Path planning of automated guided vehicles based on improved A-Star algorithm

TLDR

Automated guided vehicles are increasingly vital in flexible manufacturing and unmanned factories, improving efficiency, reducing labor costs, and unifying information flow, with path planning being a central challenge. The study addresses shortest‑time path planning and collision avoidance for multiple AGVs. An enhanced A* algorithm incorporating turning costs and edge‑removal heuristics is used to compute k shortest paths and dynamically plan collision‑free, time‑optimal routes. Simulations and experiments confirm the algorithm’s feasibility for efficient, collision‑free AGV path planning.

Abstract

With the development of automated logistics systems, flexible manufacture systems (FMS) and unmanned automated factories, the application of automated guided vehicle (AGV) gradually become more important to improve production efficiency and logistics automatism for enterprises. The development of the AGV systems play an important role in reducing labor cost, improving working conditions, unifying information flow and logistics. Path planning has been a key issue in AGV control system. In this paper, two key problems, shortest time path planning and collision in multi AGV have been solved. An improved A-Star (A*) algorithm is proposed, which introduces factors of turning, and edge removal based on the improved A* algorithm is adopted to solve k shortest path problem. Meanwhile, a dynamic path planning method based on A* algorithm which searches effectively the shortest-time path and avoids collision has been presented. Finally, simulation and experiment have been conducted to prove the feasibility of the algorithm.