skip to main content
10.1145/3627341.3630400acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiccvitConference Proceedingsconference-collections
research-article

An Empirical Study on Genetic Algorithm for 2D Path Planning of Unmanned Aerial Vehicles with Obstacle Avoidance

Published: 15 December 2023 Publication History

Abstract

Unmanned aerial vehicle (UAV), with high efficiency, low manpower costs, and flexible convenience, has attracted widespread attention in logistics. UAV trajectory planning is an essential component in this context. Genetic algorithm(GA) is an effective tool for solving complex route optimization problems. However, selecting the optimal combination of parameters for UAV path planning remains a challenging problem, especially in complex urban environments with numerous obstacles. This paper proposes a genetic algorithm-based UAV path planning approach(UAV_GA) that considers obstacle avoidance on a two-dimensional grid-based map. Furthermore, to investigate the general patterns in selecting parameters for GA in such problems, a comprehensive empirical study is conducted. The study analyzes the effects of population size, number of iterations, crossover, and mutation probabilities on algorithm performance. It aims to identify universal patterns for optimal parameter combinations and provide guidance for parameter selection in GA for UAV trajectory planning with obstacle avoidance.

References

[1]
G. Wu, M. Fan, J. Shi and Y. Feng, "Reinforcement Learning based Truck-and-Drone Coordinated Delivery," in IEEE Transactions on Artificial Intelligence.
[2]
D. Baek, Y. Chen, N. Chang, E. Macii and M. Poncino, "Energy-Efficient Coordinated Electric Truck-Drone Hybrid Delivery Service Planning," 2020 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE), Turin, Italy, 2020, pp. 1-6.
[3]
Huang, A. V. Savkin and C. Huang, "Drone Routing in a Time-Dependent Network: Toward Low-Cost and Large-Range Parcel Delivery," in IEEE Transactions on Industrial Informatics, vol. 17, no. 2, pp. 1526-1534, Feb. 2021.
[4]
Gerrits and P. Schuur, "Parcel Delivery For Smart Cities: A Synchronization Approach For Combined Truck-Drone-Street Robot Deliveries," 2021 Winter Simulation Conference (WSC), Phoenix, AZ, USA, 2021, pp. 1-12.
[5]
Shahzaad, B. Alkouz, J. Janszen and A. Bouguettaya, "Optimizing Drone Delivery in Smart Cities," in IEEE Internet Computing.
[6]
Perreault and K. Behdinan, "Delivery Drone Driving Cycle," in IEEE Transactions on Vehicular Technology, vol. 70, no. 2, pp. 1146-1156, Feb. 2021.
[7]
Huang and A. V. Savkin, "A Method of Optimized Deployment of Charging Stations for Drone Delivery," in IEEE Transactions on Transportation Electrification, vol. 6, no. 2, pp. 510-518, June 2020.
[8]
Palazzetti, C. M. Pinotti and G. Rigoni, "A run in the wind: favorable winds make the difference in drone delivery," 2021 17th International Conference on Distributed Computing in Sensor Systems (DCOSS), Pafos, Cyprus, 2021, pp. 109-116.
[9]
B. Sorbelli, F. Corò, L. Palazzetti, C. M. Pinotti and G. Rigoni, "How the Wind Can Be Leveraged for Saving Energy in a Truck-Drone Delivery System," in IEEE Transactions on Intelligent Transportation Systems, vol. 24, no. 4, pp. 4038-4049, April 2023.
[10]
P. M. Kornatowski, M. Feroskhan, W. J. Stewart and D. Floreano, "A Morphing Cargo Drone for Safe Flight in Proximity of Humans," in IEEE Robotics and Automation Letters, vol. 5, no. 3, pp. 4233-4240, July 2020.
[11]
S. Leng and H. Sun, "UAV Path Planning in 3D Complex Environments Using Genetic Algorithms," 2021 33rd Chinese Control and Decision Conference (CCDC), Kunming, China, 2021, pp. 1324-1330.
[12]
Z. Hao, "Research on Motion Path Planning of Industrial Robot Based on Genetic Algorithms," 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), Dublin, Ireland, 2019, pp. 578-582.
[13]
J. Xie and J. Chen, "Multiregional Coverage Path Planning for Multiple Energy Constrained UAVs," in IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 10, pp. 17366-17381, Oct. 2022.
[14]
Yu-Lin Lan Fa-Gui Liu, Wing W. Y. Ng, Jun Zhang, and Mengke Gui, “Decomposition based multi-objective variable neighborhood descent algorithm for logistics dispatching,” IEEE Transactions on Emerging Topics in Computational Intelligence, 2021, 5(5): 826-839.

Index Terms

  1. An Empirical Study on Genetic Algorithm for 2D Path Planning of Unmanned Aerial Vehicles with Obstacle Avoidance
        Index terms have been assigned to the content through auto-classification.

        Recommendations

        Comments

        Information & Contributors

        Information

        Published In

        cover image ACM Other conferences
        ICCVIT '23: Proceedings of the 2023 International Conference on Computer, Vision and Intelligent Technology
        August 2023
        378 pages
        ISBN:9798400708701
        DOI:10.1145/3627341
        Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 15 December 2023

        Permissions

        Request permissions for this article.

        Check for updates

        Author Tags

        1. genetic algorithm
        2. obstacle avoidance
        3. parameter configuration
        4. path planning
        5. unmanned aerial vehicle

        Qualifiers

        • Research-article
        • Research
        • Refereed limited

        Funding Sources

        • Hunan Provincial Natural Science Foundation of China
        • Science and Technology Development Project of Chenzhou
        • Scientific Research Fund of Hunan Provincial Education Department

        Conference

        ICCVIT 2023

        Acceptance Rates

        ICCVIT '23 Paper Acceptance Rate 54 of 142 submissions, 38%;
        Overall Acceptance Rate 54 of 142 submissions, 38%

        Contributors

        Other Metrics

        Bibliometrics & Citations

        Bibliometrics

        Article Metrics

        • 0
          Total Citations
        • 16
          Total Downloads
        • Downloads (Last 12 months)14
        • Downloads (Last 6 weeks)1
        Reflects downloads up to 20 Jan 2025

        Other Metrics

        Citations

        View Options

        Login options

        View options

        PDF

        View or Download as a PDF file.

        PDF

        eReader

        View online with eReader.

        eReader

        HTML Format

        View this article in HTML Format.

        HTML Format

        Media

        Figures

        Other

        Tables

        Share

        Share

        Share this Publication link

        Share on social media