teleoperation draws many research interests because it provides haptic feedback for the surgeon, which can ease and improve the surgical tasks performing, for example, enhancing surgical accuracy [2], optimizing dexterity and minimizing the trauma of the patient [3], [4], etc. The lack of haptic feedback in teleoperated surgical tasks could lead to some adverse effects such as tissue damage, and the operational procedure can be time-consuming. Achieving accurate force sensing on the remote site is of vital importance for bilateral teleoperation in the robot-assisted surgery using adaptive compensation [9]–[11]
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