Pareto Optimization Design Techniques for the AFIT (Air Force Institute of Technology)/AAMRL (Armstrong Aeronautical Medical Research Laboratory) Anthropomorphic Robotic Manipulator

Abstract

A method to optimize a robotic parallel manipulator configuration using Pareto Optimization techniques was developed. Pareto optimization is a cooperative effort between design parameters. The design parameters to be optimized included the payload mass, the length of the manipulator link labelled 12,and the prescribed time for the manipulator to move a prescribed distance. Three functionals were computed for design optimization. These included the mechanical efficiency of the system, the maximum value of torque for motor one, and the maximum value of torque for motor two. A functional analysis was performed using two trajectories for the manipulator; a horizontal trajectory and a vertical trajectory. A combination of these paths allow the manipulator to reach anywhere within its workspace. Algorithms were developed for computing each of the functionals when changing any of the design parameters. When the horizontal path was traversed, mechanical efficiency was zero, thus total input work of the manipulator was evaluated. The sensitivities of the design parameter changes were evaluated for optimization. When a horizontal path was followed, only the link 1 (2) length had changing sensitivity values. Sensitivity changes occurred for all of the design parameters for a vertical trajectory. Theses (SDW)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1989
Accession Number
ADA216178

Entities

People

  • Jerrel D. Tumlin Jr

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • Autonomy
  • Biomedical
  • Human Systems
  • Space

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Air Force Facilities
  • Algorithms
  • Computations
  • Computer Programs
  • Computers
  • Engineering
  • Engineers
  • Equations Of Motion
  • Functional Analysis
  • Margin Of Safety
  • Mass
  • Moment Of Inertia
  • Peak Values
  • Safety Factor
  • Time Intervals

Readers

  • Computational Modeling and Simulation
  • Graph Algorithms and Convex Optimization.
  • Robotics and Automation.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Autonomy