Digital twin-driven deep reinforcement learning for real-time optimisation in dynamic AGV systems
提出一种数字孪生主动参与训练和决策的深度强化学习方法，用于动态AGV系统的实时路径优化，工业案例验证了其提升适应性和效率的能力。
Automated guided vehicle (AGV) systems play a critical role in production logistics and workflow management within manufacturing. As manufacturing environments become increasingly dynamic, real-time optimisation of AGV routing and path planning has become essential. Many studies have applied deep reinforcement learning (DRL) to address these challenges. However, DRL often lacks adaptability to real-time environmental changes. To overcome this limitation, digital twin (DT) technologies have been explored alongside DRL. Most existing studies, however, utilise DT only as a post-training validation tool. This study proposes a novel DT-driven DRL approach in which DT actively participates in both training and decision-making phases. A real-time optimisation system is designed for dynamic AGV operations, and its effectiveness is validated through an industrial case study. Results demonstrate the proposed approach’s capability to significantly enhance adaptability and efficiency in complex manufacturing settings.