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In real manufacturing environments, the number of automatic guided vehicles (AGV) is limited. Therefore, the scheduling problem that considers a limited number of AGVs is much nearer to real production and very important. In this paper, we studied the flexible job shop scheduling problem with a limited number of AGVs (FJSP-AGV) and propose an improved genetic algorithm (IGA) to minimize makespan. Compared with the classical genetic algorithm, a population diversity check method was specifically designed in IGA. To evaluate the effectiveness and efficiency of IGA, it was compared with the state-of-the-art algorithms for solving five sets of benchmark instances. Experimental results show that the proposed IGA outperforms the state-of-the-art algorithms. More importantly, the current best solutions of 34 benchmark instances of four data sets were updated.

In this paper, we present our investigation of the 2D Hand Gesture Recognition (HGR) which may be suitable for the control of the Automated Guided Vehicle (AGV). In real conditions, we deal with, among others, a complex background, changing lighting conditions, and different distances of the operator from the AGV. For this reason, in the article, we describe the database of 2D images created during the research. We tested classic algorithms and modified them by us ResNet50 and MobileNetV2 which were retrained partially using the transfer learning approach, as well as proposed a simple and effective Convolutional Neural Network (CNN). As part of our work, we used a closed engineering environment for rapid prototyping of vision algorithms, i.e., Adaptive Vision Studio (AVS), currently Zebra Aurora Vision, as well as an open Python programming environment. In addition, we shortly discuss the results of preliminary work on 3D HGR, which seems to be very promising for future work. The results show that, in our case, from the point of view of implementing the gesture recognition methods in AGVs, better results may be expected for RGB images than grayscale ones. Also using 3D imaging and a depth map may give better results.

Intelligent manufacturing workshop uses automatic guided vehicles as an important logistics and transportation carrier, and most of the existing research adopts the intelligent manufacturing workshop layout and Automated Guided Vehicle (AGV) path step-by-step optimization, which leads to problems such as low AGV operation efficiency and inability to achieve the optimal layout. For this reason, a smart manufacturing assembly line layout optimization model considering AGV path planning with the objective of minimizing the amount of material flow and the shortest AGV path is designed for the machining shop of a discrete manufacturing enterprise of a smart manufacturing company. Firstly, the information of the current node, the next node and the target node is added to the heuristic information, and the dynamic adjustment factor is added to make the heuristic information guiding in the early stage and the pheromone guiding in the later stage of iteration; secondly, the Laplace distribution is introduced to regulate the volatilization of the pheromone in the pheromone updating of the ant colony algorithm, which speeds up the speed of convergence; the path obtained by the ant colony algorithm is subjected to the deletion of the bi-directional redundant nodes, which enhances the path smoothing degree; and finally, the improved ant colony algorithm is fused with the improved dynamic window algorithm, so as to enable the robots to arrive at the end point safely. Simulation shows that in the same map environment, the ant colony algorithm compared with the basic ant colony algorithm reduces the path length by 40% to 67% compared to the basic ant colony algorithm and reduces the path inflection points by 34% to 60%, which is more suitable for complex environments. It also verifies the feasibility and superiority of the conflict-free path optimization strategy in solving the production scheduling problem of the flexible machining operation shop.

This paper presents a comparative analysis of several metaheuristic optimization methods for generating trajectories of automated guided vehicles, which commonly operate in industrial environments. The goal is to address the challenge of efficient path planning for mobile robots, taking into account the specific capabilities and mobility limitations inherent to automated guided vehicles. To do this, three optimization techniques are compared: genetic algorithms, particle swarm optimization and pattern search. The findings of this study reveal the different efficiency of these trajectory optimization approaches. This comprehensive research shows the strengths and weaknesses of various optimization methods and offers valuable information for optimizing the trajectories of industrial vehicles using geometric occupancy maps.

To meet the production demand of workshop, this paper proposes an efficient discrete artificial bee colony (DABC) algorithm to solve a new automatic guided vehicle (AGV) scheduling problem with delivery and pickup in a matrix manufacturing workshop. The goal is to produce a AGV transportation solution that minimizes the total cost, including travel cost, time cost, and AGV cost. Therefore, a mixed integer linear programming model is established. To improve the transportation efficiency, a dynamic calculation method is developed. In the DABC algorithm, a heuristic algorithm and a median based probability selection method are used. For improving the quality of the solutions, four effective neighborhood operators are introduced. In the local search, a rule is given to save the operation time and a problem-based search operator is proposed to improve the quality of the best individual. Finally, a series of comparison experiments were implemented with the iterative greedy algorithm, artificial bee colony algorithm, hybrid fruit fly optimization algorithm, discrete artificial bee colony algorithm, improved harmony search, and hybrid genetic-sweep algorithm. The results show that the proposed DABC algorithm has high performance on solving the delivery and pickup problem.

Emergencies such as machine breakdowns and rush orders greatly affect the production activities of manufacturing enterprises. How to deal with the rescheduling problem after emergencies have high practical value. Meanwhile, under the background of intelligent manufacturing, automatic guided vehicles are gradually emerging in enterprises. To deal with the disturbances in flexible job shop scheduling problem with automatic guided vehicle transportation, a mixed-integer linear programming model is established. According to the traits of this model, an improved NSGA-II is designed, aiming at minimizing makespan, energy consumption and machine workload deviation. To improve solution qualities, the local search operator based on a critical path is designed. In addition, an improved crowding distance calculation method is used to reduce the computation complexity of the algorithm. Finally, the validity of the improvement strategies is tested, and the robustness and superiority of the proposed algorithm are verified by comparing it with NSGA, NSGA-II and SPEA2.

With the booming development of logistics, manufacturing and warehousing fields, the autonomous navigation and intelligent obstacle avoidance technology of automated guided vehicles (AGVs) has become the focus of scientific research. In this paper, an enhanced deep reinforcement learning (DRL) framework is proposed, aiming to empower AGVs with the ability of autonomous navigation and obstacle avoidance in the unknown and variable complex environment. To address the problems of time‐consuming training and limited generalisation ability of traditional DRL, we refine the twin delayed deep deterministic policy gradient algorithm by integrating adaptive noise attenuation and dynamic delayed updating, optimising both training efficiency and model robustness. In order to further strengthen the AGV's ability to perceive and respond to changes of a dynamic environment, we introduce a distance‐based obstacle penalty term in the designed composite reward function, which ensures that the AGV is capable of predicting and avoiding obstacles effectively in dynamic scenarios. Experiments indicate that the AGV model trained by this algorithm presents excellent autonomous navigation capability in both static and dynamic environments, with a high task completion rate, stable and reliable operation, which fully proves the high efficiency and robustness of this method and its practical value.

With the expansion of automated container terminals (ACTs), joint scheduling among multiple types of equipment has become a critical factor affecting operational efficiency. This study investigates a joint scheduling optimization problem of quay cranes (QCs) and automated guided vehicles (AGVs) by considering AGV battery swapping strategies under spare battery constraints. With the objective of minimizing the final task completion time of AGVs, a mixed-integer programming model is formulated that simultaneously accounts for task assignment, operation sequencing, battery swapping thresholds, spare battery quantity, and mutual waiting times between AGVs and QCs. To solve this problem efficiently, a hill-climbing genetic algorithm (HC-GA) is proposed. Numerical experiments under different task scales show that HC-GA outperforms the genetic algorithm (GA), simulated annealing (SA), Q-learning, and the Q-learning-based genetic algorithm (Q-GA) in key indicators. In addition, the experimental results show that a proper configuration of AGVs can improve scheduling coordination and enhance the energy utilization efficiency of AGVs. The number of spare batteries and the threshold have significant impacts on overall system performance. When both operational efficiency and equipment utilization are considered, appropriately configuring the number of spare batteries and the threshold can effectively enhance the operational efficiency of ACTs.

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.

Automated guided vehicle (AGV) is a logistics transport vehicle with high safety performance and excellent availability, which can genuinely achieve unmanned operation. The use of AGV in intelligent warehouses or unmanned warehouses for sorting can improve the efficiency of warehouses and enhance the competitiveness of enterprises. In this paper, a multi-objective mathematical model was developed and integrated with two adaptive genetic algorithms (AGA) and a multi-adaptive genetic algorithm (MAGA) to optimize the task scheduling of AGVs by taking the charging task and the changeable speed of the AGV into consideration to minimize makespan, the number of AGVs used, and the amount of electricity consumption. The numerical experiments showed that MAGA is the best of the three algorithms. The value of objectives before and after optimization changed by about 30%, which proved the rationality and validity of the model and MAGA.