Development of a Simulator for UAV Swarm Operation Planning using Evolutionary Algorithms
Metehan Aydın1, Gazi Erkan Bostancı2*, Mehmet Serdar Güzel3, Koray Açıcı4
1Ankara University, Ankara, Türkiye
2Ankara University, Ankara, Türkiye
3Ankara University, Ankara, Türkiye
4Ankara University, Ankara, Türkiye
* Corresponding author: ebostanci@ankara.edu.tr
Presented at the Cognitive Models and Artificial Intelligence Conference (BMYZ2023), Ankara, Türkiye, Oct 26, 2023
SETSCI Conference Proceedings, 2023, 15, Page (s): 13-17 , https://doi.org/10.36287/setsci.6.1.009
Published Date: 29 December 2023 | 1228 1
Abstract
In latest years, UAVs are widely used in many areas. Their advantages attracts researches all around the world. When UAVs are wanted to be used effectively, swarm systems offer better and more advantageous solutions. Using a UAV swarm has some advantages over a single agent UAV but operation planning must be considered carefully. There are lots of operation planning algorithms in literature. If these algorithms are evaluated for their effectiveness, evolutionary algorithms comes to the fore. Operation planning algorithms and UAV swarms are generally tested in simulation environments. There are lots of simulation environments and tools. They can handle simulations in many areas ranging from aviation to automobile. However, they do not offer any features dedicated for UAV swarms. In this work, a simulation software is designed for UAV swarms. The designed simulation software can plan an operation for an UAV swarm using evolutionary algorithms and simulate them. In this paper, the designed software and its architecture is explained in details.
Keywords - UAV swarm, simulation, operation planning, evolutionary algorithms, genetic algorithms
References
Baballe, M. A., Bello, M. I., Alkali, A. U., Abdulkadir, Z., Muhammad, A. S., & Muhammad, F. (2022). The Unmanned Aerial Vehicle (UAV): Its Impact and Challenges. Journal homepage: https://gjrpublication. com/gjrecs, 2(03).
Iñaki Navarro and Fernando Matía, “An Introduction to Swarm Robotics,” ISRN Robotics, vol. 2013, Article ID 608164, 10 pages,2013.
E. Şahin, “Swarm robotics: from sources of inspiration to domains of application,” in Swarm Robotics Workshop: State-of-the-Art Survey, E Şahin and W. Spears, Eds., Lecture Notes in Computer Science, no. 3342, pp. 10–20, Berlin, Germany, 2005.
Cheraghi, Ahmad Reza, Sahdia Shahzad, and Kalman Graffi. "Past, present, and future of swarm robotics." Intelligent Systems and Applications: Proceedings of the 2021 Intelligent Systems Conference (IntelliSys) Volume 3. Springer International Publishing, 2022.
Sampedro, C., Bavle, H., Sanchez-Lopez, J. L., Fernández, R. A. S., Rodríguez-Ramos, A., Molina, M., & Campoy, P. (2016, June). A flexible and dynamic mission planning architecture for uav swarm coordination. In 2016 International Conference on Unmanned Aircraft Systems (ICUAS) (pp. 355-363). IEEE.
Whitley, Darrell. "An overview of evolutionary algorithms: practical issues and common pitfalls." Information and software technology 43.14 (2001): 817-831.
Spears, William M., et al. "An overview of evolutionary computation."European conference on machine learning. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993.
Fogel, David B. "The Advantages of Evolutionary Computation." Bcec (1997): 1-11.
Back, Thomas, Ulrich Hammel, and H-P. Schwefel. "Evolutionary computation: Comments on the history and current state." IEEE transactions on Evolutionary Computation 1.1 (1997): 3-17.
Mitchell, Melanie, and Charles E. Taylor. "Evolutionary computation: an overview." Annual Review of Ecology and Systematics 30.1 (1999):
593-616.
Pena-Reyes, Carlos Andrés, and Moshe Sipper. "Evolutionary computation in medicine: an overview." Artificial Intelligence in Medicine 19.1 (2000): 1-23.
Jones, Gareth. "Genetic and evolutionary algorithms." Encyclopedia of Computational Chemistry 2.1127-1136 (1998): 40.
Tian, Ye, et al. "Evolutionary large-scale multi-objective optimization: A survey." ACM Computing Surveys (CSUR) 54.8 (2021): 1-34.
Soria, Enrica, Fabrizio Schiano, and Dario Floreano. "SwarmLab: A MATLAB drone swarm simulator." 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2020.
Calderón-Arce, Cindy, Juan Carlos Brenes-Torres, and Rebeca SolisOrtega. "Swarm robotics: Simulators, platforms and applications review." Computation 10.6 (2022): 80.
(2023) MATLAB website [Online]. Available: https://www.mathworks.com/products/matlab.html
(2023) SCADE Suit website [Online]. Available: https://www.ansys.com/products/embedded-software/ansys-scadesuite
(2023) Qt Framework website [Online]. Available: https://www.qt.io/
Deb, Kalyanmoy, et al. "A fast and elitist multiobjective genetic algorithm: NSGA-II." IEEE transactions on evolutionary computation 6.2 (2002): 182-197
Zhang, Qingfu, and Hui Li. "MOEA/D: A multiobjective evolutionary algorithm based on decomposition." IEEE Transactions on evolutionary computation 11.6 (2007): 712-731.