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Robot Task Planning and Trajectory Learning for Flexible Automation

[+] Author Affiliations
Iraj Mantegh

National Research Council Canada, Montreal, QC, Canada

Paper No. DETC2012-71359, pp. 1347-1353; 7 pages
doi:10.1115/DETC2012-71359
From:
  • ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
  • Volume 4: 36th Mechanisms and Robotics Conference, Parts A and B
  • Chicago, Illinois, USA, August 12–15, 2012
  • Conference Sponsors: Design Engineering Division, Computers and Information in Engineering Division
  • ISBN: 978-0-7918-4503-5
  • Copyright © 2012 by Her Majesty the Queen in Right of Canada

abstract

A task planning method is presented to model and reproduce robot trajectories based on those captured from human demonstrations. In the framework of the Programming by Demonstration (PbD) approach, task planning algorithm is developed to determine the general type of trajectory pattern, its parameters, and its kinematic profile. The pattern is described independently of the shape of the surface on which it is demonstrated. Key pattern points are identified based on changes in direction and velocity, and are then reduced based on their proximity. The results of the analysis are provided are used inside a task planning algorithm, to produce robot trajectories based on the workpiece geometries. The trajectory is output in the form of robot native language code so that it can be readily downloaded on the robot.

Copyright © 2012 by Her Majesty the Queen in Right of Canada

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