Organizational Unit:
Institute for Robotics and Intelligent Machines (IRIM)

Permanent Link
https://hdl.handle.net/1853/70657
Research Organization Registry ID
Description
Previous Names
Parent Organization
Parent Organization
Includes Organization(s)
Organizational Unit
Organizational Unit
ArchiveSpace Name Record

Filters

2000 - 2003 4
4
4
4
4
Reset filters

Settings
Author: Howard, Ayanna M. × End date: 2009 × Start date: 2000 × search.filters.applied.f.resourcesubtype: Post-print ×

Publication Search Results

Now showing 1 - 4 of 4
  • Thumbnail Image
    Item
    Fuzzy Image Processing in Sun Sensor
    (Georgia Institute of Technology, 2003-06) Mobasser, Sohrab ; Liebe, Carl Christian ; Howard, Ayanna M.
    Sun sensors are widely used in spacecraft attitude determination subsystems to provide a measurement of the sun vector in spacecraft coordinates. At the Jet Propulsion Laboratory, California Institute of Technology, there is an ongoing research activity to utilize Micro Electro Mechanical Systems (MEMS) processes to develop a smaller and lighter sun sensor for space applications. A prototype sun sensor has been designed and constructed. It consists of a piece of silicon coated with a thin layer of chrome, and a layer of gold with hundreds of small pinholes, placed on top of an image detector at a distance of less than a "eter. Images of the sun are formed on the detector when the sun illuminates the assembly. Software algorithms must be able to identify the individual pinholes on the image detector and calculate the angle to the sun. Fuzzy image processing is utilized in this process. This paper will describe how the fuzzy image processing is implemented in the instrument. Comparison of the Fuzzy image processing and a more conventional image pmcessing algorithm is provided and shows that the Fuzzy image processing yields better accuracy then conventional image processing.
  • Thumbnail Image
    Item
    A Real-Time Autonomous Rover Navigation System
    (Georgia Institute of Technology, 2000-06) Howard, Ayanna M. ; Seraji, Homayoun
    To enable real-time autonomous navigation, a mobile robot is equipped with on-board processing power, image-processing algorithms, and a fuzzy computation engine that allow the rover to safely navigate to a designated goal while avoiding obstacles and impassible terrains. The underlying architecture discussed in this paper utilizes real-time measurement of terrain characteristics and a fuzzy logic framework for onboard analysis of terrain traversability. The overall navigation strategy, consisting of terrain-traverse and goal-seeking behaviors, requires no a priori information about the environment, and uses the on-board traversability analysis to enable the rover to select easy-to-traverse paths to the goal autonomously. The rover navigation system is tested and validated with a set of physical rover experiments. These experiments demonstrate the real-time capability of the navigation system.
  • Thumbnail Image
    Item
    A Learning Methodology for Robotic Manipulation of Deformable Objects
    (Georgia Institute of Technology, 2000-06) Howard, Ayanna M. ; Bekey, George A.
    The majority of manipulation systems are designed with the assumption that the objects being handled are rigid and do not deform when grasped. This paper address the problem of robotic grasping and manipulation of 3- D deformable objects, such as rubber balls or bags filled with sand. Specifically, we have developed a generalized learning algorithm for handling of 3-D deformable objects in which prior knowledge of object attributes is not required and thus it can be applied to a large class of object types. A description of our learning methodology will be given in this paper. We outline our methodology for modeling the object deformation and learning the required minimum forces for grasping. Evaluation of the overall algorithm demonstrates that we can achieve an error level of 14% with respect to the minimum physical lifting force.
  • Thumbnail Image
    Item
    Real Time Intelligent Target Detection and Analysis with Machine Vision
    (Georgia Institute of Technology, 2000-06) Howard, Ayanna M. ; Padgett, Curtis ; Brown, Kenneth R.
    This paper presents an algorithm for detecting a specified set of target objects embedded in visual imagery for an Automatic Target Recognition (ATR) application. ATR involves processing images for detecting, classifying, and tracking targets embedded in a background scene. We address the problem of discriminating between targets and non-target objects located within a cluttered environment by evaluating 40x40 image blocks belonging to a segmented image scene. Using directed principal component analysis, the data dimensionality of an image block is first reduced and then clustered into one of n classes based on a minimum distance to a set of n cluster prototypes. Following clustering, each image pattern is fed into an associated trained neural network for classification. A detailed description of our algorithm will be given in this paper. Evaluation of the overall algorithm demonstrates that our detection rates approach 96% with a false positive rate of less than 0.03%.