Path Planning Algorithm for Multiple AGVs

Wong, Mun Keat (2019) Path Planning Algorithm for Multiple AGVs. Final Year Project (Bachelor), Tunku Abdul Rahman University College.

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Abstract

This project is conducted to design a path planning algorithm for multiple AGVs in finding their shortest paths to carry out their material handling tasks in a warehouse. Dijkstra’s algorithm will be used as the shortest path algorithm to compute an optimal path for the AGV to reach its working station in MATLAB. The system consists of multiple sets of nodes that will be created in MATLAB to represent the working stations in a warehouse and the pathway. Few experiments have been conducted to test the path planning algorithm with different number of AGVs deployed within a fixed size of warehouse map. The path planning algorithm is used to determine the nearest destination from its current location that the AGVs are needed to go to execute their material handling tasks. Total 12 experiments had been carried out with different number of AGVs and task numbers to test the feasibility of the algorithm. The results from the experiments show that when there are more AGVs that are being used to execute their material handling tasks with the path planning algorithm it takes lesser time to finish all the tasks or in other words the total distance travelled decreases. For the experiment that consist of one single AGV, the algorithm able to generate a shortest path for the AGV to move from starting point to perform multiple tasks and then exit through the exit point. Same thing goes with experiments that consist of 2 AGVs and 4 AGVs. The algorithm is able to generate individual shortest path for every AGV to move from starting point to perform multiple tasks and then to the exit point with priority given to preceding AGVs. In a nutshell, all the objectives in this project is achieved after a series of experiments that had been carried out. The algorithm offers a way to help AGVs to find their shortest path travel to multiple destinations efficiently (via shortest distance and optimized number of AGVs) and effectively (covering all destinations which required to go) and also the flexibility to test with number of AGVs. The main limitation in this project is the collision detection algorithm unable to be integrated into the program that is meant to be used together with the path planning algorithm. This algorithm will become the future work in this project by further studying the algorithm and integrate it into the path planning algorithm. This project able to contribute to supply chain management or warehouse management because this project produces a flexible, effective and efficient path planning algorithm for multiple destinations and multiple AGVs.