Dual-Arm Robotic Needle Insertion With Active Tissue Deformation for Autonomous Suturing

Zhong, Fangxun; Wang, Yaqing; Wang, Zerui; Liu, Yunhui

doi:10.1109/lra.2019.2913082

Cited by 75 publications

(38 citation statements)

References 30 publications

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“…Assume that the projection point s i of the visual feedback satisfies the following affine projection model near the feature point of the flexible tissue 20 :

s_{i} = M r_{i} + b \in R^{2}

…”

Section: Methodsmentioning

confidence: 99%

Design and control of a bionic needle puncture robot

Zhang

Liu

2020

Robotics Computer Surgery

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Background The application of minimally invasive interventional breast surgery is becoming more and more widespread. The accurate puncture of breast cancer needs to solve the problems of tissue deformation and target displacement. Methods In this study, we analysed the process of leech blood absorption and developed a robotic needle insertion method based on bionic technology to improve the accuracy of breast cancer diagnosis and treatment. Among them, the design purpose of the sucker manipulator is to adjust and fix the breast tissue. We use uncalibrated visual servo to control soft tissue deformation. Results We compare the puncture effect of bionic needle puncture robot and common needle puncture on breast prosthesis and in vitro tissue. Experimental data shows that, compared with ordinary needle insertion, the robotic needle insertion method based on bionic technology greatly reduces the targeting error. Conclusions This method is expected to provide a safe and effective alternative to traditional puncture for breast cancer diagnosis and treatment.

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“…Assume that the projection point s i of the visual feedback satisfies the following affine projection model near the feature point of the flexible tissue 20 :

s_{i} = M r_{i} + b \in R^{2}

…”

Section: Methodsmentioning

confidence: 99%

Design and control of a bionic needle puncture robot

Zhang

Liu

2020

Robotics Computer Surgery

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“…A combination of kinematic and geometric modelling is proposed in [72], where the trajectory is generated to minimize the tissue deformation. Estimation approaches such as Unscented Kalman Filter [73] and an online evaluated deformation matrix [74] were used to estimate the tissue and needle deformation.…”

Section: Needle Insertionmentioning

confidence: 99%

Autonomy in Surgical Robotics

Attanasio

Scaglioni

Momi

et al. 2021

Annu. Rev. Control Robot. Auton. Syst.

137

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This review examines the dichotomy between automatic and autonomous behaviors in surgical robots, maps the possible levels of autonomy of these robots, and describes the primary enabling technologies that are driving research in this field. It is organized in five main sections that cover increasing levels of autonomy. At level 0, where the bulk of commercial platforms are, the robot has no decision autonomy. At level 1, the robot can provide cognitive and physical assistance to the surgeon, while at level 2, it can autonomously perform a surgical task. Level 3 comes with conditional autonomy, enabling the robot to plan a task and update planning during execution. Finally, robots at level 4 can plan and execute a sequence of surgical tasks autonomously. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 3, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…for reference paths that change during the task execution). Free-space tracking is a first essential step towards more complicated tasks such as not-tying, suturing, or tumor resection Even though the surgical environment is weakly structured, it is still possible to estimate its evolution in time, for instance, by tracking tissue deformation [7], and thus still have some reference for tracking.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Neural Network Modeling and Hierarchical MPC for a Tendon-Driven Surgical Robot With Uncertainty Minimization

Cursi

Modugno

Lanari

et al. 2021

IEEE Robot. Autom. Lett.

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In order to guarantee precision and safety in robotic surgery, accurate models of the robot and proper control strategies are needed. Bayesian Neural Networks (BNN) are capable of learning complex models and provide information about the uncertainties of the learned system. Model Predictive Control (MPC) is a reliable control strategy to ensure optimality and satisfaction of safety constraints. In this work we propose the use of BNN to build the highly nonlinear kinematic and dynamic models of a tendon-driven surgical robot, and exploit the information about the epistemic uncertainties by means of a Hierarchical MPC (Hi-MPC) control strategy. Simulation and real world experiments show that the method is capable of ensuring accurate tip positioning, while satisfying imposed safety bounds on the kinematics and dynamics of the robot.

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Dual-Arm Robotic Needle Insertion With Active Tissue Deformation for Autonomous Suturing

Cited by 75 publications

References 30 publications

Design and control of a bionic needle puncture robot

Design and control of a bionic needle puncture robot

Autonomy in Surgical Robotics

Bayesian Neural Network Modeling and Hierarchical MPC for a Tendon-Driven Surgical Robot With Uncertainty Minimization

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