Exploring Optimal Path Planning : A Comparative Study of Algorithms for Automated Guided Vehicles

In today's industrial and logistics landscape, automated guided vehicles (AGVs) play a pivotal role in streamlining operations and boosting productivity. A critical aspect of AGV functionality lies in optimal path planning, where these vehicles navigate intricate environments from a starting point to a predefined destination while considering factors such as obstacle avoidance, time efficiency, and energy consumption. This paper presents a comprehensive comparative study of three widely utilized path planning algorithms: Dijkstra, A*, and Breadth-First Search (BFS). Moreover, the study offers practical insights and recommendations for selecting the most suitable algorithm tailored to specific application requirements. By shedding light on effective path planning strategies for AGVs, this research aims to drive advancements in automated systems, facilitating the development of more efficient, reliable, and adaptable industrial and logistics solutions in the age of automation. This study contributes to the enhancement of AGV technology, paving the way for optimized operations and improved performance in industrial and logistics domains. Keywords: A*, Breadth-First Search (BFS), Computational Complexity, Dijkstra, Efficiency, Logistics, Obstacle Avoidance, Optimization, Path Planning Algorithms