Event- and deadline-driven control of a self-localizing robot with vision-induced delays

Eelco P. van Horssen (Corresponding author), Jeroen A.A. van Hooijdonk, Duarte Antunes, W.P.M.H. Heemels

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Control based on vision data is a growing field of research and it is widespread in industry. The amount of data in each image and the processing needed to obtain control-relevant information from this data lead to significant delays with a large variability in the control loops. This often causes performance deterioration since in many cases the delay variability is not explicitly addressed in the control design. In this paper, we approach this problem by applying the ideas of recently developed model-based control design methods, which are tailored to address stochastic delays directly, to the motion control of an omnidirectional robot with a vision-based self-localization algorithm. The completion time or delay of the Random Sample Consensus (RANSAC) based localization algorithm is identified as a stochastic random variable with significant variability, illustrating the practical difficulties with data processing. Our main aim is to show that the novel deadline-driven and event-driven control designs significantly outperform a traditional periodic control implementation for a stochastic optimal control performance index.

LanguageEnglish
Article number8648426
Pages1212-1221
Number of pages10
JournalIEEE Transactions on Industrial Electronics
Volume67
Issue number2
DOIs
StatePublished - 1 Feb 2020

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Robots
Motion control
Random variables
Deterioration
Processing
Industry

Keywords

  • Deadline-driven control
  • event-driven control
  • feedback control
  • image-processing
  • robotics
  • stochastic optimal control
  • stochastic time-delay
  • vision

Cite this

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title = "Event- and deadline-driven control of a self-localizing robot with vision-induced delays",
abstract = "Control based on vision data is a growing field of research and it is widespread in industry. The amount of data in each image and the processing needed to obtain control-relevant information from this data lead to significant delays with a large variability in the control loops. This often causes performance deterioration since in many cases the delay variability is not explicitly addressed in the control design. In this paper, we approach this problem by applying the ideas of recently developed model-based control design methods, which are tailored to address stochastic delays directly, to the motion control of an omnidirectional robot with a vision-based self-localization algorithm. The completion time or delay of the Random Sample Consensus (RANSAC) based localization algorithm is identified as a stochastic random variable with significant variability, illustrating the practical difficulties with data processing. Our main aim is to show that the novel deadline-driven and event-driven control designs significantly outperform a traditional periodic control implementation for a stochastic optimal control performance index.",
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Event- and deadline-driven control of a self-localizing robot with vision-induced delays. / van Horssen, Eelco P. (Corresponding author); van Hooijdonk, Jeroen A.A.; Antunes, Duarte; Heemels, W.P.M.H.

In: IEEE Transactions on Industrial Electronics, Vol. 67, No. 2, 8648426, 01.02.2020, p. 1212-1221.

Research output: Contribution to journalArticleAcademicpeer-review

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