Partitioning Contact-State Space Using the Theory of Polyhedral Convex Cones.

Abstract

The assembly plan from observation (APO) system observes a human operator perform an assembly task, analyzes the observations, models the task, and generates the programs for the robot to perform the same task. A major component of the APO system is the task recognition module, which models the observed task. The task model in the APO context is defined as a sequence of assembly states of the part being assembled and the actions which cause the transition between states. The state of the assembled part is based on its freedom which can be computed from the geometry of the contacts between the part and its environment. This freedom can be represented as a polyhedral convex cone (PCC) in screw space. We show that any contact configuration can be classed into a finite number of contact states. These contact states correspond to topologically distinct intersections of the PCC with a linear subspace T in screw space. The models of any observed task can be represented compactly as a path in a transition graph obtained from these contact states. We illustrate the application of the theory by implementing the APO system for the case of objects assembled in a plane using rotation and translation motion.

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Document Details

Document Type
Technical Report
Publication Date
Aug 01, 1994
Accession Number
ADA293545

Entities

People

  • George V. Paul
  • Katsushi Ikeuchi

Organizations

  • Carnegie Mellon University

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Artificial Intelligence
  • Assembly
  • Automatic Programming
  • Classification
  • Computer Programming
  • Computer Vision
  • Disassembly
  • Geometry
  • Motion Planning
  • Object Recognition
  • Recognition
  • Robots
  • Teleoperation
  • Three Dimensional
  • Two Dimensional

Readers

  • Graph Algorithms and Convex Optimization.
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.
  • Unmanned Aerial System (UAS) Autonomous Capabilities and Mission Reconnaissance.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Space
  • Space - Spacecraft Maneuvers