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

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.