Wireless Control of Autonomous Guided Vehicle using Reinforcement Learning

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Abstrakt

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%.

OriginalsprogEngelsk
TitelGLOBECOM 2020 - 2020 IEEE Global Communications Conference
Antal sider7
ForlagIEEE
Publikationsdato2020
Sider1-7
Artikelnummer9322156
ISBN (Trykt)978-1-7281-8299-5
ISBN (Elektronisk)978-1-7281-8298-8
DOI
StatusUdgivet - 2020
BegivenhedGLOBECOM 2020 - 2020 IEEE Global Communications Conference - Taipei, Taiwan
Varighed: 7 dec. 202011 dec. 2020

Konference

KonferenceGLOBECOM 2020 - 2020 IEEE Global Communications Conference
LandTaiwan
ByTaipei
Periode07/12/202011/12/2020
NavnGlobecom. I E E E Conference and Exhibition
ISSN1930-529X

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