Reliable vision-guided grasping

Automated assembly of truss structures in space requires vision-guided servoing for grasping a strut when its position and orientation are uncertain. This paper presents a methodology for efficient and robust vision-guided robot grasping alignment. The vision-guided grasping problem is related to vision-guided 'docking' problems. It differs from other hand-in-eye visual servoing problems, such as tracking, in that the distance from the target is a relevant servo parameter. The methodology described in this paper is hierarchy of levels in which the vision/robot interface is decreasingly 'intelligent,' and increasingly fast. Speed is achieved primarily by information reduction. This reduction exploits the use of region-of-interest windows in the image plane and feature motion prediction. These reductions invariably require stringent assumptions about the image. Therefore, at a higher level, these assumptions are verified using slower, more reliable methods. This hierarchy provides for robust error recovery in that when a lower-level routine fails, the next-higher routine will be called and so on. A working system is described which visually aligns a robot to grasp a cylindrical strut. The system uses a single camera mounted on the end effector of a robot and requires only crude calibration parameters. The grasping procedure is fast and reliable, with a multi-level error recovery system

Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

A Programmable Display-Layer Architecture for Virtual-Reality Applications

Two important technical objectives of virtual-reality systems are to provide compelling visuals and effective 3D user interaction. In this respect, modern virtual reality system architectures suffer from a number of short-comings. The reduction of end-to-end latency, crosstalk and judder are especially difficult challenges, each of which negatively affects visual quality or user interaction. In order to provide higher quality visuals, complex scenes consisting of large models are often used. Rendering such a complex scene is a time-consuming process resulting in high end-to-end latency, thereby hampering user interaction. Classic virtual-reality architectures can not adequately address these challenges due to their inherent design principles. In particular, the tight coupling between input devices, the rendering loop and the display system inhibits these systems from addressing all the aforementioned challenges simultaneously. In this thesis, a virtual-reality architecture design is introduced that is based on the addition of a new logical layer: the Programmable Display Layer (PDL). The governing idea is that an extra layer is inserted between the rendering system and the display. In this way, the display can be updated at a fast rate and in a custom manner independent of the other components in the architecture, including the rendering system. To generate intermediate display updates at a fast rate, the PDL performs per-pixel depth-image warping by utilizing the application data. Image warping is the process of computing a new image by transforming individual depth-pixels from a closely matching previous image to their updated locations. The PDL architecture can be used for a range of algorithms and to solve problems that are not easily solved using classic architectures. In particular, techniques to reduce crosstalk, judder and latency are examined using algorithms implemented on top of the PDL. Concerning user interaction techniques, several six-degrees-of-freedom input methods exists, of which optical tracking is a popular option. However, optical tracking methods also introduce several constraints that depend on the camera setup, such as line-of-sight requirements, the volume of the interaction space and the achieved tracking accuracy. These constraints generally cause a decline in the effectiveness of user interaction. To investigate the effectiveness of optical tracking methods, an optical tracker simulation framework has been developed, including a novel optical tracker to test this framework. In this way, different optical tracking algorithms can be simulated and quantitatively evaluated under a wide range of conditions. A common approach in virtual reality is to implement an algorithm and then to evaluate the efficacy of that algorithm by either subjective, qualitative metrics or quantitative user experiments, after which an updated version of the algorithm may be implemented and the cycle repeated. A different approach is followed here. Throughout this thesis, an attempt is made to automatically detect and quantify errors using completely objective and automated quantitative methods and to subsequently attempt to resolve these errors dynamically

Efficient visual grasping alignment for cylinders

Monocular information from a gripper-mounted camera is used to servo the robot gripper to grasp a cylinder. The fundamental concept for rapid pose estimation is to reduce the amount of information that needs to be processed during each vision update interval. The grasping procedure is divided into four phases: learn, recognition, alignment, and approach. In the learn phase, a cylinder is placed in the gripper and the pose estimate is stored and later used as the servo target. This is performed once as a calibration step. The recognition phase verifies the presence of a cylinder in the camera field of view. An initial pose estimate is computed and uncluttered scan regions are selected. The radius of the cylinder is estimated by moving the robot a fixed distance toward the cylinder and observing the change in the image. The alignment phase processes only the scan regions obtained previously. Rapid pose estimates are used to align the robot with the cylinder at a fixed distance from it. The relative motion of the cylinder is used to generate an extrapolated pose-based trajectory for the robot controller. The approach phase guides the robot gripper to a grasping position. The cylinder can be grasped with a minimal reaction force and torque when only rough global pose information is initially available

Data-driven modeling of the olfactory neural codes and their dynamics in the insect antennal lobe

Recordings from neurons in the insects' olfactory primary processing center, the antennal lobe (AL), reveal that the AL is able to process the input from chemical receptors into distinct neural activity patterns, called olfactory neural codes. These exciting results show the importance of neural codes and their relation to perception. The next challenge is to \emph{model the dynamics} of neural codes. In our study, we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a neural network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons, and is capable of producing unique olfactory neural codes for the tested odorants. Specifically, we (i) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (ii) characterize scent recognition, i.e., decision-making based on olfactory signals and (iii) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study answers a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns

Comparing D-Bar and Common Regularization-Based Methods for Electrical Impedance Tomography

Objective: To compare D-bar difference reconstruction with regularized linear reconstruction in electrical impedance tomography. Approach: A standard regularized linear approach using a Laplacian penalty and the GREIT method for comparison to the D-bar difference images. Simulated data was generated using a circular phantom with small objects, as well as a \u27Pac-Man\u27 shaped conductivity target. An L-curve method was used for parameter selection in both D-bar and the regularized methods. Main results: We found that the D-bar method had a more position independent point spread function, was less sensitive to errors in electrode position and behaved differently with respect to additive noise than the regularized methods. Significance: The results allow a novel pathway between traditional and D-bar algorithm comparison