52,550 research outputs found
Semantic Robot Programming for Goal-Directed Manipulation in Cluttered Scenes
We present the Semantic Robot Programming (SRP) paradigm as a convergence of
robot programming by demonstration and semantic mapping. In SRP, a user can
directly program a robot manipulator by demonstrating a snapshot of their
intended goal scene in workspace. The robot then parses this goal as a scene
graph comprised of object poses and inter-object relations, assuming known
object geometries. Task and motion planning is then used to realize the user's
goal from an arbitrary initial scene configuration. Even when faced with
different initial scene configurations, SRP enables the robot to seamlessly
adapt to reach the user's demonstrated goal. For scene perception, we propose
the Discriminatively-Informed Generative Estimation of Scenes and Transforms
(DIGEST) method to infer the initial and goal states of the world from RGBD
images. The efficacy of SRP with DIGEST perception is demonstrated for the task
of tray-setting with a Michigan Progress Fetch robot. Scene perception and task
execution are evaluated with a public household occlusion dataset and our
cluttered scene dataset.Comment: published in ICRA 201
Near real-time stereo vision system
The apparatus for a near real-time stereo vision system for use with a robotic vehicle is described. The system is comprised of two cameras mounted on three-axis rotation platforms, image-processing boards, a CPU, and specialized stereo vision algorithms. Bandpass-filtered image pyramids are computed, stereo matching is performed by least-squares correlation, and confidence ranges are estimated by means of Bayes' theorem. In particular, Laplacian image pyramids are built and disparity maps are produced from the 60 x 64 level of the pyramids at rates of up to 2 seconds per image pair. The first autonomous cross-country robotic traverses (of up to 100 meters) have been achieved using the stereo vision system of the present invention with all computing done onboard the vehicle. The overall approach disclosed herein provides a unifying paradigm for practical domain-independent stereo ranging
Scientific Computing Meets Big Data Technology: An Astronomy Use Case
Scientific analyses commonly compose multiple single-process programs into a
dataflow. An end-to-end dataflow of single-process programs is known as a
many-task application. Typically, tools from the HPC software stack are used to
parallelize these analyses. In this work, we investigate an alternate approach
that uses Apache Spark -- a modern big data platform -- to parallelize
many-task applications. We present Kira, a flexible and distributed astronomy
image processing toolkit using Apache Spark. We then use the Kira toolkit to
implement a Source Extractor application for astronomy images, called Kira SE.
With Kira SE as the use case, we study the programming flexibility, dataflow
richness, scheduling capacity and performance of Apache Spark running on the
EC2 cloud. By exploiting data locality, Kira SE achieves a 2.5x speedup over an
equivalent C program when analyzing a 1TB dataset using 512 cores on the Amazon
EC2 cloud. Furthermore, we show that by leveraging software originally designed
for big data infrastructure, Kira SE achieves competitive performance to the C
implementation running on the NERSC Edison supercomputer. Our experience with
Kira indicates that emerging Big Data platforms such as Apache Spark are a
performant alternative for many-task scientific applications
Robot Acting on Moving Bodies (RAMBO): Interaction with tumbling objects
Interaction with tumbling objects will become more common as human activities in space expand. Attempting to interact with a large complex object translating and rotating in space, a human operator using only his visual and mental capacities may not be able to estimate the object motion, plan actions or control those actions. A robot system (RAMBO) equipped with a camera, which, given a sequence of simple tasks, can perform these tasks on a tumbling object, is being developed. RAMBO is given a complete geometric model of the object. A low level vision module extracts and groups characteristic features in images of the object. The positions of the object are determined in a sequence of images, and a motion estimate of the object is obtained. This motion estimate is used to plan trajectories of the robot tool to relative locations rearby the object sufficient for achieving the tasks. More specifically, low level vision uses parallel algorithms for image enhancement by symmetric nearest neighbor filtering, edge detection by local gradient operators, and corner extraction by sector filtering. The object pose estimation is a Hough transform method accumulating position hypotheses obtained by matching triples of image features (corners) to triples of model features. To maximize computing speed, the estimate of the position in space of a triple of features is obtained by decomposing its perspective view into a product of rotations and a scaled orthographic projection. This allows use of 2-D lookup tables at each stage of the decomposition. The position hypotheses for each possible match of model feature triples and image feature triples are calculated in parallel. Trajectory planning combines heuristic and dynamic programming techniques. Then trajectories are created using dynamic interpolations between initial and goal trajectories. All the parallel algorithms run on a Connection Machine CM-2 with 16K processors
Information visualization for DNA microarray data analysis: A critical review
Graphical representation may provide effective means of making sense of the complexity and sheer volume of data produced by DNA microarray experiments that monitor the expression patterns of thousands of genes simultaneously. The ability to use ldquoabstractrdquo graphical representation to draw attention to areas of interest, and more in-depth visualizations to answer focused questions, would enable biologists to move from a large amount of data to particular records they are interested in, and therefore, gain deeper insights in understanding the microarray experiment results. This paper starts by providing some background knowledge of microarray experiments, and then, explains how graphical representation can be applied in general to this problem domain, followed by exploring the role of visualization in gene expression data analysis. Having set the problem scene, the paper then examines various multivariate data visualization techniques that have been applied to microarray data analysis. These techniques are critically reviewed so that the strengths and weaknesses of each technique can be tabulated. Finally, several key problem areas as well as possible solutions to them are discussed as being a source for future work
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