10,969 research outputs found
Dynamic Active Constraints for Surgical Robots using Vector Field Inequalities
Robotic assistance allows surgeons to perform dexterous and tremor-free
procedures, but robotic aid is still underrepresented in procedures with
constrained workspaces, such as deep brain neurosurgery and endonasal surgery.
In these procedures, surgeons have restricted vision to areas near the surgical
tooltips, which increases the risk of unexpected collisions between the shafts
of the instruments and their surroundings. In this work, our
vector-field-inequalities method is extended to provide dynamic
active-constraints to any number of robots and moving objects sharing the same
workspace. The method is evaluated with experiments and simulations in which
robot tools have to avoid collisions autonomously and in real-time, in a
constrained endonasal surgical environment. Simulations show that with our
method the combined trajectory error of two robotic systems is optimal.
Experiments using a real robotic system show that the method can autonomously
prevent collisions between the moving robots themselves and between the robots
and the environment. Moreover, the framework is also successfully verified
under teleoperation with tool-tissue interactions.Comment: Accepted on T-RO 2019, 19 Page
Visual servoing of aerial manipulators
The final publication is available at link.springer.comThis chapter describes the classical techniques to control an aerial manipulator by means of visual information and presents an uncalibrated image-based visual servo method to drive the aerial vehicle. The proposed technique has the advantage that it contains mild assumptions about the principal point and skew values of the camera, and it does not require prior knowledge of the focal length, in contrast to traditional image-based approaches.Peer ReviewedPostprint (author's final draft
Manipulating Highly Deformable Materials Using a Visual Feedback Dictionary
The complex physical properties of highly deformable materials such as
clothes pose significant challenges fanipulation systems. We present a novel
visual feedback dictionary-based method for manipulating defoor autonomous
robotic mrmable objects towards a desired configuration. Our approach is based
on visual servoing and we use an efficient technique to extract key features
from the RGB sensor stream in the form of a histogram of deformable model
features. These histogram features serve as high-level representations of the
state of the deformable material. Next, we collect manipulation data and use a
visual feedback dictionary that maps the velocity in the high-dimensional
feature space to the velocity of the robotic end-effectors for manipulation. We
have evaluated our approach on a set of complex manipulation tasks and
human-robot manipulation tasks on different cloth pieces with varying material
characteristics.Comment: The video is available at goo.gl/mDSC4
Independent Motion Detection with Event-driven Cameras
Unlike standard cameras that send intensity images at a constant frame rate,
event-driven cameras asynchronously report pixel-level brightness changes,
offering low latency and high temporal resolution (both in the order of
micro-seconds). As such, they have great potential for fast and low power
vision algorithms for robots. Visual tracking, for example, is easily achieved
even for very fast stimuli, as only moving objects cause brightness changes.
However, cameras mounted on a moving robot are typically non-stationary and the
same tracking problem becomes confounded by background clutter events due to
the robot ego-motion. In this paper, we propose a method for segmenting the
motion of an independently moving object for event-driven cameras. Our method
detects and tracks corners in the event stream and learns the statistics of
their motion as a function of the robot's joint velocities when no
independently moving objects are present. During robot operation, independently
moving objects are identified by discrepancies between the predicted corner
velocities from ego-motion and the measured corner velocities. We validate the
algorithm on data collected from the neuromorphic iCub robot. We achieve a
precision of ~ 90 % and show that the method is robust to changes in speed of
both the head and the target.Comment: 7 pages, 6 figure
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