Design of the multiple Neural Network compensator for a billiard robot

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In this paper, a multiple Neural Network (NN) compensator is designed for a billiard robot to finish a task, in which the trained robot is commanded to control the cue ball to a specific target point along a trajectory with multiple cushion rebounds. A novel pyramid classification has been established to sort out the pattern of trajectory and its segments. For each trajectory pattern, a corresponding Back Propagation Neural Network (BPNN) model has been established to fit the deviation between theoretical direction point and actual one. The pyramid classification and a finite number of BPNN models composited the multiple NN compensator. In the test, the robot will calculate the deviation and work out the actual direction point for potting. The test results have verified the reliability and workability of the multiple NN compensator.

Original languageEnglish
Title of host publicationICNSC 2015 - 2015 IEEE 12th International Conference on Networking, Sensing and Control
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages17-22
Number of pages6
ISBN (Electronic)9781479980697
DOIs
StatePublished - 1 Jun 2015
Event2015 12th IEEE International Conference on Networking, Sensing and Control, ICNSC 2015 - Taipei, Taiwan, Province of China
Duration: 9 Apr 201511 Apr 2015

Publication series

NameICNSC 2015 - 2015 IEEE 12th International Conference on Networking, Sensing and Control

Conference

Conference2015 12th IEEE International Conference on Networking, Sensing and Control, ICNSC 2015
Country/TerritoryTaiwan, Province of China
CityTaipei
Period9/04/1511/04/15

Keywords

  • Neural Network(NN)
  • billiard robot
  • multiple rebounds
  • pyramid classification

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