10 research outputs found
Snake-Like Robots for Minimally Invasive, Single Port, and Intraluminal Surgeries
The surgical paradigm of Minimally Invasive Surgery (MIS) has been a key
driver to the adoption of robotic surgical assistance. Progress in the last
three decades has led to a gradual transition from manual laparoscopic surgery
with rigid instruments to robot-assisted surgery. In the last decade, the
increasing demand for new surgical paradigms to enable access into the anatomy
without skin incision (intraluminal surgery) or with a single skin incision
(Single Port Access surgery - SPA) has led researchers to investigate
snake-like flexible surgical devices. In this chapter, we first present an
overview of the background, motivation, and taxonomy of MIS and its newer
derivatives. Challenges of MIS and its newer derivatives (SPA and intraluminal
surgery) are outlined along with the architectures of new snake-like robots
meeting these challenges. We also examine the commercial and research surgical
platforms developed over the years, to address the specific functional
requirements and constraints imposed by operations in confined spaces. The
chapter concludes with an evaluation of open problems in surgical robotics for
intraluminal and SPA, and a look at future trends in surgical robot design that
could potentially address these unmet needs.Comment: 41 pages, 18 figures. Preprint of article published in the
Encyclopedia of Medical Robotics 2018, World Scientific Publishing Company
www.worldscientific.com/doi/abs/10.1142/9789813232266_000
Design Considerations and Robustness to Parameter Uncertainty in Wire-Wrapped Cam Mechanisms
Collaborative robots must simultaneously be safe enough to operate in close
proximity to human operators and powerful enough to assist users in industrial
tasks such as lifting heavy equipment. The requirement for safety necessitates
that collaborative robots are designed with low-powered actuators. However,
some industrial tasks may require the robot to have high payload capacity
and/or long reach. For collaborative robot designs to be successful, they must
find ways of addressing these conflicting design requirements. One promising
strategy for navigating this tradeoff is through the use of static balancing
mechanisms to offset the robot's self weight, thus enabling the selection of
lower-powered actuators. In this paper, we introduce a novel, 2 degree of
freedom static balancing mechanism based on spring-loaded, wire-wrapped cams.
We also present an optimization-based cam design method that guarantees the
cams stay convex, ensures the springs stay below their extensions limits, and
minimizes sensitivity to unmodeled deviations from the nominal spring constant.
Additionally, we present a model of the effect of friction between the wire and
the cam. Lastly, we show experimentally that the torque generated by the cam
mechanism matches the torque predicted in our modeling approach. Our results
also suggest that the effects of wire-cam friction are significant for
non-circular cams
Task and Configuration Space Compliance of Continuum Robots via Lie Group and Modal Shape Formulations
Continuum robots suffer large deflections due to internal and external
forces. Accurate modeling of their passive compliance is necessary for accurate
environmental interaction, especially in scenarios where direct force sensing
is not practical. This paper focuses on deriving analytic formulations for the
compliance of continuum robots that can be modeled as Kirchhoff rods. Compared
to prior works, the approach presented herein is not subject to the
constant-curvature assumptions to derive the configuration space compliance,
and we do not rely on computationally-expensive finite difference
approximations to obtain the task space compliance. Using modal approximations
over curvature space and Lie group integration, we obtain closed-form
expressions for the task and configuration space compliance matrices of
continuum robots, thereby bridging the gap between constant-curvature analytic
formulations of configuration space compliance and variable curvature task
space compliance. We first present an analytic expression for the compliance of
a single Kirchhoff rod. We then extend this formulation for computing both the
task space and configuration space compliance of a tendon-actuated continuum
robot. We then use our formulation to study the tradeoffs between computation
cost and modeling accuracy as well as the loss in accuracy from neglecting the
Jacobian derivative term in the compliance model. Finally, we experimentally
validate the model on a tendon-actuated continuum segment, demonstrating the
model's ability to predict passive deflections with error below 11.5\% percent
of total arc length
Unsupervised Deformable Image Registration for Respiratory Motion Compensation in Ultrasound Images
In this paper, we present a novel deep-learning model for deformable
registration of ultrasound images and an unsupervised approach to training this
model. Our network employs recurrent all-pairs field transforms (RAFT) and a
spatial transformer network (STN) to generate displacement fields at online
rates (apprx. 30 Hz) and accurately track pixel movement. We call our approach
unsupervised recurrent all-pairs field transforms (U-RAFT). In this work, we
use U-RAFT to track pixels in a sequence of ultrasound images to cancel out
respiratory motion in lung ultrasound images. We demonstrate our method on
in-vivo porcine lung videos. We show a reduction of 76% in average pixel
movement in the porcine dataset using respiratory motion compensation strategy.
We believe U-RAFT is a promising tool for compensating different kinds of
motions like respiration and heartbeat in ultrasound images of deformable
tissue
Unsupervised Deformable Ultrasound Image Registration and Its Application for Vessel Segmentation
This paper presents a deep-learning model for deformable registration of
ultrasound images at online rates, which we call U-RAFT. As its name suggests,
U-RAFT is based on RAFT, a convolutional neural network for estimating optical
flow. U-RAFT, however, can be trained in an unsupervised manner and can
generate synthetic images for training vessel segmentation models. We propose
and compare the registration quality of different loss functions for training
U-RAFT. We also show how our approach, together with a robot performing
force-controlled scans, can be used to generate synthetic deformed images to
significantly expand the size of a femoral vessel segmentation training dataset
without the need for additional manual labeling. We validate our approach on
both a silicone human tissue phantom as well as on in-vivo porcine images. We
show that U-RAFT generates synthetic ultrasound images with 98% and 81%
structural similarity index measure (SSIM) to the real ultrasound images for
the phantom and porcine datasets, respectively. We also demonstrate that
synthetic deformed images from U-RAFT can be used as a data augmentation
technique for vessel segmentation models to improve intersection-over-union
(IoU) segmentation performanc