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

Eelco P. van Horssen; Jeroen A.A. van Hooijdonk; Duarte Antunes; W.P.M.H. Heemels

doi:10.1109/TIE.2019.2899553

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

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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.

Original language	English
Article number	8648426
Pages (from-to)	1212-1221
Number of pages	10
Journal	IEEE Transactions on Industrial Electronics
Volume	67
Issue number	2
DOIs	https://doi.org/10.1109/TIE.2019.2899553
Publication status	Published - 1 Feb 2020

Funding

Manuscript received April 14, 2018; revised October 13, 2018 and December 22, 2018; accepted January 30, 2019. Date of publication February 21, 2019; date of current version September 30, 2019. This work was supported in part by the Netherlands Organisation for Scientific Research (NWO-TTW) under Grant 12697 “Control Based on Data-Intensive Sensing,” and in part by the Innovational Research Incentives Scheme under the VICI Grant 11382 “Wireless Control Systems: A New Frontier in Automation” awarded by NWO-TTW. (Corresponding author: Eelco Van Horssen.) The authors are with the Control Systems Technology Group, Eindhoven University of Technology, 5600 MB Eindhoven, The Netherlands (e-mail:, [email protected]; jeroenvhooijdonk@ gmail.com; [email protected]; [email protected]).

Funders	Funder number
Netherlands Organisation for Applied Scientific Research	12697

Keywords

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

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10.1109/TIE.2019.2899553

publishers versionFinal published version, 1.67 MBLicence: TAVERNE

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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

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