Cybernetics and Computer Engineering, 2023, 1(211)
1International Research and Training Center for Information Technologies
and Systems of the National Academy of Sciences of Ukraine
and Ministry of Education and Science of Ukraine.
40, Akad. Glushkov ave., Kyiv, 03187, Ukraine.
2Shanxi Agricultural University
81, Longcheng str., Xiaodian Taiyuan, Shanxi, 030031, China.
Rue de la Fusée, 96, Brussels, 1130, Belgium
TRAJECTORY MOVEMENT CONTROL OF UNMANNED AERIAL VEHICLES IN A SWARM
Introduction. Today, the use of swarms of unmanned aerial vehicles (UAVs) is effective for solving the tasks of monitoring large areas of the earth’s surface and infrastructure objects, processing large areas of agricultural land, digital mapping, designing land objects in 3D, planning and designing construction works, road surface monitoring, etc. An important issue here is the potential for simultaneous conflicts between unmanned aerial vehicles moving in a swarm.
The purpose of the work is to develop a scalable, flexible method of controlling the trajectory of unmanned aerial vehicles in a swarm based on the approach of artificial potential fields.
The results. The developed method has properties of scalability and flexibility. The method contains a simple control algorithm, that allows several UAVs to fly as part of a swarm along a given trajectory, while solving the task of resolving conflict situations (preventing collisions between swarm members and with static and dynamic obstacles). The proposed method consists in decentralized real-time management of the swarm. The simulation results show that the method, presented in the article, increases the efficiency of swarm formation and flight performance, as well as UAV collision avoidance.
Conclusions. The proposed method scales well and is suitable for controlling a swarm of different sizes, it can also be applied to control a swarm of UAVs with different flight characteristics, since the formation of the resulting motion vector does not depend on the specific technical characteristics of the UAV, but takes into account certain limitations.
Keywords: unmanned aerial vehicle, modified artificial potential fields, UAV swarm, collision avoidance.
1. A. Benghezal, R. Louali, A. Bazoula, T. Chettibi. Trajectory generation for a fixed-wing UAV by the potential field method. 3rd International Conference on Control, Engineering & Information Technology (CEIT). Tlemcen. 2015. P. 1-6. DOI: 10.1109/CEIT.2015.7233049
2. Chen, H., Yin, Ch., Xie, L. Automatic Discovery of Subgoals Sequential Decision Problems Using Potential Fields. Conference Paper in Communications in Computer and Information Science. 2007. DOI: 10.1007/978-3-540-74282-1_66.
3. Y. U. Cao, A. S. Fukunaga, A. B. Kahng. Cooperative Mobile Robotics: Antecedents and Directions. Autonomous Robots. 1997. V.4. P. 1-23.
4. H. C. Hsu, A. Liu. Applying a Taxonomy of Formation Control in Developing a Robotic System. IEEE International Conference on Tools with Artificial Intelligence. 2005. P. 3-10.
5. D. Galzi, Y. Shtessel. UAV formations control using high order sliding modes. American Control Conference. Minneapolis. MN. 2006. P. 6. DOI: 10.1109/ACC.2006.1657386
6. Y. Huang, J. Tang, S. Lao. UAV Group Formation Collision Avoidance Method Based on Second-Order Consensus Algorithm and Improved Artificial Potential Field. Symmetry 11(9):1162. 2019. DOI: 10.3390/sym11091162.
7. H. Yin, L. Cam, U. Roy. Formation control for multiple unmanned aerial vehicles in constrained space using modified artificial potential field. MATHEMATICAL MODELLING OF ENGINEERING PROBLEMS. 2017. V.4. P. 100-105. DOI: 10.18280/mmep.040207.
8. L. Chaimowicz, V. Kumar. Aerial Shepherds: Coordination among UAVs and Swarm Robots. International Symposium on Distributed Autonomous Robotic Systems 2004.
9. P. Kostelnik, M. Samulka, M. Janosik. Scalable multi-robot formations using local sensing and communication. Proceedings of the Third International Workshop on Robot Motion and Control.2002. P. 319-324.
10. N. E. Leonard, E. Fiorelli. Virtual leaders, artificial potentials and coordinated control of groups. Proceedings of the IEEE Conference on Decision and Control. 2001. P. 2968-2973.
11. K. Sugihara, I. Suzuki. Distributed algorithms for formation of geometric patterns with many mobile robots. Robot Systems. 1996. V. 13.
12. M. Fields. Modeling large scale troop movement using reaction diffusion equations U.S. Army Research Laboratory. 1993.
13. Y. Koren and J. Borenstein. Potential field methods and their inherent limitations for mobile robot navigation. Proceedings of IEEE International Conference on Robotics and Automation 1991.
14. Ch. Huang, W. Li, Ch. Xiao, B. Liang, S. Han, Songchen. Potential field method for persistent surveillance of multiple unmanned aerial vehicle sensors. International Journal of Distributed Sensor Networks. 14. 2018. DOI: 10.1177/1550147718755069.
15. V. Kharchenko, V. Chepizhenko, S. Pavlova. Synergy of Piloted, Remotely Piloted and Unmanned Air Systems in Single Air Navigation Space. 2016. DOI: 10.13140/RG.2.1.1502.8885.
16. I. Skyrda. Decentralized Autonomous Unmanned Aerial Vehicle Swarm Formation and Flight Control. Information and Communication Technologies in Education, Research, and Industrial Applications. 14th International Conference, ICTERI 2018. Kyiv, Ukraine, May 14-17, 2018, Revised Selected Papers.2018. P. 197-219. DOI 978-3-030-13929-2
17. W. Kang, N. Xi, Y. Zhao, J. Tan, Y. Wang. Formation control of multiple autonomous vehicles: Theory and experimentation. Proceedings of IFAC 15th Triennial World Congress. 2002.
18. N. Leonard, E. Fiorelli. Leaders, artificial potentials and coordinated control of groups. Conference on Decision and Control. 2001.