Adaptive image-based visual servoing with reinforcement learning for wheeled mobile robots

Haobin Shi, Gang Sun, Renyu Zhang, Xuanwen Chen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Appropriate servoing gain are critical to good performance of image-based visual servoing (IBVS). Servoing gain affects the stability and the convergence rate for the robot to reach a desired position, but the servoing gains in many IBVS applications are heuristically set as a constant. A generic method for determining a series of the servoing gains is proposed, which adjusts adaptively the servoing gain by using Q-learning in order to realize more efficient control. The proposed method addresses problems associated with IBVS control, for instance, slow convergence and low stability. The complete IBVS control system is validated by several experiments on a WMRs that reaches a desired position. Simulation and experimental results demonstrate that the proposed IBVS method has better convergence and stability than the competing methods.

Original languageEnglish
Title of host publicationProceedings of 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages954-959
Number of pages6
ISBN (Electronic)9781538660720
DOIs
StatePublished - 5 Oct 2018
Event15th IEEE International Conference on Mechatronics and Automation, ICMA 2018 - Changchun, China
Duration: 5 Aug 20188 Aug 2018

Publication series

NameProceedings of 2018 IEEE International Conference on Mechatronics and Automation, ICMA 2018

Conference

Conference15th IEEE International Conference on Mechatronics and Automation, ICMA 2018
Country/TerritoryChina
CityChangchun
Period5/08/188/08/18

Keywords

  • Image-based visual servoing
  • Mobile robot
  • Reinforcement learning
  • Servoing gain

Fingerprint

Dive into the research topics of 'Adaptive image-based visual servoing with reinforcement learning for wheeled mobile robots'. Together they form a unique fingerprint.

Cite this