Wireless Control of Autonomous Guided Vehicle using Reinforcement Learning
Pedro M. de Sant Ana, Nikolaj Marchenko, Petar Popovski, Beatriz Soret
Abstract
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%.