Configuration Comparison Analysis for the AFIT/AAMRL Anthropomorphic Robotic Manipulator

Abstract

A method of calculating mechanical efficiency was developed as a means of comparing the performance of different types of manipulators. As an initial approach to this problem takes into account inertial and gravitational terms of the robot configurations in addition to a variable payload. The method included developing a numerical integration algorithm to calculate the work done by each manipulator at any point in that manipulator's workspace. The efficiencies of two robotic manipulator configurations that are candidates for the design of the AFIT, AAMRL, Anthropomorphic Robotic Manipulator, were analyzed. The two designs were a serial open link direct drive manipulator, and the closed parallel kinematic chain direct drive manipulator design by Dr. Asada at M. I. T. The difference between the manipulator was actual mass and kinematic design. The efficiency measure used to analyze both manipulators was based on the magnitude of the total work done by the manipulator to move a payload a prescribed distance. The effects of a variable mass payload on efficiency have now been individually examined for the cases when the arm has been 'tuned' for some nominal payload by means of compensating for gravity, making the robotic configuration invariant, and decoupling the manipulator's dynamic equations of motion. Keywords: Algorithms, Theses.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1988
Accession Number
ADA202618

Entities

People

  • Steven L. Parker

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Air Platforms
  • Autonomy
  • Biomedical
  • Space

DTIC Thesaurus Topics

  • Air Force
  • Algorithms
  • Computer Programming
  • Control
  • Control Systems
  • Decoupling
  • Dynamics
  • Efficiency
  • Equations
  • Equations Of Motion
  • Joints (Anatomy)
  • Numerical Analysis
  • Numerical Integration
  • Payload
  • Robotics
  • Robots
  • Tools

Readers

  • Control Systems Engineering.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • Autonomy