Abstract
Avoiding the multi-automated guided vehicle (AGV) path conflicts is of importance for the efficiency of the AGV system, and we propose a bi-level path planning algorithm to optimize the routing of multi-AGVs. In the first level, we propose an improved A* algorithm to plan the AGV global path in the global topology map, which aims to make the path shortest and reduce the AGV path conflicts as much as possible. In the second level, we present the dynamic rapidly-exploring random trees (RRT) algorithm with kinematic constraints to obtain the passable local path with collisions in the local grid map. Compared to the Dijkstra algorithm and classic A* algorithm, the simulation results showed that the proposed bi-level path planning algorithm performed well in terms of the search efficiency, significantly reducing the incidence of multiple AGV path conflicts.
Highlights
In recent years, with the development of industrial intelligence, unmanned storage, intelligent logistics, and intelligent factories have been gradually implemented and have become a reality [1,2].The automated guided vehicle (AGV) plays a vital role in significantly reducing transportation costs and improving transportation efficiency [3,4]
Path planning of AGV based on the classic A* algorithm and improved A* algorithm are shown
We studied the stability of the AGV system to control a single AGV, using an AGV and randomly
Summary
The automated guided vehicle (AGV) plays a vital role in significantly reducing transportation costs and improving transportation efficiency [3,4]. Recent papers focused on planning the paths of AGVs to complete transportation tasks more efficiently in the factory without collision, deadlock, and other traffic problems [5,6]. For the problem of AGV path planning, researchers have adopted different methods. There are three types of AGV path planning algorithms as far as we know, one of which is the classic graph search algorithm [7,8,9]. Kim and Jin [8] applied Dijkstra’s shortest-path algorithm to plan AGVs path through the concept of a time-windows graph. Chunbao Wang et al [9] presented a multi-AGV A*
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