Direct teaching algorithm based on task assistance for machine tending

Young Loul Kim, Kuk Hyun Ahn, Jae-Bok Song

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Citations (Scopus)

Abstract

Much research has been done on direct teaching in which an operator directly teaches a robot its task by hand guiding. In this study, we propose a novel algorithm to add virtual stiffness to a robot based on its task without changing the mode of teaching. The proposed algorithm defines the task assistance domains with spherical, cylindrical, and hexahedral shapes depending on the task environment. Inside this domain, stiffness is added to assist the operator in teaching the robot. At the boundary of the task assistance domain, stiffness is gradually changed to avoid the instability due to an abrupt change in stiffness. The proposed method was implemented on a 6 DOF industrial robot performing direct teaching for machine tending to demonstrate its feasibility. The experiments show that the operator can perform direct teaching more efficiently and this algorithm is particularly good for repetitive teaching on a small batch production.

Original languageEnglish
Title of host publication2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages861-866
Number of pages6
ISBN (Electronic)9781509008216
DOIs
Publication statusPublished - 2016 Oct 21
Event13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016 - Xian, China
Duration: 2016 Aug 192016 Aug 22

Publication series

Name2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016

Other

Other13th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2016
Country/TerritoryChina
CityXian
Period16/8/1916/8/22

Keywords

  • Cooperative manipulators
  • Force control

ASJC Scopus subject areas

  • Modelling and Simulation
  • Artificial Intelligence
  • Control and Optimization

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