rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Learning the motion map of a robot arm with neural networks
  name:
    name part: Saxon, James B.
    role:
      role term: creator
  name:
    name part: Mukerjee, Amitabha
    name part: 1957-
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 1990
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: print
    extent: 6 leaves : illustrations ; 28 cm.
  abstract: Abstract: "This paper discusses the integration of neural self- organization and circular reaction into a robot guidance system (Neurobot). A two degree of freedom robot arm 'learns' a cognitive map which contains both the visual workspace and also the robot's joint angles in a biologically inspired neural network. The range of positions of the robot's end effector is called the workspace, the corresponding joint angle space is called the configuration space. In other words, the workspace is the physical world of the robot while the configuration space is an abstract space necessary for controlling the arm motions.
  abstract: Neurobot creates an association between its visual position and its joint position by training a self-organizing neural network using both spaces as inputs. This position-map is thus a hypersurface represented by a sheet of neurons in which each neuron represents a point in both the workspace and the configuration space. The position-map can be considered sensory-motor schema in the sense that it can now be used for robot guidance without using computationally expensive transformation equations. Rather, a visual input target would cause a spreading activation to the present position.
  abstract: Further, it can also perform efficient obstacle avoidance where visual input obstacles inhibit or gate the correpsonding joint angles. Thus, it can be used for minimal computation robot guidance, obstacle avoidance, and most importantly, is learned by a self-organization process governed by a circular feedback loop."
  note: James Saxon, Amitabha Mukerjee.
  note: "January 1990."
  note: Includes bibliographical references.
  subject:
    topic: Manipulators (Mechanism)
  subject:
    topic: Neural networks (Computer science)
  subject:
    topic: Robotics
  subject:
    topic: Control theory
  related item:
    title info:
      title: Technical report (Texas A & M University. Computer Science Department) ; 90-008
  record info:
    record content source: PMC
    record creation date: 920316
    record change date: 20151003082100.0
    record identifier: in00001252707