Research on Shortest Path BFS Strategy in Multi-AGV Scheduling System
Download PDF

Keywords

AGV
Path planning
AGV scheduling system
BFS algorithm

DOI

10.26689/jera.v8i3.7183

Submitted : 2024-05-20
Accepted : 2024-06-04
Published : 2024-06-19

Abstract

With the increasing maturity of automated guided vehicles (AGV) technology and the widespread application of flexible manufacturing systems, enhancing the efficiency of AGVs in complex environments has become crucial. This paper analyzes the challenges of path planning and scheduling in multi-AGV systems, introduces a map-based path search algorithm, and proposes the BFS algorithm for shortest path planning. Through optimization using the breadth-first search (BFS) algorithm, efficient scheduling of multiple AGVs in complex environments is achieved. In addition, this paper validated the effectiveness of the proposed method in a production workshop experiment. The experimental results show that the BFS algorithm can quickly search for the shortest path, reduce the running time of AGVs, and significantly improve the performance of multi-AGV scheduling systems.

References

Chen H, 2022, Research on Task Scheduling and Path Planning of Multi-AGV Scheduling System, dissertation, Hefei University of Technology.

Zhan Y, 2020, Research on Task Scheduling of Multi-AGV Systems, dissertation, Hubei University of Technology.

Shi T, 2015, Research and Implementation of Path Planning for Single AGV in Automated Stereoscopic Warehouse, dissertation, Anhui University of Science and Technology.

Xiao B, Wu X, Lou T, et al., 2004, Study on AGV Navigation Strategy Based on Fuzzy Data Fusion. Systems Simulation Journal, 16(1): 148–151.

Li J, Zhuang J, Wang S, 2005, Adaptive Trajectory Tracking Control of Wheeled Mobile Robots Based on Comprehensive Guidance. Journal of Xi’an Jiaotong University, 39(3): 252–255.

Chen Z, 2021, Research on Path Planning Optimization and Communication Adaptation Technology of AGV Scheduling System, dissertation, Institute of Mechanical Science Research, Beijing.

Wei X, 2021, Research on Efficient Task Allocation Algorithm for AGV Scheduling System, dissertation, Zhejiang University.

Chu J, 2021, Research on AGV Path Planning and Dynamic Scheduling in Intelligent Packaging Workshop, dissertation, Harbin Institute of Technology.

Tarjan R, 1972, Depth-First Search and Linear Graph Algorithms. SIAM J Comput, 1: 146–160.

Bundy A, Wallen L, 1984, Breadth-First Search. Proceedings of Catalogue of Artificial Intelligence Tools, 13

Karaman S, 2012, Sampling-based Algorithms for Optimal Path Planning Problems, dissertation, Massachusetts Institute of Technology, Cambridge.

Zhao J, Zhang Y, Ma Z, et al., 2018, Improvement and Verification of A-Star Algorithm for AGV Path Planning. Computer Engineering and Applications, 54(21): 217–223.

Wang X, Lu W, Feng C, et al., 2019, Path Planning Research Based on an Improved A* Algorithm from Obilerobot. IOP Conference Series: Materials Science and Engineering, 569(5): 52044.

Lao C, Li P, Feng Y, 2021, Greenhouse Robot Path Planning Based on Improved A* and DWA Algorithm Fusion. Journal of Agricultural Machinery, 52(1): 14–22.

Zong C, Lu L, Yu X, et al., 2017, A Spatial Multi-Degree-of-Freedom Robotic Arm Path Planning Method Based on A* Algorithm. Journal of Hefei University of Technology (Natural Science Edition), 40(2): 164–168.