skip to main content
10.1145/2908961.2931679acmconferencesArticle/Chapter ViewAbstractPublication PagesgeccoConference Proceedingsconference-collections
research-article

Multi-UAV Path Planning with Parallel Genetic Algorithms on CUDA Architecture

Published: 20 July 2016 Publication History

Abstract

In recent years, the use of Unmanned Aerial Vehicles (UAVs) has grown quickly due to its low cost and easily programming for autonomous path following for accomplishing different types of missions. Due to the numerous advantages of multi-UAVs, when comparing with a single powerful one, to perform reconnaissance, monitoring, detection and surveying missions the use of multi-UAVs is generally preferred. While the number of control points and the number of UAVs are increased, the complexity of the problem also increases. This paper presents a solution to the problem of minimum time coverage of ground areas using a number of UAVs. The solution is divided into two parts: Firstly the area is partitioned with K-means clustering and then the problem is solved in each cluster with parallel genetic algorithm approach on CUDA architecture. To illustrate the methodology, the paper presents the experimental results obtained with a multi-UAV system, which has a different number of control points. The results showed the proposed approach produces efficient solutions for these type NP-Hard problems of homeland security applications like wide-area surveillance and site security by using multiple UAVs.

References

[1]
J. d. S. Arantes, M. d. S. Arantes, C. F. M. Toledo, and B. C. Williams. A multi-population genetic algorithm for uav path re-planning under critical situation. In Tools with Artificial Intelligence (ICTAI), 27th International Conference on, pages 486--493. IEEE, 2015.
[2]
Y. Bao, X. Fu, and X. Gao. Path planning for reconnaissance uav based on particle swarm optimization. In Computational Intelligence and Natural Computing, Second International Conference on, volume 2, pages 28--32. IEEE, 2010.
[3]
Baykar. Technical Features of Bayraktar Mini UAS. http://baykarmakina.com/en/sistemler-2/bayraktar-mini-iha/#1458634622425-7f20b8f9-b28c. Accessed: 2016-04-03.
[4]
U. Cekmez, M. Ozsiginan, and O. K. Sahingoz. Adapting the ga approach to solve traveling salesman problems on cuda. In Computational Intelligence and Informatics (CINTI), 14th International Symposium on, pages 423--428. IEEE, 2013.
[5]
U. Cekmez, M. Ozsiginan, and O. K. Sahingoz. A uav path planning with parallel aco algorithm on cuda. In Unmanned Aircraft Systems, International Conference on, pages 347--354. IEEE, 2014.
[6]
Y. Lu, L. Zheng, L. Li, and M. Guo. Parallelism vs. speculation: exploiting speculative genetic algorithm on gpu. In Sixth International Workshop on Programming Models and Applications for Multicores and Manycores, pages 68--74. ACM, 2015.
[7]
F. Neumann and C. Witt. Bioinspired computation in combinatorial optimization: algorithms and their computational complexity. In 15th annual conference companion on Genetic and evolutionary computation, pages 567--590. ACM, 2013.
[8]
T. Phienthrakul. Clustering evolutionary computation for solving travelling salesman problems. International Journal of Advanced Computer Science and Information Technology, 3(3):243--262, 2014.
[9]
K. Rocki and R. Suda. Accelerating 2-opt and 3-opt local search using gpu in the travelling salesman problem. In High Performance Computing and Simulation (HPCS), 2012 International Conference on, pages 489--495. IEEE, 2012.
[10]
O. K. Sahingoz. Flyable path planning for a multi-uav system with genetic algorithms and bezier curves. In Unmanned Aircraft Systems (ICUAS), International Conference on, pages 41--48, May 2013.
[11]
S. Sancí and V.Işler. A parallel algorithm for uav flight route planning on gpu. International Journal of Parallel Programming, 39(6):809--837, 2011.
[12]
A. Sathyan, N. Boone, and K. Cohen. Comparison of approximate approaches to solving the travelling salesman problem & its application to uav swarming. Int. J. Unmanned Syst. Eng, 3(1):1--16, 2015.
[13]
B. M. Sathyaraj, L. C. Jain, A. Finn, and S. Drake. Multiple uavs path planning algorithms: a comparative study. Fuzzy Optimization and Decision Making, 7(3):257--267, 2008.

Cited By

View all
  • (2024)Research on a Distributed Cooperative Guidance Law for Obstacle Avoidance and Synchronized Arrival in UAV SwarmsDrones10.3390/drones80803528:8(352)Online publication date: 29-Jul-2024
  • (2024)Energy-Efficient Multi-UAV Collaborative Path Planning using Levy Flight and Improved Gray Wolf Optimization2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650411(1-8)Online publication date: 30-Jun-2024
  • (2024)UAV path planning techniques: a surveyRAIRO - Operations Research10.1051/ro/202407358:4(2951-2989)Online publication date: 31-Jul-2024
  • Show More Cited By

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GECCO '16 Companion: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion
July 2016
1510 pages
ISBN:9781450343237
DOI:10.1145/2908961
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 ACM 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]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 20 July 2016

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. 2-opt
  2. genetic algorithms
  3. k-means clustering
  4. multi-uav path planning
  5. parallel evolutionary algorithms

Qualifiers

  • Research-article

Conference

GECCO '16
Sponsor:
GECCO '16: Genetic and Evolutionary Computation Conference
July 20 - 24, 2016
Colorado, Denver, USA

Acceptance Rates

GECCO '16 Companion Paper Acceptance Rate 137 of 381 submissions, 36%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)27
  • Downloads (Last 6 weeks)10
Reflects downloads up to 17 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2024)Research on a Distributed Cooperative Guidance Law for Obstacle Avoidance and Synchronized Arrival in UAV SwarmsDrones10.3390/drones80803528:8(352)Online publication date: 29-Jul-2024
  • (2024)Energy-Efficient Multi-UAV Collaborative Path Planning using Levy Flight and Improved Gray Wolf Optimization2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10650411(1-8)Online publication date: 30-Jun-2024
  • (2024)UAV path planning techniques: a surveyRAIRO - Operations Research10.1051/ro/202407358:4(2951-2989)Online publication date: 31-Jul-2024
  • (2024)Collaborative Path Planning for Multi-UAVs by Evolutionary ComputingProceedings of 3rd 2023 International Conference on Autonomous Unmanned Systems (3rd ICAUS 2023)10.1007/978-981-97-1083-6_49(527-537)Online publication date: 26-Apr-2024
  • (2023)UAV Formation Trajectory Planning Algorithms: A ReviewDrones10.3390/drones70100627:1(62)Online publication date: 16-Jan-2023
  • (2022)Path-finding and Planning in a 3D Environment An Analysis Using Bidirectional Versions of Dijkstra’s, Weighted A*, and Greedy Best First Search Algorithms2022 2nd Asian Conference on Innovation in Technology (ASIANCON)10.1109/ASIANCON55314.2022.9909251(1-8)Online publication date: 26-Aug-2022
  • (2021)Parallel Algorithm on GPU for Wireless Sensor Data Acquisition Using a Team of Unmanned Aerial VehiclesSensors10.3390/s2120685121:20(6851)Online publication date: 15-Oct-2021
  • (2021)Multiunmanned Aerial Vehicle Path Planner on Graphics Processing UnitIEEE Canadian Journal of Electrical and Computer Engineering10.1109/ICJECE.2021.308829444:3(364-375)Online publication date: Oct-2022
  • (2021)An enhanced genetic algorithm for path planning of autonomous UAV in target coverage problemsApplied Soft Computing10.1016/j.asoc.2021.107796112(107796)Online publication date: Nov-2021
  • (2021)A review of artificial intelligence applied to path planning in UAV swarmsNeural Computing and Applications10.1007/s00521-021-06569-4Online publication date: 14-Oct-2021
  • Show More Cited By

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media