Abstract
In the modern world, the development of unmanned aerial vehicles (UAVs) is becoming an increasingly relevant and important task. Controlling the traffic of UAVs is a key component to ensure the safety and efficiency of their movement in airspace. This article explores mathematical models and methods that help address the challenges of UAV traffic management. The first section of the article is dedicated to mathematical models of UAV movement in a two-dimensional plane. The use of motion equations allows predicting trajectories and controlling the movement of UAVs, ensuring their safety and accurate location. The second section of the article discusses the important task of collision avoidance for multiple UAVs. The mathematical model of this task helps find optimal paths for each UAV to minimize mutual distances and avoid possible collisions in airspace. The third section discusses dynamic route planning for UAVs. The use of optimization methods allows finding the shortest route and optimizing fuel costs, which are crucial aspects for ensuring the efficiency of UAV operations. This article summarizes the importance of mathematical models and methods in UAV traffic management and emphasizes their role in ensuring the safety and reliability of unmanned aerial vehicles in airspace. The development of these models and methods is an important direction for further research and improvement of UAV traffic management systems.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kuchar, J.K., Yang, L.C., Boussemart, Y.: A review of conflict detection and resolution modeling methods. IEEE Trans. Intell. Transp. Syst. 3(1), 28–38 (2002). https://doi.org/10.1109/6979.994791
Khatib, W., Lesire, C.: A survey on motion planning and control techniques for robotic unmanned aerial vehicles. Annu. Rev. Control. 38(1), 113–132 (2014). https://doi.org/10.1016/j.arcontrol.2014.04.005
Rathinam, S., Koenig, S., Tovey, C.A.: Planning and replanning in domains with cost functions. In: Proceedings of the 19th National Conference on Artificial Intelligence, pp. 930–935. AAAI Press (2004). https://www.aaai.org/Papers/AAAI/2004/AAAI04-148.pdf
Ryan, J., How, J.P., Vian, J.: Motion planning in winds for UAVs. In: AIAA Guidance, Navigation, and Control Conference, p. 4435. American Institute of Aeronautics and Astronautics (2013). https://doi.org/10.2514/6.2013-4435
Frazzoli, E., Bullo, F.: Decentralized algorithms for fault-tolerant UAV formations. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 3, pp. 2124–2130 (2000). https://doi.org/10.1109/IROS.2000.893292
Rios-Torres, J., Horowitz, R.: Reach set approximation and control synthesis for autonomous pursuit-evasion. J. Guid. Control. Dyn. 31(2), 437–446 (2008). https://doi.org/10.2514/1.31233
Shishika, D., Kishida, M.: Unmanned aerial vehicle collision avoidance using receding horizon control. J. Guid. Control. Dyn. 39(1), 65–79 (2016). https://doi.org/10.2514/1.G001390
Bresina, J., Khatib, W.: A framework for online aircraft conflict detection and resolution. In: AIAA Guidance, Navigation, and Control Conference and Exhibit, p. 8580. American Institute of Aeronautics and Astronautics (2007). https://doi.org/10.2514/6.2007-8580
Hoffmann, G.M., Waslander, S.L., Tomlin, C.J.: Quadrotor helicopter trajectory tracking control. In: Proceedings of the 2007 American Control Conference, pp. 2246–2251. IEEE (2007). https://doi.org/10.1109/ACC.2007.4282405
Zeghal, K., Charfi, T.: A survey on collision avoidance in UAVs using optimization techniques. Aerosp. Sci. Technol. 78, 400–409 (2018). https://doi.org/10.1016/j.ast.2018.05.038
Ochieng, W.Y., See, L.M., Higgins, M., Tomlin, C.: Air traffic conflict resolution via local replanning. AIAA Guid. Navigat. Control Conf. Exhibit. (2002). https://doi.org/10.2514/6.2002-4593
Barros, A., Maza, I., Ollero, A.: Decentralized path planning for multiple UAVs: a potential fields based approach. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 552–557 (2006). https://doi.org/10.1109/ROBOT.2006.1641859
Chen, Y., How, J.P.: Decentralized path planning for multi-agent teams in complex environments. Auton. Robot. 40(1), 19–33 (2016). https://doi.org/10.1007/s10514-015-9425-1
Leung, J., Frazzoli, E.: Conflict resolution with continuous maneuvers using velocity obstacles. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 4292–4297 (2009). https://doi.org/10.1109/ROBOT.2009.5152644
Pertijs, M., Chu, Q., Keviczky, T., How, J.P.: Path planning for UAVs in wind with input constraints using mixed-integer linear programming. In: AIAA Guidance, Navigation, and Control Conference, p. 4908. American Institute of Aeronautics and Astronautics (2013). https://doi.org/10.2514/6.2013-4908
Lin, T.Y., Frazzoli, E., Tomlin, C.: A mixed logical dynamical approach to modeling and analysis of manned and unmanned aircraft operations. In: Proceedings of the IEEE Conference on Decision and Control (CDC), vol. 4, pp. 3833–3839 (2004). https://doi.org/10.1109/CDC.2004.1430375
Yang, S.J., How, J.P.: Robust trajectory optimization with probabilistic state constraints. J. Guid. Control. Dyn. 33(3), 896–903 (2010). https://doi.org/10.2514/1.45355
Rathinam, S., Tovey, C.A., Koenig, S.: Path planning for unmanned aerial vehicles to enable precision agriculture. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), vol. 3, pp. 2383–2388 (2004). https://doi.org/10.1109/ROBOT.2004.1307671
D’Andrea, R., Rizzi, A.: Decentralized collision avoidance for two UAVs in wind. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 361–366 (2005). https://doi.org/10.1109/ROBOT.2005.1570118
Sujit, P.B., Rathinam, S., How, J.P.: A mixed-integer linear programming approach for trajectory planning of multiple aircraft. J. Guid. Control. Dyn. 31(5), 1165–1176 (2008). https://doi.org/10.2514/1.32546
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Yena, M. (2024). Optimizing Air Traffic Control: Innovative Approaches to Collision Avoidance in UAV Operations. In: Nechyporuk, M., Pavlikov, V., Krytskyi, D. (eds) Integrated Computer Technologies in Mechanical Engineering - 2023. ICTM 2023. Lecture Notes in Networks and Systems, vol 996. Springer, Cham. https://doi.org/10.1007/978-3-031-60549-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-031-60549-9_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-60548-2
Online ISBN: 978-3-031-60549-9
eBook Packages: EngineeringEngineering (R0)