Wireless Control of Autonomous Guided Vehicle using Reinforcement Learning

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review


Real-time wireless networked control of an Autonomous Guided Vehicle (AGV) from an edge cloud controller is an attractive approach to reduce hardware costs of AGVs, e.g., for industrial applications. We specify a networked control protocol for AGV and investigate how system performance and stability are affected by the reliability of the wireless link with fading. Particularly, there is a trade-off between the AGV speed, the control stability, and the channel quality. Our model takes into account end-to-end latency, which includes control loops and communication. Considering the model complexity, we employ a Reinforcement Learning (RL) approach in order to find the optimal speed of AGV to complete a mission path in shortest time. The proposed solution achieves system stability at par with widely used baseline state-of-the-art controllers, while reducing the AGV mission time by more than 30%.

Original languageEnglish
Title of host publicationGLOBECOM 2020 - 2020 IEEE Global Communications Conference
Number of pages7
Publication date2020
Article number9322156
ISBN (Print)978-1-7281-8299-5
ISBN (Electronic)978-1-7281-8298-8
Publication statusPublished - 2020
EventGLOBECOM 2020 - 2020 IEEE Global Communications Conference - Taipei, Taiwan, Province of China
Duration: 7 Dec 202011 Dec 2020


ConferenceGLOBECOM 2020 - 2020 IEEE Global Communications Conference
CountryTaiwan, Province of China
SeriesGlobecom. I E E E Conference and Exhibition


  • Autonomous Guided Vehicle
  • Reinforcement Learning
  • Wireless Networked Control Systems

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