@inproceedings{487bc939f5284e039e3e607748999300,
title = "Path Planning for Cellular-connected UAV using Heuristic Algorithm and Reinforcement Learning",
abstract = "With the development of Unmanned Aerial Vehicle (UAV), a novel technology called cellular-connected UAV has been proposed to make UAV complete its mission more efficiently. We consider a scenario where UAV must take off from a random start point, travel over some specific points (e.g. collecting data from sparce sensors in large area) and reach a random end point while keep connected to the Ground Base Station. One of the major challenges is to plan the flying path of UAV while satisfies all constraints. We abstract the path planning problem into Travel Salesman Problem (TSP) and use A∗ combine with Genetic Algorithm, Simulated Annealing Algorithm and Reinforcement Learning Model to solve TSP to get the best path for cellular-connected UAV. In addition, we did experiments and recorded the results to analyze the advantages and disadvantages of these algorithms.",
keywords = "Cellular-connected UAV, Heuristic Algorithm, Reinforcement Learning, Travel Salesman Problem, UAV, path planning",
author = "Junqi Bao and Yunchu Yang and Yapeng Wang and Xu Yang and Zhenyu Du",
note = "Publisher Copyright: {\textcopyright} 2023 Global IT Research Institute (GiRI).; 25th International Conference on Advanced Communications Technology, ICACT 2023 ; Conference date: 19-02-2023 Through 22-02-2023",
year = "2023",
doi = "10.23919/ICACT56868.2023.10079278",
language = "English",
series = "International Conference on Advanced Communication Technology, ICACT",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "454--459",
booktitle = "25th International Conference on Advanced Communications Technology",
address = "United States",
}