475 research outputs found
A surgical system for automatic registration, stiffness mapping and dynamic image overlay
In this paper we develop a surgical system using the da Vinci research kit
(dVRK) that is capable of autonomously searching for tumors and dynamically
displaying the tumor location using augmented reality. Such a system has the
potential to quickly reveal the location and shape of tumors and visually
overlay that information to reduce the cognitive overload of the surgeon. We
believe that our approach is one of the first to incorporate state-of-the-art
methods in registration, force sensing and tumor localization into a unified
surgical system. First, the preoperative model is registered to the
intra-operative scene using a Bingham distribution-based filtering approach. An
active level set estimation is then used to find the location and the shape of
the tumors. We use a recently developed miniature force sensor to perform the
palpation. The estimated stiffness map is then dynamically overlaid onto the
registered preoperative model of the organ. We demonstrate the efficacy of our
system by performing experiments on phantom prostate models with embedded stiff
inclusions.Comment: International Symposium on Medical Robotics (ISMR 2018
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Soft Morphological Computation
Soft Robotics is a relatively new area of research, where progress in material science has powered the next generation of robots, exhibiting biological-like properties such as soft/elastic tissues, compliance, resilience and more besides. One of the issues when employing soft robotics technologies is the soft nature of the interactions arising between the robot and its environment. These interactions are complex, and the their dynamics are non-linear and hard to capture with known models. In this thesis we argue that complex soft interactions
can actually be beneficial to the robot, and give rise to rich stimuli which can be used for the resolution of robot tasks. We further argue that the usefulness of these interactions depends on statistical regularities, or structure, that appear in the stimuli. To this end, robots should appropriately employ their morphology and their actions, to influence the system-environment interactions such that structure can arise in the stimuli. In this thesis we show that learning processes can be used to perform such a task. Following this rationale, this thesis proposes and supports the theory of Soft Morphological Computation (SoMComp), by which a soft robot should appropriately condition, or ‘affect’, the soft interactions to improve the quality of the physical stimuli arising from it. SoMComp is composed of four main principles, i.e.: Soft Proprioception, Soft Sensing, Soft Morphology and Soft Actuation. Each of these principles is explored in the context of haptic object recognition or object handling in soft robots. Finally, this thesis provides an overview of this research and its future directions.AHDB CP17
Complementary Situational Awareness for an Intelligent Telerobotic Surgical Assistant System
Robotic surgical systems have contributed greatly to the advancement of Minimally Invasive Surgeries (MIS). More specifically, telesurgical robots have provided enhanced dexterity to surgeons performing MIS procedures. However, current robotic teleoperated systems have only limited situational awareness of the patient anatomy and surgical environment that would typically be available to a surgeon in an open surgery. Although the endoscopic view enhances the visualization of the anatomy, perceptual understanding of the environment and anatomy is still lacking due to the absence of sensory feedback.
In this work, these limitations are addressed by developing a computational framework to provide Complementary Situational Awareness (CSA) in a surgical assistant. This framework aims at improving the human-robot relationship by providing elaborate guidance and sensory feedback capabilities for the surgeon in complex MIS procedures. Unlike traditional teleoperation, this framework enables the user to telemanipulate the situational model in a virtual environment and uses that information to command the slave robot with appropriate admittance gains and environmental constraints. Simultaneously, the situational model is updated based on interaction of the slave robot with the task space environment.
However, developing such a system to provide real-time situational awareness requires that many technical challenges be met. To estimate intraoperative organ information continuous palpation primitives are required. Intraoperative surface information needs to be estimated in real-time while the organ is being palpated/scanned. The model of the task environment needs to be updated in near real-time using the estimated organ geometry so that the force-feedback applied on the surgeon's hand would correspond to the actual location of the model. This work presents a real-time framework that meets these requirements/challenges to provide situational awareness of the environment in the task space. Further, visual feedback is also provided for the surgeon/developer to view the near video frame rate updates of the task model. All these functions are executed in parallel and need to have a synchronized data exchange. The system is very portable and can be incorporated to any existing telerobotic platforms with minimal overhead
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
Towards a robust slam framework for resilient AUV navigation
Autonomous Underwater Vehicles (AUVs) are playing an increasing part in modern
navies, to the point that the control of oceans will soon be decided by their strategic
use. In face of more complex missions occurring in potentially hostile environments,
the resilience of such systems becomes critical. In this study, we investigate the
following scenario: how does a lone AUV could recover from a temporary breakdown
that has created a gap in its measurements, while remaining beneath the surface to
avoid detection? It is assumed that the AUV is equipped with an active sonar and
is operating in an uncharted area. The vehicle has to rely on itself by recovering
its location using a Simultaneous Localization and Mapping (SLAM) algorithm.
While SLAM is widely investigated and developed in the case of aerial and terrestrial
robotics, the nature of the poorly structured underwater environment dramatically
challenges its effectiveness. To address such a complex problem, the usual side
scan sonar data association techniques are investigated under a global registration
problem while applying robust graph SLAM modelling. In particular, ways to
improve the global detection of features from sonar mosaic region patches that react
well to the MICR similarity measure are discussed. The main contribution of this
study is centered on a novel data processing framework that is able to generate
different graph topologies using robust SLAM techniques. One of its advantages is to
facilitate the testing of different modelling hypotheses to tackle the data gap following
the temporary breakdown and make the most of the limited available information.
Several research perspectives related to this framework are discussed. Notably, the
possibility to further extend the proposed framework to heterogeneous datasets and
the opportunity to accelerate the recovery process by inferring information about
the breakdown using machine learning.PH
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