Due to the advantages of high efficiency and flexibility, the Automatic Guided Vehicle System (AGVS) has been more and more widely used in many industries. However, one of the main challenges in AGVS control is how to prevent collision and deadlock between vehicles. Although many collision and deadlock prevention algorithms have been proposed, they are inefficient for the AGVS based on a unidirectional guide-path network (UGN). In order to solve this problem, this paper proposes a collision and deadlock prevention method with a traffic sequence optimization strategy for the UGN-based AGVS. First, a vehicle coordination method based on the semaphore theory and Internet of Things (IoT) positioning technology is proposed to prevent collisions and intersection congestion deadlocks. Then, to avoid cycle deadlocks, a cycle deadlock search and avoid algorithm based on the digraph theory is developed. After that a bidding mechanism-based strategy is developed to optimize the vehicle traffic sequence in each path intersection. Finally, extensive simulation tests are conducted to verify the performance of the proposed methods and strategy. Simulation results show that, compared to the zone controlled (ZC) methods, the average travel time of the proposed methods is reduced by 6.2%–29.6%, and the average throughput is increased by 3.5%–27.4%. Also, the bidding mechanism-based traffic sequence optimization strategy can not only increase the average throughput of the processing subsystem but also reduce the congestion and deadlock risk of the logistics transportation subsystem. The proposed method is suitable for an UGN-based AGVS in the manufacturing application environment.

The heterogeneous automatic guided vehicle (AGV) system, composed of several AGVs with different load capability and handling function, has good flexibility and agility to operational requirements. Accurate task travel time prediction (T3P) is vital for the efficient operation of heterogeneous AGV systems. However, T3P remains a challenging problem due to individual task correlations and dynamic changes in model input/output dimensions. To address these challenges, a biomimetics-inspired learning framework based on a radial basis function (RBF) neural network with an improved mayfly algorithm and a selective update strategy (IMA-SURBF) is proposed. Firstly, a T3P model is constructed by using travel-influencing factors as input and task travel time as output of the RBF neural network, where the input/output dimension is determined dynamically. Secondly, the improved mayfly algorithm (IMA), a biomimetic metaheuristic method, is adopted to optimize the initial parameters of the RBF neural network, while a selective update strategy is designed for parameter updates. Finally, simulation experiments on model design, parameter initialization, and comparison with deep learning-based models are conducted in a complex assembly line scenario to validate the accuracy and efficiency of the proposed method.