Virtual Simulation and Prototype Test for Behavior of Robot in Picking Process
CSTR:
Author:
Affiliation:

Clc Number:

Fund Project:

  • Article
  • |
  • Figures
  • |
  • Metrics
  • |
  • Reference
  • |
  • Related
  • |
  • Cited by
  • |
  • Materials
  • |
  • Comments
    Abstract:

    To test and verify the intelligent algorithm of anti-collision behavior and planning of picking robot, a simulation system based on virtual reality for picking robot was designed. Taking grape picking robot for example, a virtual reality environment for picking robot working scene was firstly built, which was used to simulate the testing environment of facility vineyard. Then the kinematics modeling of virtual picking robot was constructed by using the D-H parameter calculation method. An end-effector with clamping-lifting-cutting steps was designed for grape picking robot based on grape shape characteristic, and subsequently the control model of picking process of the end-effector was built. The space pose transformation between the end joint of manipulator and actuator was set up, and a trajectory planning method for manipulator was designed. The data communication interface between visual perception and virtual robot was designed, and the virtual picking robot simulation system was developed based on the virtual reality software platform EON. To verify the practicability of this simulation system, totally 18 times picking tests for grape anti-collision path planning and shearing behavior were implemented on the developed simulation system, and the success rate was up to 8889%. Then the algorithms were transplanted to the physical prototype, totally 43 times of tests were implemented, and the success rate was about 8605%. The testing results showed that the simulation system developed can provide a virtual test platform for the testing and improvement of the intelligent behavior algorithm of the picking robot.

    Reference
    Related
    Cited by
Get Citation
Share
Article Metrics
  • Abstract:
  • PDF:
  • HTML:
  • Cited by:
History
  • Received:January 26,2018
  • Revised:
  • Adopted:
  • Online: May 10,2018
  • Published:
Article QR Code