HaptoClone (Haptic-Optical Clone) for Mutual Tele-Environment by Real-time 3D Image Transfer with Midair Force Feedback

Author(s):  
Yasutoshi Makino ◽  
Yoshikazu Furuyama ◽  
Seki Inoue ◽  
Hiroyuki Shinoda
Author(s):  
Fei Zheng ◽  
WenFeng Lu ◽  
Yoke San Wong ◽  
Kelvin Weng Chiong Foong

Dental bone drilling is an inexact and often a blind art. Dentist risks damaging the invisible tooth roots, nerves and critical dental structures like mandibular canal and maxillary sinus. This paper presents a haptics-based jawbone drilling simulator for novice surgeons. Through the real-time training of tactile sensations based on patient-specific data, improved outcomes and faster procedures can be provided. Previously developed drilling simulators usually adopt penalty-based contact force models and often consider only spherical-shaped drill bits for simplicity and computational efficiency. In contrast, our simulator is equipped with a more precise force model, adapted from the Voxmap-PointShell (VPS) method to capture the essential features of the drilling procedure. In addition, the proposed force model can accommodate various shapes of drill bits. To achieve better anatomical accuracy, our oral model has been reconstructed from Cone Beam CT, using voxel-based method. To enhance the real-time response, the parallel computing power of Graphics Processing Units is exploited through extra efforts for data structure design, algorithms parallelization, and graphic memory utilization. Preliminary results show that the developed system can produce appropriate force feedback at different tissue layers.


2015 ◽  
Vol 798 ◽  
pp. 319-323
Author(s):  
Ali Reza Hassan Beiglou ◽  
Javad Dargahi

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.


2005 ◽  
Vol 2 (1) ◽  
pp. 53-60 ◽  
Author(s):  
C. W. Kennedy ◽  
J. P. Desai

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.


Author(s):  
Jinling Wang ◽  
Wen F. Lu

Virtual reality technology plays an important role in the fields of product design, computer animation, medical simulation, cloth motion, and many others. Especially with the emergence of haptics technology, virtual simulation system provides an intuitive way of human and computer interaction, which allows user to feel and touch the virtual environment. For a real-time simulation system, a physically based deformable model including complex material properties with a high resolution is required. However, such deformable model hardly satisfies the update rate of interactive haptic rendering that exceeds 1 kHz. To tackle this challenge, a real-time volumetric model with haptic feedback is developed in this paper. This model, named as Adaptive S-chain model, extends the S-chain model and integrates the energy-based wave propagation method by the proposed adaptive re-mesh method to achieve realistic graphic and haptic deformation results. The implemented results show that the nonlinear, heterogeneous, anisotropic, shape retaining material properties and large range deformation are well modeled. An accurate force feedback is generated by the proposed Adaptive S-chain model in case study which is quite close to the experiment data.


1995 ◽  
Vol 73 (3) ◽  
pp. 1201-1222 ◽  
Author(s):  
J. McIntyre ◽  
E. V. Gurfinkel ◽  
M. I. Lipshits ◽  
J. Droulez ◽  
V. S. Gurfinkel

1. When interacting with the environment, human arm movements may be prevented in certain directions (i.e., when sliding the hand along a surface) resulting in what is called a "constrained motion." In the directions that the movement is restricted, the subject is instead free to control the forces against the constraint. 2. Control strategies for constrained motion may be characterized by two extreme models. Under the active compliance model, an essentially feedback-based approach, measurements of contact force may be used in real time to modify the motor command and precisely control the forces generated against the constraint. Under the passive compliance model the motion would be executed in a feedforward manner, using an internal model of the constraint geometry. The feedforward model relies on the compliant behavior of the passive mechanical system to maintain contact while avoiding excessive contact forces. 3. Subjects performed a task in which they were required to slide the hand along a rigid surface. This task was performed in a virtual force environment in which contact forces were simulated by a two-dimensional force-actuated joystick. Unknown to the subject, the orientation of the surface constraint was varied from trial to trial, and contact force changes induced by these perturbations were measured. 4. Subjects showed variations in contact force correlated with the direction of the orientation perturbation. "Upward" tilts resulted in higher contact forces, whereas "downward" tilts resulted in lower contact forces. This result is consistent with a feedforward-based control of a passively compliant system. 5. Subject responses did not, however, correspond exactly to the predictions of a static analysis of a passive, feedforward-controlled system. A dynamic analysis reveals a much closer resemblance between a passive, feedforward model and the observed data. Numerical simulations demonstrate that a passive, dynamic system model of the movement captures many more of the salient features observed in the measured human data. 6. We conclude that human subjects execute surface-following motions in a largely feedforward manner, using an a priori model of the surface geometry. The evidence does not suggest that active, real time use of force feedback is used to guide the movement or to control limb impedance. We do not exclude, however, the possibility that the internal model of the constraint is updated at somewhat longer latencies on the basis of proprioceptive information.


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