24,426 research outputs found
Improved GelSight Tactile Sensor for Measuring Geometry and Slip
A GelSight sensor uses an elastomeric slab covered with a reflective membrane
to measure tactile signals. It measures the 3D geometry and contact force
information with high spacial resolution, and successfully helped many
challenging robot tasks. A previous sensor, based on a semi-specular membrane,
produces high resolution but with limited geometry accuracy. In this paper, we
describe a new design of GelSight for robot gripper, using a Lambertian
membrane and new illumination system, which gives greatly improved geometric
accuracy while retaining the compact size. We demonstrate its use in measuring
surface normals and reconstructing height maps using photometric stereo. We
also use it for the task of slip detection, using a combination of information
about relative motions on the membrane surface and the shear distortions. Using
a robotic arm and a set of 37 everyday objects with varied properties, we find
that the sensor can detect translational and rotational slip in general cases,
and can be used to improve the stability of the grasp.Comment: IEEE/RSJ International Conference on Intelligent Robots and System
Distributed Control of Microscopic Robots in Biomedical Applications
Current developments in molecular electronics, motors and chemical sensors
could enable constructing large numbers of devices able to sense, compute and
act in micron-scale environments. Such microscopic machines, of sizes
comparable to bacteria, could simultaneously monitor entire populations of
cells individually in vivo. This paper reviews plausible capabilities for
microscopic robots and the physical constraints due to operation in fluids at
low Reynolds number, diffusion-limited sensing and thermal noise from Brownian
motion. Simple distributed controls are then presented in the context of
prototypical biomedical tasks, which require control decisions on millisecond
time scales. The resulting behaviors illustrate trade-offs among speed,
accuracy and resource use. A specific example is monitoring for patterns of
chemicals in a flowing fluid released at chemically distinctive sites.
Information collected from a large number of such devices allows estimating
properties of cell-sized chemical sources in a macroscopic volume. The
microscopic devices moving with the fluid flow in small blood vessels can
detect chemicals released by tissues in response to localized injury or
infection. We find the devices can readily discriminate a single cell-sized
chemical source from the background chemical concentration, providing
high-resolution sensing in both time and space. By contrast, such a source
would be difficult to distinguish from background when diluted throughout the
blood volume as obtained with a blood sample
Investigation of a new method for improving image resolution for camera tracking applications
Camera based systems have been a preferred choice in many motion tracking applications due to the ease of installation and the ability to work in unprepared environments. The concept of these systems is based on extracting image information (colour and shape properties) to detect the object location. However, the resolution of the image and the camera field-of- view (FOV) are two main factors that can restrict the tracking applications for which these systems can be used. Resolution can be addressed partially by using higher resolution cameras but this may not always be possible or cost effective.
This research paper investigates a new method utilising averaging of offset images to improve the effective resolution using a standard camera. The initial results show that the minimum detectable position change of a tracked object could be improved by up to 4 times
GelSlim: A High-Resolution, Compact, Robust, and Calibrated Tactile-sensing Finger
This work describes the development of a high-resolution tactile-sensing
finger for robot grasping. This finger, inspired by previous GelSight sensing
techniques, features an integration that is slimmer, more robust, and with more
homogeneous output than previous vision-based tactile sensors. To achieve a
compact integration, we redesign the optical path from illumination source to
camera by combining light guides and an arrangement of mirror reflections. We
parameterize the optical path with geometric design variables and describe the
tradeoffs between the finger thickness, the depth of field of the camera, and
the size of the tactile sensing area. The sensor sustains the wear from
continuous use -- and abuse -- in grasping tasks by combining tougher materials
for the compliant soft gel, a textured fabric skin, a structurally rigid body,
and a calibration process that maintains homogeneous illumination and contrast
of the tactile images during use. Finally, we evaluate the sensor's durability
along four metrics that track the signal quality during more than 3000 grasping
experiments.Comment: RA-L Pre-print. 8 page
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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