Shape Control of Elastic Objects Based on Implicit Sensorimotor Models and Data-Driven Geometric Features

Wanyu Ma, Jihong Zhu, David Navarro-Alarcon

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

2 Citations (Scopus)

Abstract

This paper proposes a general approach to design automatic controls to manipulate elastic objects into desired shapes. The object’s geometric model is defined as the shape feature based on the specific task to globally describe the deformation. Raw visual feedback data is processed using classic regression methods to identify parameters of data-driven geometric models in real-time. Our proposed method is able to analytically compute a pose-shape Jacobian matrix based on implicit functions. This model is then used to derive a shape servoing controller. To validate the proposed method, we report a detailed experimental study with robotic manipulators deforming an elastic rod.

Original languageEnglish
Title of host publicationIntelligent Autonomous Systems 16 - Proceedings of the 16th International Conference IAS-16
EditorsMarcelo H. Ang Jr, Hajime Asama, Wei Lin, Shaohui Foong
PublisherSpringer Science and Business Media Deutschland GmbH
Pages518-531
Number of pages14
ISBN (Print)9783030958916
DOIs
Publication statusPublished - Apr 2022
Event16th International Conference on Intelligent Autonomous Systems, IAS-16 2020 - Virtual, Online
Duration: 22 Jun 202125 Jun 2021

Publication series

NameLecture Notes in Networks and Systems
Volume412 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

Conference16th International Conference on Intelligent Autonomous Systems, IAS-16 2020
CityVirtual, Online
Period22/06/2125/06/21

Keywords

  • Deformable objects
  • Robotics
  • Shape control
  • Visual servoing

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Signal Processing
  • Computer Networks and Communications

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