A parallel algorithm for multi-AGV systems

Yu, Dingding; Hu, Xianliang; Liang, Kewei; Jiang, Ying

doi:10.1007/s12652-021-02987-3

Cited by 15 publications

(3 citation statements)

References 41 publications

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“…Hongying Shan et al (2021) proposed an improved path planning method with time window to avoid the problem of path conflict between AGVs for the multi-AGV cooperative transportation problem. For the path planning problem of AGV group, Dingding Yu et al (2021) first used an A Ã algorithm with penalty factor to generate a single path and then proposed a control method based on resource locking to avoid path conflicts between AGVs. Jahangir Moshayedi et al (2023) carried a field of view camera on unmanned aerial vehicles (UAVs) and trained single shot multiBox detector (SSD) deep learning networks with different vehicle speed models to achieve vehicle speed detection and estimation.…”

Section: Literature Reviewmentioning

confidence: 99%

Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

Cheng,

Meng

2024

RIA

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Purpose This study aims to solve the problem of job scheduling and multi automated guided vehicle (AGV) cooperation in intelligent manufacturing workshops. Design/methodology/approach In this study, an algorithm for job scheduling and cooperative work of multiple AGVs is designed. In the first part, with the goal of minimizing the total processing time and the total power consumption, the niche multi-objective evolutionary algorithm is used to determine the processing task arrangement on different machines. In the second part, AGV is called to transport workpieces, and an improved ant colony algorithm is used to generate the initial path of AGV. In the third part, to avoid path conflicts between running AGVs, the authors propose a simple priority-based waiting strategy to avoid collisions. Findings The experiment shows that the solution can effectively deal with job scheduling and multiple AGV operation problems in the workshop. Originality/value In this paper, a collaborative work algorithm is proposed, which combines the job scheduling and AGV running problem to make the research results adapt to the real job environment in the workshop.

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Section: Literature Reviewmentioning

confidence: 99%

Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

Cheng,

Meng

2024

RIA

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“…26 Majdi et al 27 used fuzzy control techniques to navigate multi-AGVs in an unknown environment. Yu et al developed a mixed integer planning model for multi-AGV systems in path planning, 28 which has the advantage that it can be transformed into a series of subproblems before solving them one by one under certain conditions and adds penalty terms to the algorithm for generating paths. Digani et al 9 proposed an integrated approach based on a two-layer control architecture and automatic algorithms, as well as some classical algorithms such as A* Algorithm, Dijkstra Algorithm, and Ant Colony Algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…Yu et al. developed a mixed integer planning model for multi-AGV systems in path planning, 28 which has the advantage that it can be transformed into a series of subproblems before solving them one by one under certain conditions and adds penalty terms to the algorithm for generating paths. Digani et al.…”

Section: Related Workmentioning

confidence: 99%

A scheduling optimization method for multiple automated guided vehicle systems

Zhang,

Xu,

et al. 2024

International Journal of Advanced Robotic Systems

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When constructing a multiple automated guided vehicle (multi-AGV) system, it is essential to calculate the potential values of the system’s feature parameters. This article presents two time-cost calculators and an improved simulation platform for scientifically estimating the cost of constructing a multi-AGV system. By inputting the parameters of the number of automated guided vehicles (AGVs), the velocity of the AGV, the number of tasks, scheduling interval and path length, the designed calculator can output the system’s time expense when all tasks are completed. The designed simulation platform can display a specific multi-AGV system’s time cost and how the entire progress works in a visual interface. An advanced scheduling method based on handover mode and A* algorithm is used to solve task scheduling problems in a multi-AGV system. The overall performance of the newly integrated scheduling system is compared with other scheduling systems to validate its superiority and shortcomings in different corresponding work scenarios. The final results present a robust solution to improve a multi-AGV system’s effectiveness.

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Decentralized Multi-Agent Path Finding in Warehouse Environments for Fleets of Mobile Robots with Limited Communication Range

Maoudj

Christensen

2022

Lecture Notes in Computer Science

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A parallel algorithm for multi-AGV systems

Cited by 15 publications

References 41 publications

Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

Collaborative algorithm of workpiece scheduling and AGV operation in flexible workshop

A scheduling optimization method for multiple automated guided vehicle systems

Decentralized Multi-Agent Path Finding in Warehouse Environments for Fleets of Mobile Robots with Limited Communication Range

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