Unscented Kalman Filter and 3D Vision to Improve Cable Driven Surgical Robot Joint Angle Estimation

Haghighipanah, Mohammad and Miyasaka, Muneaki and Li, Yangming and Hannaford, Blake (2016) Unscented Kalman Filter and 3D Vision to Improve Cable Driven Surgical Robot Joint Angle Estimation. In: 2016 IEEE International Conference on Robotics and Automation, Stockholm, Sweden.

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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.

Item Type: Conference or Workshop Item (Paper)
Subjects: C Surgical Robots > C Surgical Robots(General)
C Surgical Robots > CA Robotic Control
Divisions: Department of Electrical Engineering
Depositing User: Muneaki Miyasaka
Date Deposited: 23 Jun 2016 17:16
Last Modified: 23 Jun 2016 17:16
URI: http://brl.ee.washington.edu/eprints/id/eprint/291

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