5,208 research outputs found
Assistance strategies for robotized laparoscopy
Robotizing laparoscopic surgery not only allows achieving better
accuracy to operate when a scale factor is applied between master and slave or thanks to the use of tools with 3 DoF, which cannot be used in conventional manual surgery, but also due to additional informatic support. Relying on computer assistance different strategies that facilitate the task of the surgeon can be incorporated, either in the form of autonomous navigation or cooperative guidance, providing sensory or visual feedback, or introducing certain limitations of movements. This paper describes different ways of assistance aimed at improving the work capacity of the surgeon and achieving more safety for the patient, and the results obtained with the prototype developed at UPC.Peer ReviewedPostprint (author's final draft
How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change
Direct visual localization has recently enjoyed a resurgence in popularity
with the increasing availability of cheap mobile computing power. The
competitive accuracy and robustness of these algorithms compared to
state-of-the-art feature-based methods, as well as their natural ability to
yield dense maps, makes them an appealing choice for a variety of mobile
robotics applications. However, direct methods remain brittle in the face of
appearance change due to their underlying assumption of photometric
consistency, which is commonly violated in practice. In this paper, we propose
to mitigate this problem by training deep convolutional encoder-decoder models
to transform images of a scene such that they correspond to a previously-seen
canonical appearance. We validate our method in multiple environments and
illumination conditions using high-fidelity synthetic RGB-D datasets, and
integrate the trained models into a direct visual localization pipeline,
yielding improvements in visual odometry (VO) accuracy through time-varying
illumination conditions, as well as improved metric relocalization performance
under illumination change, where conventional methods normally fail. We further
provide a preliminary investigation of transfer learning from synthetic to real
environments in a localization context. An open-source implementation of our
method using PyTorch is available at https://github.com/utiasSTARS/cat-net.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the
IEEE International Conference on Robotics and Automation (ICRA'18), Brisbane,
Australia, May 21-25, 201
Robust Dense Mapping for Large-Scale Dynamic Environments
We present a stereo-based dense mapping algorithm for large-scale dynamic
urban environments. In contrast to other existing methods, we simultaneously
reconstruct the static background, the moving objects, and the potentially
moving but currently stationary objects separately, which is desirable for
high-level mobile robotic tasks such as path planning in crowded environments.
We use both instance-aware semantic segmentation and sparse scene flow to
classify objects as either background, moving, or potentially moving, thereby
ensuring that the system is able to model objects with the potential to
transition from static to dynamic, such as parked cars. Given camera poses
estimated from visual odometry, both the background and the (potentially)
moving objects are reconstructed separately by fusing the depth maps computed
from the stereo input. In addition to visual odometry, sparse scene flow is
also used to estimate the 3D motions of the detected moving objects, in order
to reconstruct them accurately. A map pruning technique is further developed to
improve reconstruction accuracy and reduce memory consumption, leading to
increased scalability. We evaluate our system thoroughly on the well-known
KITTI dataset. Our system is capable of running on a PC at approximately 2.5Hz,
with the primary bottleneck being the instance-aware semantic segmentation,
which is a limitation we hope to address in future work. The source code is
available from the project website (http://andreibarsan.github.io/dynslam).Comment: Presented at IEEE International Conference on Robotics and Automation
(ICRA), 201
Robotic micromanipulation for microassembly : modelling by sequencial function chart and achievement by multiple scale visual servoings.
International audienceThe paper investigates robotic assembly by focusing on the manipulation of microparts. This task is formalized through the notion of basic tasks which are organized in a logical sequence represented by a function chart and interpreted as the model of the behavior of the experimental setup. The latter includes a robotic system, a gripping system, an imaging system, and a clean environment. The imaging system is a photon videomicroscope able to work at multiple scales. It is modelled by a linear projective model where the relation between the scale factor and the magnification or zoom is explicitly established. So, the usual visual control law is modified in order to take into account this relation. The manipulation of some silicon microparts (400 μm×400 μm×100 μm) by means of a distributed robotic system (xyθ system, ϕz system), a two-finger gripping system and a controllable zoom and focus videomicroscope shows the relevance of the concepts. The 30 % of failure rate comes mainly from the physical phenomena (electrostatic and capillary forces) instead of the accuracy of control or the occultations of microparts
Activity profiling for minimally invasive surgery
Imperial Users onl
A continuum robotic platform for endoscopic non-contact laser surgery: design, control, and preclinical evaluation
The application of laser technologies in surgical interventions has been accepted in the clinical
domain due to their atraumatic properties. In addition to manual application of fibre-guided
lasers with tissue contact, non-contact transoral laser microsurgery (TLM) of laryngeal tumours
has been prevailed in ENT surgery. However, TLM requires many years of surgical training
for tumour resection in order to preserve the function of adjacent organs and thus preserve the
patient’s quality of life. The positioning of the microscopic laser applicator outside the patient
can also impede a direct line-of-sight to the target area due to anatomical variability and limit
the working space. Further clinical challenges include positioning the laser focus on the tissue
surface, imaging, planning and performing laser ablation, and motion of the target area during
surgery. This dissertation aims to address the limitations of TLM through robotic approaches and
intraoperative assistance. Although a trend towards minimally invasive surgery is apparent, no
highly integrated platform for endoscopic delivery of focused laser radiation is available to date.
Likewise, there are no known devices that incorporate scene information from endoscopic imaging
into ablation planning and execution. For focusing of the laser beam close to the target tissue, this
work first presents miniaturised focusing optics that can be integrated into endoscopic systems.
Experimental trials characterise the optical properties and the ablation performance. A robotic
platform is realised for manipulation of the focusing optics. This is based on a variable-length
continuum manipulator. The latter enables movements of the endoscopic end effector in five
degrees of freedom with a mechatronic actuation unit. The kinematic modelling and control of the
robot are integrated into a modular framework that is evaluated experimentally. The manipulation
of focused laser radiation also requires precise adjustment of the focal position on the tissue. For
this purpose, visual, haptic and visual-haptic assistance functions are presented. These support
the operator during teleoperation to set an optimal working distance. Advantages of visual-haptic
assistance are demonstrated in a user study. The system performance and usability of the overall
robotic system are assessed in an additional user study. Analogous to a clinical scenario, the
subjects follow predefined target patterns with a laser spot. The mean positioning accuracy of the
spot is 0.5 mm. Finally, methods of image-guided robot control are introduced to automate laser
ablation. Experiments confirm a positive effect of proposed automation concepts on non-contact
laser surgery.Die Anwendung von Lasertechnologien in chirurgischen Interventionen hat sich aufgrund der atraumatischen Eigenschaften in der Klinik etabliert. Neben manueller Applikation von fasergefĂĽhrten
Lasern mit Gewebekontakt hat sich die kontaktfreie transorale Lasermikrochirurgie (TLM) von
Tumoren des Larynx in der HNO-Chirurgie durchgesetzt. Die TLM erfordert zur Tumorresektion
jedoch ein langjähriges chirurgisches Training, um die Funktion der angrenzenden Organe zu
sichern und damit die Lebensqualität der Patienten zu erhalten. Die Positionierung des mikroskopis chen Laserapplikators außerhalb des Patienten kann zudem die direkte Sicht auf das Zielgebiet
durch anatomische Variabilität erschweren und den Arbeitsraum einschränken. Weitere klinische
Herausforderungen betreffen die Positionierung des Laserfokus auf der Gewebeoberfläche, die
Bildgebung, die Planung und AusfĂĽhrung der Laserablation sowie intraoperative Bewegungen
des Zielgebietes. Die vorliegende Dissertation zielt darauf ab, die Limitierungen der TLM durch
robotische Ansätze und intraoperative Assistenz zu adressieren. Obwohl ein Trend zur minimal
invasiven Chirurgie besteht, sind bislang keine hochintegrierten Plattformen fĂĽr die endoskopische
Applikation fokussierter Laserstrahlung verfĂĽgbar. Ebenfalls sind keine Systeme bekannt, die
Szeneninformationen aus der endoskopischen Bildgebung in die Ablationsplanung und -ausfĂĽhrung
einbeziehen. Für eine situsnahe Fokussierung des Laserstrahls wird in dieser Arbeit zunächst
eine miniaturisierte Fokussieroptik zur Integration in endoskopische Systeme vorgestellt. Experimentelle Versuche charakterisieren die optischen Eigenschaften und das Ablationsverhalten. Zur
Manipulation der Fokussieroptik wird eine robotische Plattform realisiert. Diese basiert auf einem
längenveränderlichen Kontinuumsmanipulator. Letzterer ermöglicht in Kombination mit einer
mechatronischen Aktuierungseinheit Bewegungen des Endoskopkopfes in fĂĽnf Freiheitsgraden.
Die kinematische Modellierung und Regelung des Systems werden in ein modulares Framework
eingebunden und evaluiert. Die Manipulation fokussierter Laserstrahlung erfordert zudem eine
präzise Anpassung der Fokuslage auf das Gewebe. Dafür werden visuelle, haptische und visuell haptische Assistenzfunktionen eingeführt. Diese unterstützen den Anwender bei Teleoperation
zur Einstellung eines optimalen Arbeitsabstandes. In einer Anwenderstudie werden Vorteile der
visuell-haptischen Assistenz nachgewiesen. Die Systemperformanz und Gebrauchstauglichkeit
des robotischen Gesamtsystems werden in einer weiteren Anwenderstudie untersucht. Analog zu
einem klinischen Einsatz verfolgen die Probanden mit einem Laserspot vorgegebene Sollpfade. Die
mittlere Positioniergenauigkeit des Spots beträgt dabei 0,5 mm. Zur Automatisierung der Ablation
werden abschließend Methoden der bildgestützten Regelung vorgestellt. Experimente bestätigen
einen positiven Effekt der Automationskonzepte fĂĽr die kontaktfreie Laserchirurgie
Robotic Micromanipulation and Microassembly using Mono-view and Multi-scale visual servoing.
International audienceThis paper investigates sequential robotic micromanipulation and microassembly in order to build 3-D microsystems and devices. A mono-view and multiple scale 2-D visual control scheme is implemented for that purpose. The imaging system used is a photon video microscope endowed with an active zoom enabling to work at multiple scales. It is modelled by a non-linear projective method where the relation between the focal length and the zoom factor is explicitly established. A distributed robotic system (xy system, z system) with a twofingers gripping system is used in conjunction with the imaging system. The results of experiments demonstrate the relevance of the proposed approaches. The tasks were performed with the following accuracy: 1.4 m for the positioning error, and 0.5 for the orientation error
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