Unscented Kalman Filter and 3D vision to improve cable driven surgical robot joint angle estimation

Abstract

Cable driven manipulators are popular in surgical robots due to compact design, low inertia, and remote actuation. In these manipulators, encoders are usually mounted on the motor, and joint angles are estimated based on transmission kinematics. However, due to non-linear properties of cables such as cable stretch, lower stiffness, and uncertainties in kinematic model parameters, the precision of joint angle estimation is limited with transmission kinematics approach. To improve the positioning of these manipulators, we use a pair of low cost stereo camera as the observation for joint angles and we input these noisy measurements into an Unscented Kalman Filter (UKF) for state estimation. We use the dual UKF to estimate cable parameters and states offline. We evaluated the effectiveness of the proposed method on a Raven-II experimental surgical research platform. Additional encoders at the joint output were employed as a reference system. From the experiments, the UKF improved the accuracy of joint angle estimation by 33-72%. Also, we tested the reliability of state estimation under camera occlusion. We found that when the system dynamics is tuned with offline UKF parameter estimation, the camera occlusion has no effect on the online state estimation.

UKF-Based Motion Estimation of Cable-Driven Forceps for Robot-Assisted Surgical System

“…Since the motion estimation using the LSM is a linear identification process, the nonlinear characteristics of the cabledriven system cannot be presented. After (29) is performed, the identification error is modeled as an approximation to compensate the system nonlinearity, and then the motion estimation model of compensated LSM can be obtained:…”

“…However, the linear identification model has limitations in the presentation of the nonlinear characteristics of cabledriven systems. In order to approximate the system nonlinearity, the Unscented Kalman Filter (UKF) method was adopted to explore the motion estimation of cable-driven end joints [26], [27], and further implemented the motion control and external forces estimation [28], [29]. These studies have revealed that the joint motion estimation is critical for the high-precision motion control of surgical instruments when there are no sensors installed on the end joints or slender tool shaft.…”

Gaussian Process Regression for Sensorless Grip Force Estimation of Cable-Driven Elongated Surgical Instruments

“…Therefore, the feature space formed by these variables should be better than the space formed only by q m and Γ. Consequently, the estimation precision should be improved[17], [21]. However, the joint position and velocity are not observed in the process due to the lack of external sensors, and their estimation depend on cable properties[14], [22]. As one of the goals of this work is to explore the possibility of avoiding manually tuning cable parameters, we consider q l and ${\dot{q}}_l$ as unavailable.…”

Dynamic modeling of cable driven elongated surgical instruments for sensorless grip force estimation

“…15. Although we do not have the ground truth for positions and velocities in this study, the dynamic modelbased UKF for position estimation is reviewed in [15], [25]. …”