Online needle-tissue interaction model identification for force feedback enhancement in robot-assisted interventional procedures

2021 ◽  
pp. 1-1
Author(s):  
Marco Ferro ◽  
Claudio Gaz ◽  
Michele Anzidei ◽  
Marilena Vendittelli
Keyword(s):  
Interventional Procedures ◽  
Force Feedback ◽  
Model Identification ◽  
Interaction Model ◽  
Robot Assisted ◽  
Tissue Interaction ◽  
Feedback Enhancement

scholarly journals Estimating Tool–Tissue Forces Using a 3-Degree-of-Freedom Robotic Surgical Tool

2016 ◽  
Vol 8 (5) ◽  
Author(s):  
Baoliang Zhao ◽  
Carl A. Nelson
Keyword(s):  
Force Feedback ◽  
Estimation Method ◽  
Interaction Force ◽  
Normal Function ◽  
Degree Of Freedom ◽  
Force Estimation ◽  
Robot Assisted ◽  
Interaction Forces ◽  
Tissue Interaction ◽  
Surgical Tool

Robot-assisted minimally invasive surgery (MIS) has gained popularity due to its high dexterity and reduced invasiveness to the patient; however, due to the loss of direct touch of the surgical site, surgeons may be prone to exert larger forces and cause tissue damage. To quantify tool–tissue interaction forces, researchers have tried to attach different kinds of sensors on the surgical tools. This sensor attachment generally makes the tools bulky and/or unduly expensive and may hinder the normal function of the tools; it is also unlikely that these sensors can survive harsh sterilization processes. This paper investigates an alternative method by estimating tool–tissue interaction forces using driving motors' current, and validates this sensorless force estimation method on a 3-degree-of-freedom (DOF) robotic surgical grasper prototype. The results show that the performance of this method is acceptable with regard to latency and accuracy. With this tool–tissue interaction force estimation method, it is possible to implement force feedback on existing robotic surgical systems without any sensors. This may allow a haptic surgical robot which is compatible with existing sterilization methods and surgical procedures, so that the surgeon can obtain tool–tissue interaction forces in real time, thereby increasing surgical efficiency and safety.

scholarly journals A Magnetorheological Fluids-Based Robot-Assisted Catheter/Guidewire Surgery System for Endovascular Catheterization

Micromachines ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 640
Author(s):  
Linshuai Zhang ◽  
Shuoxin Gu ◽  
Shuxiang Guo ◽  
Takashi Tamiya
Keyword(s):  
Collision Detection ◽  
Endovascular Procedures ◽  
Force Feedback ◽  
Force Measurement ◽  
Occupational Hazards ◽  
Robot Assisted ◽  
X Ray ◽  
Surgical Safety ◽  
Surgical Tool ◽  
Haptic Cues

A teleoperated robotic catheter operating system is a solution to avoid occupational hazards caused by repeated exposure radiation of the surgeon to X-ray during the endovascular procedures. However, inadequate force feedback and collision detection while teleoperating surgical tools elevate the risk of endovascular procedures. Moreover, surgeons cannot control the force of the catheter/guidewire within a proper range, and thus the risk of blood vessel damage will increase. In this paper, a magnetorheological fluid (MR)-based robot-assisted catheter/guidewire surgery system has been developed, which uses the surgeon’s natural manipulation skills acquired through experience and uses haptic cues to generate collision detection to ensure surgical safety. We present tests for the performance evaluation regarding the teleoperation, the force measurement, and the collision detection with haptic cues. Results show that the system can track the desired position of the surgical tool and detect the relevant force event at the catheter. In addition, this method can more readily enable surgeons to distinguish whether the proximal force exceeds or meets the safety threshold of blood vessels.

scholarly journals Nonlinear Force Feedback Enhancement for Cooperative Robotic Neurosurgery Enforces Virtual Boundaries on Cortex Surface

2016 ◽  
Vol 01 (02) ◽  
pp. 1650001 ◽  
Author(s):  
Elisa Beretta ◽  
Giancarlo Ferrigno ◽  
Elena De Momi
Keyword(s):  
Soft Tissues ◽  
Force Feedback ◽  
System Stability ◽  
Hand Tremor ◽  
Nonlinear Force ◽  
Feedback Enhancement ◽  
Safety Features ◽  
Robotic Neurosurgery ◽  
Robotic Assistant ◽  
Future Work

Surgeons can benefit from the cooperation with a robotic assistant during the repetitive execution of precise targeting tasks on soft tissues, such as brain cortex stimulation procedures in open-skull neurosurgery. Position-based force-to-motion control schemes may not be satisfactory solution to provide the manipulator with the high compliance desirable during guidance along wide trajectories. A new torque controller with nonlinear force feedback enhancement (FFE) is presented to provide augmented haptic perception to the operator from instrument-tissue interaction. Simulation tests were performed to evaluate the system stability according to different nonlinear force modulation functions (power, sigmoidal and arc tangent). The FFE controller with power modulation was experimentally validated with a pool of nonexpert users using brain-mimicking gelatin phantoms (8–16% concentration). Besides providing hand tremor rejection for a stable holding of the tool, the FFE controller was proven to allow for a safer tissue contact with respect to both robotic assistance without force feedback and freehand executions (50% and 75% reduction of the indentation depth, respectively). Future work will address the evaluation of the safety features of the FFE controller with expert surgeons on a realistic brain phantom, also accounting for unpredictable tissue motions as during seizures due to cortex stimulation.

Real-Time 2D Surface Profile Mapping of Biological Tissue with Force Feedback in Robot-Assisted Minimally Invasive Surgery

2015 ◽  
Vol 798 ◽  
pp. 319-323
Author(s):  
Ali Reza Hassan Beiglou ◽  
Javad Dargahi
Keyword(s):  
Minimally Invasive Surgery ◽  
Minimally Invasive ◽  
Real Time ◽  
Invasive Surgery ◽  
Strain Gauge ◽  
Force Feedback ◽  
Surface Profile ◽  
Robot Assisted ◽  
Contact Points ◽  
Force Signal

It has been more than 20 years that robot-assisted minimally invasive surgery (RMIS) has brought remarkable accuracy and dexterity for surgeons along with the decreasing trauma for the patients. In this paper a novel method of the tissue’s surface profile mapping is proposed. The tissue surface profile plays an important role for material identification during RMIS. It is shown how by integrating the force feedback into robot controller the surface profile of the tissue can be obtained with force feedback scanning. The experiment setup includes a 5 degree of freedoms (DOFs) robot which is equipped with a strain-gauge ball caster as the force feedback. Robot joint encoders signals and the captured force signal of the strain-gauge are transferred to developed surface transformation algorithm (STA). The real-time geometrical transformation process is triggered with force signal to identify contact points between the ball caster and the artificial tissue. The 2D surface profile of tissue will be mapped based on these contact points. Real-time capability of the proposed system is evaluated experimentally for the artifical tissues in a designed test rig.

scholarly journals A Vision-Based Approach for Estimating Contact Forces: Applications to Robot-Assisted Surgery

2005 ◽  
Vol 2 (1) ◽  
pp. 53-60 ◽  
Author(s):  
C. W. Kennedy ◽  
J. P. Desai
Keyword(s):  
Finite Element ◽  
Finite Element Model ◽  
Real Time ◽  
Force Feedback ◽  
Element Model ◽  
Robot Arm ◽  
Robot Assisted ◽  
Interaction Forces ◽  
Fem Model ◽  
Nodal Displacements

The primary goal of this paper is to provide force feedback to the user using vision-based techniques. The approach presented in this paper can be used to provide force feedback to the surgeon for robot-assisted procedures. As proof of concept, we have developed a linear elastic finite element model (FEM) of a rubber membrane whereby the nodal displacements of the membrane points are measured using vision. These nodal displacements are the input into our finite element model. In the first experiment, we track the deformation of the membrane in real-time through stereovision and compare it with the actual deformation computed through forward kinematics of the robot arm. On the basis of accurate deformation estimation through vision, we test the physical model of a membrane developed through finite element techniques. The FEM model accurately reflects the interaction forces on the user console when the interaction forces of the robot arm with the membrane are compared with those experienced by the surgeon on the console through the force feedback device. In the second experiment, the PHANToM haptic interface device is used to control the Mitsubishi PA-10 robot arm and interact with the membrane in real-time. Image data obtained through vision of the deformation of the membrane is used as the displacement input for the FEM model to compute the local interaction forces which are then displayed on the user console for providing force feedback and hence closing the loop.

Sign in / Sign up

Export Citation Format

Share Document