Open Access

ARTICLE

Design and Validation of a Route Planner for Logistic UAV Swarm

Meng-Tse Lee^1,*, Ying-Chih Lai², Ming-Lung Chuang¹, Bo-Yu Chen¹

1 Department of Automation Engineering, National Formosa University, Yunlin, 632, Taiwan
2 Department of Aeronautics and Astronautics Engineering, National Cheng-Kung University, Tainan, 701, Taiwan

* Corresponding Author: Meng-Tse Lee. Email:

(This article belongs to this Special Issue: Machine Learning and Deep Learning for Transportation)

Intelligent Automation & Soft Computing 2021, 28(1), 227-240. https://doi.org/10.32604/iasc.2021.015339

Received 16 November 2020; Accepted 17 December 2020; Issue published 17 March 2021

Abstract

Unmanned Aerial Vehicles (UAV) are widely used in different fields of aviation today. The efficient delivery of packages by drone may be one of the most promising applications of this technology. In logistic UAV missions, due to the limited capacities of power supplies, such as fuel or batteries, it is almost impossible for one unmanned vehicle to visit multiple wide areas. Thus, multiple unmanned vehicles with well-planned routes become necessary to minimize the unnecessary consumption of time, distance, and energy while carrying out the delivery missions. The aim of the present study was to develop a multiple-vehicle mission dispatch system that can automatically compile a set of optimal paths and avoid passing through no-travel zones. For this function, the A* search algorithm was adopted to determine an alternative path that does not cross the no-travel zone when the distance array is set, and an improved two-phased Tabu search was applied to converge any initial solutions into a feasible solution. In this study, a group of five multicopters was set up to validate the swarm system, and the result shows that our improved 2TS+2OPT is able to converge to a better solution that allows logistic UAV swarms to operate in a more efficient way.

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Citations