A Method of Dual-AGV-Ganged Path Planning Based on the Genetic Algorithm

The genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection, and it is known for its iterative optimization capabilities in both constrained and unconstrained environments. In this paper, a novel method for GA-based dual-automatic guided vehicle (AGV)-ganged path plann...

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Bibliographic Details
Published in:Applied sciences 2024-09, Vol.14 (17), p.7482
Main Authors: Cai, Yongrong, Liu, Haibin, Li, Mingfei, Ren, Fujie
Format: Article
Language:English
Subjects:
Accuracy
Artificial intelligence
dual-AGV-ganged
genetic algorithm
Genetic algorithms
leader–follower AGVs
Monte Carlo simulation
Motion control
Navigation systems
Neural networks
Optimization
path planning
Planning
Robots
safe steering radius
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Summary:The genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection, and it is known for its iterative optimization capabilities in both constrained and unconstrained environments. In this paper, a novel method for GA-based dual-automatic guided vehicle (AGV)-ganged path planning is proposed to address the problem of frequent steering collisions in dual-AGV-ganged autonomous navigation. This method successfully plans global paths that are safe, collision-free, and efficient for both leader and follower AGVs. Firstly, a new ganged turning cost function was introduced based on the safe turning radius of dual-AGV-ganged systems to effectively search for selectable safe paths. Then, a dual-AGV-ganged fitness function was designed that incorporates the pose information of starting and goal points to guide the GA toward iterative optimization for smooth, efficient, and safe movement of dual AGVs. Finally, to verify the feasibility and effectiveness of the proposed algorithm, simulation experiments were conducted, and its performance was compared with traditional genetic algorithms, Astra algorithms, and Dijkstra algorithms. The results show that the proposed algorithm effectively solves the problem of frequent steering collisions, significantly shortens the path length, and improves the smoothness and safety stability of the path. Moreover, the planned paths were validated in real environments, ensuring safe paths while making more efficient use of map resources. Compared to the Dijkstra algorithm, the path length was reduced by 30.1%, further confirming the effectiveness of the method. This provides crucial technical support for the safe autonomous navigation of dual-AGV-ganged systems.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14177482