Applying computer vision to mobile robot navigation has been studied more than two decades. The most challenging problems for a vision-based AGV running in a complex workspace involve the non-uniform illumination, sight-line occlusion or stripe damage, which inevitably result in incomplete or deformed path images as well as many fake artifacts. Neither the fixed threshold methods nor the iterative optimal threshold methods can obtain a suitable threshold for the path images acquired on all conditions. It is still an open question to estimate the model parameters of guide paths accurately by distinguishing the actual path pixels from the under- or over- segmentation error points. Hence, an intelligent path recognition approach based on KPCA–BPNN and IPSO–BTGWP is proposed here, in order to resist the interferences from the complex workspace. Firstly, curvilinear paths were recognized from their straight counterparts by means of a path classifier based on KPCA–BPNN. Secondly, an approximation method based on BTGWP was developed for replacing the curve with a series of piecewise lines (a polyline path). Thirdly, a robust path estimation method based on IPSO was proposed to figure out the path parameters from a set of path pixels surrounded by noise points. Experimental results showed that our approach can effectively improve the accuracy and reliability of a low-cost vision-guidance system for AGVs in a complex workspace.

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.

An AGV system can be used to transport different-size materials in an assembly line. The hybrid task allocation problem is involved in the assembly line, where both single-AGV tasks and multi-AGV tasks exist. However, there is little research on this problem. The goal of solving this problem is to obtain a task allocation scheme with minimum idle time and maximum system throughput. Since all necessary materials must be delivered to the assembly station before the operation can start, the delivery tasks are not independent of each other in a task group serving the operation. To solve the problem above, a hybrid task allocation method based on a task binding strategy and an improved particle swarm optimization (IPSO) is proposed. Firstly, a mathematical model considering the punctuality of material delivery and the cooperative relationship between tasks is established. Secondly, a task binding strategy and four heuristic rules are devised to improve the quality of randomly- and heuristic-generated individuals in the initial population for model optimization. Thirdly, an IPSO is developed to help the optimization algorithm jump out of local optimums. Finally, a simulation is performed to verify the effectiveness of the proposed methods. The simulation results show that a better scheme can be obtained by our hybrid task allocation method, compared to conventional Genetic Algorithms and PSO algorithms.

In the current industrial fields, automatic guided vehicles (AGVs) are widely employed to constitute the flexible manufacturing system (FMS), owing to their great advantages of routing flexibility and high efficiency. However, one main challenge lies in the coupling process of the design problem of the unidirectional guide-path network (UGN) and the task scheduling problem of AGVs. To reduce the complexity, most pertinent literatures only handle these problems one by one, based on the stepwise design methods, thereby neglecting the constraint conditions and the optimization objectives caused by the FMS environment. The motivation of the paper is to bring the coupling factors into the integrated design and solution process. Firstly, an integrated design model of designing UGN and scheduling AGVs simultaneously is proposed, with the objective of minimizing the makespan (i.e., the maximum completion time of all handling tasks), in the consideration of the practical constraints, e.g., the job handling and processing sequence constraints and the AGV number constraint. Secondly, a dual-population collaborative evolutionary genetic algorithm (CEGA) is developed to solve the problems of designing and scheduling in a parallel way. The solutions of the integrated model, i.e., the potential strongly connected UGN and the feasible processing and handling sequence, are, respectively, coded as two different subpopulations with independent and concurrent evolution processes. The neighbourhood search operation, the niche technique, and the elitism strategy are combined to improve the convergence speed and maintain the population diversity. The experimental results show that the integrated design model can formulate the problem more accurately, and the CEGA algorithm is computationally efficient with high solution quality.

Vehicle production involves a complex process and fast production pace, necessitating timely material delivery. The task scheduling problem of multi-load Automated Guided Vehicle System (AGVS) for auxiliary material distribution in automotive production workshops presents multiple optimization objectives and complex constraints. To enhance on-time delivery of auxiliary materials, this paper proposes a multi-load AGVS deadlock prevention task scheduling method based on improved Imperialist Competition Algorithm (ICA). First, a mathematical model for multi-load AGVS task scheduling is established, aiming to minimize task delivery distance and maximize the remaining time before the production line shuts down due to material shortages. By analyzing the conditions triggering deadlocks in multi-load AGVS, a deadlock prevention constraint based on the remaining trailer capacity of the buffer area is incorporated into the mathematical model. Second, an improved ICA (IICA) based deadlock prevention task scheduling method is designed. To enhance the initial national population quality of the IICA, a heuristic scheduling rule library is constructed to generate high-quality countries. An improved differential evolution algorithm is introduced during the assimilation process to improve convergence speed. Finally, a simulation platform for multi-load AGVS auxiliary material distribution is established. Experimental results indicate that the designed deadlock avoidance strategy effectively prevents deadlock occurrences while enhancing system productivity across all six algorithms. Compared to the other five algorithms, the proposed IICA achieves the highest unit hour production capacity, on-time task completion rate, and production line start-up rate, while maintaining the lowest average task execution time.