Month: September 2014

Tendon-driven Flexible Manipulator

  By ,

Project goals

This project aims to develop a flexible manipulator for transnasal/transoral surgery. Compared with existing surgical manipulators, the developed one should have better performance in workspace and dexterity, thus better facilitate the surgical operation.

Approaches

A constrained tendon-driven serpentine manipulator (CTSM) is designed as shown in Figure 1. It includes an underactuated tendon-driven flexible section, a constraint and a set of tendons. The tendon-driven flexible section is similar to our previous wire-driven robot arm design. It comprises of several identical vertebras, and an elastic tube. Two successive vertebras form a joint and the joint rotation follows the elastic tube bending. Four tendons pass through all the vertebras. For each tendon, the two ends are attached to the distal vertebra and the motor respectively. These tendons are grouped to two pairs and are orthogonally arranged as shown in Figure. 1 (b). One tendon pair controls the bending about X axis and the other tendon pair controls the bending about Y axis. The manipulator bending is planar. The bending angle and bending direction are controlled by the motion of the four tendons. The constraint can be an elastic tube or rigid tube. The constraint translates along the tendon-driven flexible section. Vertebras in the range of the constraint are confined and vertebras out of the range of the constraint are free of rotation. Thus, the last constrained vertebra serves the base of the bending section.
Fig1CTSMDesign

Figure 1 3D design of the CTSM: (a) the assembled and explosion view of the CTSM; (b) the tendon configuration; (c) the cross section view of the joint.

The bending motion of the manipulator is shown in Figure 2: when the insertion of the constraint is 0, the CTSM bends by the tendons as a traditional TSM. By pushing the constraint forward the backbone is segmented to two parts: the proximal constrained section and the distal free bending section. Compared to the distal free bending section, the proximal constrained section is stiffer and the jointsโ€™ rotations are smaller. By pushing and pulling the constraint, the lengths of the two sections are controlled.
Fig2CTSMBendingMotion

Fig. 2 Bending motion illustration: (a) the bending section is not constrained; (b) part of the bending section is constrained; (c) the whole bending section is constrained.

Prototype

A prototype is built as shown in Figure 3. In the prototype, the flexible backbone has 27 vertebras. The vertebras are fabricated by 3D printing, and the material used is plastic. Each joint can rotate up to 7.25ยฐ.The total length of the flexible backbone is 104mm, and the diameter is 7.5mm. A silicon rubber tube serves the elastic tube. The outer diameter is 3 mm and inner diameter is 2 mm. Four steel wires with nylon coating are used to control the backbone bending. The diameter of the steel is 0.3 mm. The wires are arranged orthogonally, with opponent wires make a pair. Each wire pair is connected to a drum wheel. The rotation of the drum wheel is controlled by a servo motor. The diameter of the drum wheel is 50 mm. The wires are guided by a Teflon tube, whose outer diameter is 0.9 mm and inner diameter is 0.5 mm. The replaceable constraint is hold by a chuck, which is mounted on the linear actuator. The range of the linear actuator is 100 mm.

Results

By changing the stiffness ratio between the flexible bending section and the overall stiffness ฮป, the workspace of the CTSM is as shown in Figure 4. In the simulation the length of the CTSM is 100 mm, and the number of vertebrae is 25.
Fig4workspace

Fig. 4 workspace comparison: (a) traditional TSM; (b) CTSM with elastic constraint; (c) CTSM with elastic constraint; (d) CTSM with rigid constraint.

When the CTSM with a rigid constraint is attached to a mobile base, the workspace and dexterity distribution are shown in Figure 5. For the tendon-driven serpentine manipulator (TSM), the dexterity is indexed as the kinematic flexibility. For a traditional TSM, the kinematic flexibility is 1 in most places; the maximum is 2. For the designed CTSM, the kinematic flexibility is enhances all over the workspace and the maximum is 15.
 

Fig5Comparison-wkdb15

Figure 5 Comparison of the dexterity distribution over the workspace: (a) traditional TSM; (b) CTSM with ฮป=0.

People involved

Staff: Zheng Li
Visiting Students: Gui Fu, Zhengchu Tan, Jan Feiling
PIs: Hongliang Ren and Haoyong Yu

Experiment Videos

– Phantom tests

– CTSM Experiments in ex-vivo hearts and phantoms (2014/11/22)

Publications

1. Zheng Li, Haoyong Yu and Hongliang Ren, โ€œA Novel Constrained Tendon-driven Serpentine Manipulator (CTSM)โ€, ICRA 2015 (under review)
2. Zheng Li, Haoyong Yu and Hongliang Ren, โ€œA Novel Underactuated Wire-driven Flexible Robotic Arm with Controllable Bending Section Lengthโ€, ICRA 2014 Workshop on Advances in Flexible Robots for Surgical Interventions, Hong Kong, May 31-June 7, 2014
3. Zheng Li, Ruxu Du, Haoyong Yu and Hongliang Ren, โ€œStatics Modeling of an Underactuated Wire-driven Flexible Robotic Armโ€,IEEE BioRob 2014, Sao Pauo, Brazil, Aug12-15, 2014

Presentation at BIOROB2014

Presentation at ICRA 2014

Poster at ICRA 2014

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Fig3CTSMprototype

Fig. 3 CTSM prototype.

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Tip Tracking and Shape Sensing for Flexible Surgical Robots

  By , ,

Project Goals

As a typical minimally invasive surgery, transoral surgery brings to patients significant benefits such as decreased intra-operative blood loss, less post-operative complication morbidity, shorter hospitalization length and recovery period. Flexible surgical robot (such as tendon/wire/cable-driven robot and concentric tube robot) is an efficient device for transoral inspection and diagnosis. It can work well in complicated and confined environments. One drawback of this method is that the real time tip position and shape information cannot be well estimated, especially when there is payload on the end effector. To address these challenges, we focus on a novel tip tracking and shape sensing method for the flexible surgical robot.

Approaches

The proposed method is based on the positional and directional information of limited specific joints of the robot, which are estimated with an electromagnetic tracking system. Electromagnetic sensors have been mounted in the tip of the robot to provide the tip position and direction information. Based on the section number of the robot, some other sensors will be mounted in the specific position of the robot to realize the shape sensing. The shape sensing method is based on multi quadratic Bรฉzier curves.
Fig1electromagnetic

Fig.1 Electromagnetic tracking method.

The electromagnetic tracking method is shown in Fig.1. A uniaxial sensing coil is used as the target and sensing the magnetic field that generated by the six transmitting coils. These six coils are stimulated sequentially. The position and orientation information of the sensing coil can then be estimated based on the sensing signals.
Fig2shapesensing

Fig.2 Shape sensing method.

Fig.2 shows the shape sensing method for multi-section flexible by using multi quadratic Bรฉzier curves. For a N sections robot, โŒˆN/2โŒ‰ electromagnetic sensors will be mounted in the tail of the (N-2k)th section, where 0โ‰คk<n/2. Therefore, by utilizing the positional and directional information of the sensors, each section can be reconstructed based on a quadratic Bezier curve. Compared to the image based method, this method is easy to setup; compared to the FBG based method, curvature information is not used and fewer sensors are needed in the proposed method.

Results and Remarks

Fig3twouniaxial

Fig.3 Two Uniaxial electromagnetic sensing coils are mounted in both ends of the device.

We have applied the method on a 10-joints wire-driven flexible robot. As shown in Fig.3, two uniaxial electromagnetic sensors (Aurora Shielded and Isolated 5DOF Sensor, 0.9* 12mm) have been mounted on both ends of the robot. Fig.4 shows the average errors of the experimental results of each S shape curve reconstruction in the experiments. The whole average error is 1.4mm.
We have also applied the method on a two-section concentric tube. As shown in Fig.5, a uniaxial sensor has been mounted in the tip of the robot. The tracking results can be seen in the video.
Fig4wiredriven

Fig.4 Experimental results for the wire-driven flexible robot.

Fig5tiptracking

Fig.5 Tip tracking and shape sensing for concentric tube robot. The result can be seen in the video below.

The primary contributions of our work are summarized as follows:
1)A shape sensing method based on Bรฉzier curve fitting and electromagnetic tracking is proposed. This method needs only the positional and directional information of some specific position of the curved robot.
2)Only limited sensors are needed, and thus very few modifications are required on the robot.
3)Compared with other methods, the proposed method is easy to set up and has a good accuracy.

People Involved

Staff: Shuang Song,ย Zheng Li
Investigators: Hongliang Ren, Haoyong Yu

Video

Publications

[1] Shuang Song, Wan Qiao, Baopu Li, Chao Hu, Hongliang Ren and Max Meng. โ€œAn Efficient Magnetic Tracking Method Using Uniaxial Sensing Coilโ€. Magnetics, IEEE Transactions on, 2014. 50(1), Article#: 4003707
[2] Shuang Song, Hongliang Ren and Haoyong Yu. โ€œAn Improved Magnetic Tracking Method using Rotating Uniaxial Coil with Sparse Points and Closed Form Analytic Solutionโ€. IEEE Sensors Journal, 14(10): 3585-3592, 2014
[3] Shuang Song, Baopu Li, Wan Qiao, Chao Hu, Hongliang Ren, Haoyong Yu, Qi Zhang, Max Q.-H. Meng and Guoqing Xu. โ€œ6-D Magnetic Localization and Orientation Method for an Annular Magnet Based on a Closed-Form Analytical Modelโ€. IEEE Transactions on Magnetics. 2014, 50(9), Article#: 5000411

ETH Image Based Visual Servoing to Guide Flexible Robots

  By , , ,

Video Demo

Eye-To-Hand Image Based Visual Servoing to Guide Flexible Robots

Project goals

Flexible robots including active cannula or cable driven continuum robots are typically suitable for such minimally invasive surgeries because they are able to present various flexible shapes with great dexterity, which strengthens the ability of collision avoidance and enlarges the reachability of operation tools. Using model based control method will lead to artificial singularities and even inverted mapping in many situations because the models are usually developed in free space and cannot perform effectively in constrained environments. Therefore, the goal of this project is control the motion of a tentacle-like curvilinear concentric tube robot by model-less visual servoing.

Approaches

A two-dimensional planar manipulator is constructed by enabling only the three translation inputs of a six DOF concentric tube robot. As shown in Fig. 1, the concentric tube manipulator is controlled using a PID controller and the images captured by an uncalibrated camera are used as visual feedback.
Fig1setup

Fig. 1. The experimental setup includes a concentric tube robot, a camera, a laptop, a marker and a target.

The visual tracking of the concentric tube robot is based on shape detection. The circular marker is attached to the tip of the concentric tube robot and a square target is given for the tip to trace. During the experiments, the coordinates of the marker centroid and target centroid are calculated while the next target position is calculated at the same time as shown in Fig. 2.
Fig2workingmechanism

Fig. 2. Working mechanism of the system. Top: translations of the three tubes. Bottom: marker, final target and the next target position on the image plane.

Fig3overview

Fig. 3. Overview of the control algorithm. The Jacobian matrix is estimated based on the measurements of each incremental movement detected from the camera.

The framework of the controlling the robot is shown in Fig. 3. The initial Jacobian matrix is acquired by running each individual motor separately and measuring the change of tip position of the robot in the image space. Then the optimal control is achieved by solving a typical redundant inverse kinematics. And finally the Jacobian matrix is continuously estimated based on the measured displacements.

Results

To evaluate the proposed model-less algorithm, a simulation was carried out on MATLAB first. The desired and actual trajectory was shown in Fig. 4, from which it could be seen that the robot succeeded in following the reference trajectory and reaching the target position.
Fig4simulationcrt

Fig. 4. Simulation of using the proposed algorithm to control a concentric tube robot.

The proposed algorithm was also implemented on a physical concentric tube robot in free space. It was found the robot was able to reach goal with zero steady state error in all trials as shown in Fig. 5.
Fig5experiments

Fig. 5. The concentric tube robot is able to reach a desired goal using the proposed method. Top: the motion of the robot. Bottom: the reference and actual trajectories of two experiments.

People involved

Staff: Keyu WU, Liao WU
PI: Hongliang REN

Publications

1. Keyu Wu, Liao Wu and Hongliang Ren, โ€œAn Image Based Targeting Method to Guide a Tentacle-like Curvilinear Concentric Tube Robotโ€, ROBIO 2014, IEEE International Conference on Robotics and Biomimetics, 2014.