Three Dimensional Formation Control of Unmanned Aerial Vehicles in Obstacle Environments

Abstract

Today, the use of small unmanned aerial vehicles (UAVs) has increased due to technological advances. There has been an interest in using multiple UAVs instead of a single UAV to accomplish a given mission. This is because there are many scenarios where the capabilities of a single UAV are inadequate due to certain constraints (battery capacity, time in the air). For this reason, swarm UAV studies have increased. A swarm UAV consists of a large number of UAVs cooperating to accomplish a specific mission. This study shows three-dimensional formation control of a swarm UAV system in an obstacle environment. A centralized control architecture is used in this process. All task assignments are made from a centralized system. The Artificial potential fields method creates the formation at the target point by avoiding obstacles. The study was carried out using the Robot Operating System (ROS). The methods were tested in Webots simulation environment. Crazyflie robots were used in the experiments. In the simulation environment, square, star and v formations were first tested in two dimensions. Then, as an example of three-dimensional formation, the cubic and pyramid formations were created and observed.

Keywords

• Formation Control
• Robotics
• Swarm
• UAV

References

1. [1] L. Bayındır, “A review of swarm robotics tasks,” Neurocomputing, vol. 172, pp. 292–321, 2016.
2. [2] E. S¸ ahin, “Swarm robotics: From sources of inspiration to domains of application,” in International workshop on swarm robotics. Springer, 2004, pp. 10–20.
3. [3] Z. Jiao, Y. Zhang, J. Xin, L. Mu, Y. Yi, H. Liu, and D. Liu, “A deep learning based forest fire detection approach using uav and yolov3,” in 2019 1st International conference on industrial artificial intelligence (IAI). IEEE, 2019, pp. 1–5.
4. [4] N. Jayapandian, “Cloud enabled smart firefighting drone using internet of things,” in 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT). IEEE, 2019, pp. 1079–1083.
5. [5] M. Silvagni, A. Tonoli, E. Zenerino, and M. Chiaberge, “Multipurpose uav for search and rescue operations in mountain avalanche events,” Geomatics, Natural Hazards and Risk, vol. 8, no. 1, pp. 18–33, 2017.
6. [6] F. T¨ukenmez, “Harita m¨uhendsili˘ginde iha ile karayolu projelendirme,” T¨urkiye Fotogrametri Dergisi, vol. 3, no. 2, pp. 53–61, 2021.
7. [7] H. T. Do, H. T. Hua, M. T. Nguyen, C. V. Nguyen, H. T. Nguyen, H. T. Nguyen, and N. T. Nguyen, “Formation control algorithms for multiple-uavs: a comprehensive survey,” EAI Endorsed Transactions on Industrial Networks and Intelligent Systems, vol. 8, no. 27, pp. e3–e3, 2021.
8. [8] G. Skorobogatov, C. Barrado, and E. Salami, “Multiple uav systems: A survey,” Unmanned Systems, vol. 8, no. 02, pp. 149–169, 2020.

9. [9] A. Saeed, A. Abdelkader, M. Khan, A. Neishaboori, K. A. Harras, and A. Mohamed, “On realistic target coverage by autonomous drones,” ACM Transactions on Sensor Networks (TOSN), vol. 15, no. 3, pp. 1–33, 2019.
10. [10] V. Sharma, H.-C. Chen, and R. Kumar, “Driver behaviour detection and vehicle rating using multi-uav coordinated vehicular networks,” Journal of Computer and System Sciences, vol. 86, pp. 3–32, 2017.
11. [11] J. Scherer, S. Yahyanejad, S. Hayat, E. Yanmaz, T. Andre, A. Khan, V. Vukadinovic, C. Bettstetter, H. Hellwagner, and B. Rinner, “An autonomous multi-uav system for search and rescue,” in Proceedings of the first workshop on micro aerial vehicle networks, systems, and applications for civilian use, 2015, pp. 33–38.
12. [12] J. Dong, E. Nelson, V. Indelman, N. Michael, and F. Dellaert, “Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach,” in 2015 IEEE international conference on robotics and automation (ICRA). IEEE, 2015, pp. 5807–5814.
13. [13] B. D. Anderson, B. Fidan, C. Yu, and D. Walle, “Uav formation control: Theory and application,” in Recent advances in learning and control. Springer, 2008, pp. 15–33.
14. [14] A. S. Brandao and M. Sarcinelli-Filho, “On the guidance of multiple UAV using a centralized formation control scheme and delaunay triangulation,” Journal of Intelligent Robotic Systems, vol. 84, no. 1-4, pp. 397–413, Nov. 2015. [Online]. Available: https://doi.org/10.1007/s10846-015-0300-5
15. [15] D. Kim, H. Wang, G. Ye, and S. Shin, “Decentralized control of autonomous swarm systems using artificial potential functions: analytical design guidelines,” in 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601). IEEE, 2004. [Online]. Available: https://doi.org/10.1109/cdc.2004.1428623
16. [16] Z. Li, W. Xue, D. Li, D. Lin, and J. Huang, “Distributed differential game for formation control of multi-uav with obstacle avoidance,” in 2022 China Automation Congress (CAC). IEEE, 2022, pp. 4877–4882.
17. [17] H. S. Yavuz, H. Goktas, H. Cevikalp, and H. Saribas, “Optimal task allocation for multiple uavs,” in 2020 28th Signal Processing and Communications Applications Conference (SIU). IEEE, 2020, pp. 1–4.
18. [18] T. Wang, Q. Dang, and P. Pan, “A multi-robot system based on a hybrid communication approach,” Studies in Media and Communication, vol. 1, no. 1, pp. 91–100, 2013.
19. [19] M. A. Kamel, X. Yu, and Y. Zhang, “Formation control and coordination of multiple unmanned ground vehicles in normal and faulty situations: A review,” Annual reviews in control, vol. 49, pp. 128–144, 2020.
20. [20] Q. Ouyang, Z. Wu, Y. Cong, and Z. Wang, “Formation control of unmanned aerial vehicle swarms: A comprehensive review,” Asian Journal of Control, vol. 25, no. 1, pp. 570–593, 2023.
21. [21] N. H. Li and H. H. Liu, “Formation uav flight control using virtual structure and motion synchronization,” in 2008 American Control Conference. IEEE, 2008, pp. 1782–1787.
22. [22] X.-p. Xu, X.-t. Yan, W.-y. Yang, K. An, W. Huang, and Y. Wang, “Algorithms and applications of intelligent swarm cooperative control: A comprehensive survey,” Progress in Aerospace Sciences, vol. 135, p. 100869, 2022.
23. [23] D. Xu, X. Zhang, Z. Zhu, C. Chen, P. Yang et al., “Behavior-based formation control of swarm robots,” mathematical Problems in Engineering, vol. 2014, 2014.
24. [24] L. Joseph, Robot operating system (ros) for absolute beginners. Springer, 2018.
25. [25] L. Joseph and J. Cacace, Mastering ROS for Robotics Programming: Design, build, and simulate complex robots using the Robot Operating System. Packt Publishing Ltd, 2018.
26. [26] M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, A. Y. Ng et al., “Ros: an open-source robot operating system,” in ICRA workshop on open source software, vol. 3, no. 3.2. Kobe, Japan, 2009, p. 5.
27. [27] W. Giernacki, M. Skwierczy´nski, W. Witwicki, P. Wro´nski, and P. Kozierski, “Crazyflie 2.0 quadrotor as a platform for research and education in robotics and control engineering,” in 2017 22nd International Conference on Methods and Models in Automation and Robotics (MMAR). IEEE, 2017, pp. 37–42.
28. [28] J. A. Preiss, W. Honig, G. S. Sukhatme, and N. Ayanian, “Crazyswarm: A large nano-quadcopter swarm,” in 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017, pp. 3299–3304.
29. [29] M. J. Hong and M. Arshad, “A balance-artificial potential field method for autonomous surface vessel navigation in unstructured riverine environment,” Procedia Computer Science, vol. 76, pp. 198–202, 2015.