Autonomous multilateral debridement with the Raven surgical robot

Kehoe, Ben; Kahn, Gregory; Mahler, Jeffrey; Kim, Jonathan; Lee, Alex X.; Lee, Anna; Nakagawa, Keisuke; Patil, Sachin; Boyd, W. Douglas; Abbeel, Pieter; Goldberg, Ken

doi:10.1109/icra.2014.6907040

Cited by 112 publications

(80 citation statements)

References 40 publications

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“…We also evaluate the accuracy and repeatability of reaching desired states using our correction method to modify commands sent to the robots and executing the commands in open-loop. Using this correction function, we achieve a 3.8× speedup over previous work on autonomous surgical debridement [12].…”

Section: Introductionsupporting

confidence: 54%

“…We train the learning controller on recorded trajectories of our two-arm Raven surgical robot system as it autonomously executes surgical debridement following the setup of Kehoe et al, where the goal is to find, grasp, and transport "damaged tissue" fragments [12]. Our results show that including velocity as a feature in GPR reduces the norm of the position error by 50% and the norm of the angular error by 19% on these recorded trajectories.…”

Section: Introductionmentioning

confidence: 77%

“…In prior work [12], the robot had to replan its trajectory at a fixed interval to account for inaccuracies in the kinematics. We found that the replanning interval was no longer necessary to complete the task due to our accuracy of only a few millimeters with open-loop control as reported in Section V-C.…”

Section: Debridement Task Speedupmentioning

confidence: 99%

“…3). The Raven performed a surgical debridement task, in which the goal is to find, grasp, and transport "damaged tissue" fragments [12], and we represented the tissue fragments with pieces of red foam. The locations of the red foam were determined with a stereo camera setup.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…slave teleoperation and have non-linearities due to cable elasticity and tensioning, is one example of a scenario with these challenges [12].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionsupporting

confidence: 54%

Section: Introductionmentioning

confidence: 77%

Section: Debridement Task Speedupmentioning

confidence: 99%

Section: A Experimental Setupmentioning

confidence: 99%

“…slave teleoperation and have non-linearities due to cable elasticity and tensioning, is one example of a scenario with these challenges [12].…”

Section: Introductionmentioning

confidence: 99%

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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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Advanced User Interfaces for Teleoperated Surgical Robotic Systems

Nguyễn

Wong

Thai

et al. 2023

Advanced Sensor Research

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In recent years, advances in modern technology have altered the practice of surgery from open to minimally invasive surgery (MIS) aided by robots. Teleoperated surgical robotic systems (TSRSs) provide numerous significant benefits for MIS over traditional approaches, including improved safety, more efficient and precise surgery, better cosmesis, shorter recovery time, and reduced postoperative pain. Existing TSRSs' master consoles, with improvements in vision systems, designs, and control methods, have significantly enhanced human-robot interactions, resulting in safer and more accurate medical intervention operations. Despite advances, haptic technologies, including sensors, machine assistance, and intuitive devices for user interfaces, are still limited, resulting in less effective usage of TSRSs for surgical operations. This review presents a summary of the emerging TSRSs with a focus on their user interfaces. In addition, advanced sensing, haptic, smart garments, and medical image artificial intelligence (AI) assistance technologies are shown with their potential for use in master consoles of the TSRSs are shown. Finally, a discussion on the need for a smart human-robot interface for TSRSs is given.

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Development of a novel intelligent laparoscope system for semi‐automatic minimally invasive surgery

Sun

Pan²,

Fu³

et al. 2019

Robotics Computer Surgery

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Background: Intelligent surgical robot has great significance to alleviate the fatigue of surgeons. In the minimally invasive surgery robot system, adding intelligent control method to the laparoscope control has great realizability and significance.Methods: Depth independent image Jacobian matrix was modified to make it suitable for laparoscope trocar constraint. We propose a method for intelligent and autonomous adjustment of surgeon's surgical field of view, enabling it to track and predict the motion trajectory of surgical instruments. Results:The result of experiment shows that the proposed method could realize tracking the surgical instruments and adjusting the surgical field of view autonomously. In case of occlusion, motion trajectory of surgical instruments can be predicted.Conclusion: The intelligent laparoscope system could improve the intelligent level of surgical robot system. Given providing "a third hand" for the surgeon, the proposed system is a highly improvement for semi-autonomous surgical robot system. K E Y W O R D Slaparoscope system, semi-automatic surgery, surgical robot

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Autonomous multilateral debridement with the Raven surgical robot

Cited by 112 publications

References 40 publications

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

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

Advanced User Interfaces for Teleoperated Surgical Robotic Systems

Development of a novel intelligent laparoscope system for semi‐automatic minimally invasive surgery

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