Dynamic Analysis and Modeling of DNN-based Visual Servoing Systems

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Abstract

The integration of deep learning and control techniques has created robotic systems capable of implementing visual servoing and navigating autonomously in unknown environments. However, analyzing the effect that timing interactions between controllers and deep neural networks have, has received little attention in the literature. In this paper we describe a novel model that includes the effects that detection loss and inference latency have on the controller of a visual servoing system. To test our model we created a target tracking system consisting of a video camera mounted on a moving platform that tracks objects using deep neural networks.

Original languageEnglish
Title of host publicationIntelligent Computing - Proceedings of the 2022 Computing Conference
EditorsKohei Arai
Number of pages13
PublisherSpringer
Publication date11 Jul 2022
Pages855-867
ISBN (Print)978-3-031-10463-3
ISBN (Electronic)978-3-031-10464-0
DOIs
Publication statusPublished - 11 Jul 2022
EventSAI 2022 -
Duration: 14 Jul 202215 Jul 2022

Conference

ConferenceSAI 2022
Period14/07/202215/07/2022
SeriesLecture Notes in Networks and Systems
Number2
Volume507
ISSN2367-3370

Keywords

  • Control
  • DNN
  • Dynamics
  • Modeling
  • Networked control systems
  • Visual-servoing

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