Target prediction to improve human errors in robot teleoperation system

Jiann Fuh Liaw, Pei Hsuan Tsai

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

3 Citations (Scopus)

Abstract

In robot teleoperation system, human operates and respond rely on what the video screen displays. However, unavoidable delays, such as network transmission, cause asynchronous communication between human and robot and further result in human operation errors, such as redundant or duplicate operations or even wrong commands. Different from users in large-scale industry, users in daily life may not be experts or even trained to use the robots so there are new challenges for developing home-used robots. In this paper, we design a module-based remote control system to not only illustrate the possible human operation errors caused by network delay but also provide a user-friendly framework for simulating solutions to improve human errors. We then proposed two target prediction methods, Target Area Predictor (TAP) and Stop Position Predictor (SPP), to decrease these human errors. The experiment results show that TAP and SPP effectively improve the performance by reducing 60% task completion time and 36% contour errors.

Original languageEnglish
Title of host publicationProceedings of the 2017 IEEE International Conference on Applied System Innovation
Subtitle of host publicationApplied System Innovation for Modern Technology, ICASI 2017
EditorsTeen-Hang Meen, Artde Donald Kin-Tak Lam, Stephen D. Prior
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1094-1097
Number of pages4
ISBN (Electronic)9781509048977
DOIs
Publication statusPublished - 2017 Jul 21
Event2017 IEEE International Conference on Applied System Innovation, ICASI 2017 - Sapporo, Japan
Duration: 2017 May 132017 May 17

Publication series

NameProceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017

Other

Other2017 IEEE International Conference on Applied System Innovation, ICASI 2017
CountryJapan
CitySapporo
Period17-05-1317-05-17

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality
  • Mechanical Engineering
  • Media Technology
  • Health Informatics
  • Instrumentation

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  • Cite this

    Liaw, J. F., & Tsai, P. H. (2017). Target prediction to improve human errors in robot teleoperation system. In T-H. Meen, A. D. K-T. Lam, & S. D. Prior (Eds.), Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017 (pp. 1094-1097). [7988185] (Proceedings of the 2017 IEEE International Conference on Applied System Innovation: Applied System Innovation for Modern Technology, ICASI 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICASI.2017.7988185