Application of Unscented Kalman Filter to a cable driven surgical robot: A simulation study

Ramadurai, Srikrishnan; Kosari, Sina Nia; King, H. Hawkeye; Chizeck, H.J.; Hannaford, Blake

doi:10.1109/icra.2012.6224776

Cited by 12 publications

(6 citation statements)

References 13 publications

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“…As a result, even a small amount of slack or stretch in the cables can greatly increase the uncertainty in gripper pose. State estimation has previously been explored in simulation [25], but not in physical experiments. Fig.…”

Section: Hardware a Raven Surgical Robotmentioning

confidence: 99%

Autonomous multilateral debridement with the Raven surgical robot

Kehoe

Kahn

Mahler

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

110

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Abstract-Robotic surgical assistants (RSAs) enable surgeons to perform delicate and precise minimally invasive surgery. Currently these devices are primarily controlled by surgeons in a local tele-operation (master-slave) mode. Introducing autonomy of surgical sub-tasks has the potential to assist surgeons, reduce fatigue, and facilitate supervised autonomy for remote tele-surgery. This paper considers the sub-task of surgical debridement: removing dead or damaged tissue fragments to allow the remaining healthy tissue to heal. We present an implemented automated surgical debridement system that uses the Raven, an open-architecture surgical robot with two cabledriven 7 DOF arms. Our system combines stereo vision for 3D perception, trajopt, an optimization-based motion planner, and model predictive control (MPC). Experiments with autonomous sensing, grasping, and removal of over 100 fragments suggest that it is possible for an autonomous surgical robot to achieve robustness comparable to human levels for a surgically-relevant subtask, although for our current implementation, execution time is 2-3× slower than human levels, primarily due to replanning times for MPC. This paper provides three contributions: (i) introducing debridement as a surgically-relevant sub-task for robotics, (ii) designing and implementing an autonomous multilateral surgical debridement system that uses both arms of the Raven surgical robot, and (iii) providing experimental data that highlights the importance of accurate state estimation for future research.

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Section: Hardware a Raven Surgical Robotmentioning

confidence: 99%

Autonomous multilateral debridement with the Raven surgical robot

Kehoe

Kahn

Mahler

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

110

View full text Add to dashboard Cite

show abstract

“…Naerum et al considered both offline and online parameter estimation using the Unscented Kalman Filter [19] with an explicit model of a cabledriven 1-DOF system with motor angle measurements. This work was extended to the 7-DOF Raven Surgical robot in simulation, but the accuracy was sensitive to hand-tuned process noise estimates [24].…”

Section: Related Workmentioning

confidence: 99%

Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression

Mahler¹,

Krishnan²,

Laskey³

et al. 2014

2014 IEEE International Conference on Automation Science and Engineering (CASE)

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Abstract-Precise control of industrial automation systems with non-linear kinematics due to joint elasticity, variation in cable tensioning, or backlash is challenging; especially in systems that can only be controlled through an interface with an imprecise internal kinematic model. Cable-driven Robotic Surgical Assistants (RSAs) are one example of such an automation system, as they are designed for master-slave teleoperation. We consider the problem of learning a function to modify commands to the inaccurate control interface such that executing the modified command on the system results in a desired state. To achieve this, we must learn a mapping that accounts for the non-linearities in the kinematic chain that are not accounted for by the system's internal model. Gaussian Process Regression (GPR) is a data-driven technique that can estimate this non-linear correction in a task-specific region of state space, but it is sensitive to corruption of training examples due to partial occlusion or lighting changes. In this paper, we extend the use of GPR to learn a non-linear correction for cable-driven surgical robots by using i) velocity as a feature in the regression and ii) removing corrupted training observations based on rotation limits and the magnitude of velocity. We evaluate this approach on the Raven II Surgical Robot on the task of grasping foam "damaged tissue" fragments, using the PhaseSpace LED-based motion capture system to track the Raven end-effector. Our main result is a reduction in the norm of the mean position error from 2.6 cm to 0.2 cm and the norm of the mean angular error from 20.6 degrees to 2.8 degrees when correcting commands for a set of held-out trajectories. We also use the learned mapping to achieve a 3.8× speedup over past results on the task of autonomous surgical debridement. Further information on this research, including data, code, photos, and video, is available at http: //rll.berkeley.edu/surgical.

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“…It allows update of Q and R based on the time‐varying weighting calculated from the learned information during estimation and thus potentially provide better approximations for noise covariance matrices. Moreover, RM scheme has been recently used to optimize noise covariance matrices in UKF application in various disciplines such as robotics, image processing, and computer science . Consequently, in this section, update of the noise covariance Q and R are formulated using the RM stochastic approximation scheme.…”

Section: Methodsmentioning

confidence: 99%

A robust structural parameter estimation method using seismic response measurements

Kuleli

Nagayama

2019

Struct Control Health Monit

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Although successful applications of data assimilation techniques such as Kalman filter to structural parameter identification problems exist in literature, they have been explored mostly via simulations because real-world application poses challenges. Current practice is to set the process and measurement noise covariance matrices by manual tuning and keeping them constant over the estimation period. However, when the matrices are not correct, Kalman filters are prone to suboptimal performance and/ or even divergence. In this paper, use of a stochastic approximation scheme along with the unscented Kalman filter is explored. The sensitivity of the initial filter parameter selection has been investigated through both simulations and full-scale laboratory experiments. Results show that by automatically updating the filter statistics, system states, including the structural parameters, are successfully estimated with a fast convergence rate, good accuracy, and trackability in a stable and objective manner. The method is further applied to in situ seismic response records of a box-girder bridge. The stiffness values of the rubber bearings are successfully identified. KEYWORDSseismic response, stochastic approximation, structural parameter estimation, system identification, unscented Kalman filter Struct. Control Health Monit.. 2020;27:e2475. wileyonlinelibrary.com/journal/stc

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Application of Unscented Kalman Filter to a cable driven surgical robot: A simulation study

Cited by 12 publications

References 13 publications

Autonomous multilateral debridement with the Raven surgical robot

Autonomous multilateral debridement with the Raven surgical robot

Learning accurate kinematic control of cable-driven surgical robots using data cleaning and Gaussian Process Regression

A robust structural parameter estimation method using seismic response measurements

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