Control and Communication Co-Optimization Method with Handshake Frequency Hopping for Multi-AGVs

Jisong Yu, Changqing Xia (), Yang Xiao, Yueqi Li, Chi Xu and Xi Jin
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Jisong Yu: State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Changqing Xia: State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Yang Xiao: State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Yueqi Li: State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Chi Xu: State Key Laboratory of Robotics and Intelligent Systems, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
Xi Jin: School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China

Mathematics, 2025, vol. 13, issue 22, 1-21

Abstract: In dynamic, high-interference industrial and logistics environments, multi-AGV cooperative tasks are often affected by communication delays and data loss, leading to information staleness and reduced control accuracy. Traditional handshake frequency hopping communication strategies introduce additional overhead in high-load environments, and channel selection strategies struggle to adapt to dynamic changes. To address challenges related to communication delay, task coordination, and real-time information exchange, we propose a control and communication co-optimization method based on a nonlinear Age of Information (AoI) penalty and an adaptive handshake frequency hopping mechanism. The method constructs a coupled control-communication model, designs an adaptive handshake period and multi-channel frequency hopping strategy to reduce channel conflicts, and introduces a nonlinear AoI penalty function that prioritizes the update of critical timely information, improving communication success rates and path control accuracy. Furthermore, by integrating the differential dynamics model, state estimation under communication delay and control error modeling, we propose a cooperative optimization algorithm for perception control and communication based on nonlinear AoI optimization (PPO-CCBNA). The algorithm achieves efficient solution based on approximate policy optimization (PPO). Simulation results demonstrate that PPO-CCBNA significantly outperforms benchmark algorithms in communication success rates, control stability, and energy efficiency, validating its effectiveness and feasibility in complex multi-AGV cooperative tasks.

Keywords: multi-AGVs collaboration; handshake frequency hopping; nonlinear age of information; cooperative control; multi-agent deep reinforcement learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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