Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study

Hennersperger, Christoph; Fuerst, Bernhard; Virga, Salvatore; Zettinig, Oliver; Frisch, B.; Neff, Thomas; Navab, Nassir

doi:10.1109/tmi.2016.2620723

Cited by 158 publications

(82 citation statements)

References 45 publications

(56 reference statements)

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“…A commercial robotic manipulator has been released (LBR Med, KUKA AG, Augsburg, Germany) which is suitable for use in clinical environments due to its conformity with medical device safety (ISO 60601) and medical software regulations (ISO 62304). Current research suggests that such robots can be applied in diagnostics to autonomously perform aorta measurements [16], in combination with previously acquired MRI scans to autonomously find standard view-planes [17] and in intraoperative procedures to autonomously track surgical tools [18], amongst others. Whilst such robotic platforms allow for great flexibility through a large workspace and high manipulability, the use of large-scale robotic manipulators can pose various disadvantages for clinical integration.…”

Section: Introductionmentioning

confidence: 99%

Design and Integration of a Parallel, Soft Robotic End-Effector for Extracorporeal Ultrasound

Lindenroth

Housden

Wang

et al. 2020

IEEE Trans. Biomed. Eng.

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In this work we address limitations in state-of-theart ultrasound robots by designing and integrating the first soft robotic system for ultrasound imaging. It makes use of the inherent qualities of soft robotics technologies to establish a safe, adaptable interaction between ultrasound probe and patient. We acquire clinical data to establish the movement ranges and force levels required in prenatal foetal ultrasound imaging and design our system accordingly. The end-effectors stiffness characteristics allow for it to reach the desired workspace while maintaining a stable contact between ultrasound probe and patient under the determined loads. The system exhibits a high degree of safety due to its inherent compliance in the transversal direction. We verify the mechanical characteristics of the end-effector, derive and validate a kinetostatic model and demonstrate the robots controllability with and without external loading. The imaging capabilities of the robot are shown in a tele-operated setting on a foetal phantom. The design exhibits the desired stiffness characteristics with a high stiffness along the ultrasound transducer and a high compliance in lateral direction. Twist is constrained using a braided mesh reinforcement. The model can accurately predict the end-effector pose with a mean error of about 6% in position 7% in orientation. The derived controller is, with an average position error of 0.39mm able to track a target pose efficiently without and with externally applied loads. Finally, the images acquired with the system are of equally good quality compared to a manual sonographer scan.

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Section: Introductionmentioning

confidence: 99%

Design and Integration of a Parallel, Soft Robotic End-Effector for Extracorporeal Ultrasound

Lindenroth

Housden

Wang

et al. 2020

IEEE Trans. Biomed. Eng.

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“…Great thanks to Savatore Virga and Marco Esposito for their work on the iiwa_stack project [23] which has been used for our test on real hardware as well as in simulation. On top we improved the rosjava support for ROS actions as well as the support for applying velocity constraints for the KUKA LBR iiwa and ported TF to java.…”

Section: Acknowledgment and Implementation Detailsmentioning

confidence: 99%

Extrinsic Calibration of an Eye-In-Hand 2D LiDAR Sensor in Unstructured Environments Using ICP

Peters

Schmidt

Knoll

2020

IEEE Robot. Autom. Lett.

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We propose a calibration method for the six degrees of freedom (DOF) extrinsic pose of a 2D laser rangefinder mounted to a robot arm. Our goal is to design a system that allows on-site re-calibration without requiring any kind of special environment or calibration objects. By moving the sensor we generate 3D scans of the surrounding area on which we run a iterative closest point (ICP) variant to estimate the missing part of the kinematic chain. With this setup we can simply scale the density and format of our 3D scan by adjusting the robot speed and trajectory, allowing us to exploit the power of a high resolution 3D scanner for a variety of tasks such as mapping, object recognition and grasp planning. Our evaluation, performed on synthetic datasets as well as from real-data shows that the presented approach provides good results both in terms of convergence on crude initial parameters as well as in the precision of the final estimate.

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“…Our simulated environment was built in Gazebo [27] -a ROS simulation software -and consisted of a KUKA LBR IIWA simulated robot [28], to which we added the gripper, camera, table and parts. Simulated depth images were created with the Gazebo depth camera plugin.…”

Section: Creating Training Datasetmentioning

confidence: 99%

Learning Pose Estimation for High-Precision Robotic Assembly Using Simulated Depth Images

Litvak

Biess

Bar-Hillel

2019

2019 International Conference on Robotics and Automation (ICRA)

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Most of industrial robotic assembly tasks today require fixed initial conditions for successful assembly. These constraints induce high production costs and low adaptability to new tasks. In this work we aim towards flexible and adaptable robotic assembly by using 3D CAD models for all parts to be assembled. We focus on a generic assembly task -the Siemens Innovation Challenge -in which a robot needs to assemble a gearlike mechanism with high precision into an operating system. To obtain the millimeter-accuracy required for this task and industrial settings alike, we use a depth camera mounted near the robot's end-effector. We present a high-accuracy two-stage pose estimation procedure based on deep convolutional neural networks, which includes detection, pose estimation, refinement, and handling of near-and full symmetries of parts. The networks are trained on simulated depth images with means to ensure successful transfer to the real robot. We obtain an average pose estimation error of 2.16 millimeters and 0.64 degree leading to 91% success rate for robotic assembly of randomly distributed parts. To the best of our knowledge, this is the first time that the Siemens Innovation Challenge is fully addressed, with all the parts assembled with high success rates. Figure 1: The KUKA LBR IIWA robot performs the Siemens Innovation Challenge successfully in simulation (top row) and in reality (bottom row).

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Towards MRI-Based Autonomous Robotic US Acquisitions: A First Feasibility Study

Cited by 158 publications

References 45 publications

Design and Integration of a Parallel, Soft Robotic End-Effector for Extracorporeal Ultrasound

Design and Integration of a Parallel, Soft Robotic End-Effector for Extracorporeal Ultrasound

Extrinsic Calibration of an Eye-In-Hand 2D LiDAR Sensor in Unstructured Environments Using ICP

Learning Pose Estimation for High-Precision Robotic Assembly Using Simulated Depth Images

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