Improving AGV path planning efficiency using Genetic Algorithms with Hamming distance-based initialization - Breznikar, Žiga (Advances in production engineering and management, 2025)
This paper presents a Genetic Algorithm (GA) framework for warehouse navigation as a Travelling Salesman Problem (TSP) variant for Automated Guided Vehicles (AGVs). The warehouse layout is represented as a graph, where pick-up locations serve as terminal nodes. A distance matrix, computed via Breadth-First Search (BFS) enables efficient route evaluation. To promote diversity in the initial population, a Hamming distance-based vectorized initialization strategy is employed, ensuring that the chromosomes are maximally distinct. The GA balances exploration and exploitation by dynamically adjusting the fitness function. Early generations emphasize diversity, while later ones focus on solution refinement, improving convergence and avoiding premature stagnation. Our key contribution demonstrates that the Hamming distance-based approach achieves comparable or better results with significantly fewer chromosomes. This reduces computational cost and runtime, making the method well-suited for real-time AGV routing in warehouses. The framework is adaptable to structured environments and shows strong potential for integration into real-world logistics and robotics applications. Future work will focus on optimizing the algorithm and integrating it into the ROS 2 environment.
Keywords: automated guided vehicles (AGV); avtomatsko vodena vozila; combinatorial optimization; genetski algoritmi; genetsko programiranje; Hamming distance initialization; inicializacija Hammingove razdalje; kombinatorična optimizacija; Robot operating system 2 (ROS 2); Robotski operacijski sistem 2 (ROS 2); usmerjanje v skladišču; warehouse routing