Multi-Agent Tasks Scheduling for Coordinated Actions of Unmanned Aerial Vehicles Acting in Group

Multi-Agent Tasks Scheduling for Coordinated Actions of Unmanned Aerial Vehicles Acting in Group

Petr Skobelev Denis Budaev Aleksey Brankovsky Georgy Voschuk 

Samara National Research University, Samara State Technical University, Samara, Russia

Smart Solutions, Ltd, Samara, Russia

1 January 2018
| Citation



This paper discusses hardware and software prototype of multi-agent system for unmanned aerial vehicles (UAVs) group action planning. We describe the approach for system implementation as a whole and software agents within the system. The aim of current and future developments is creation of complex scientific and technical solutions for coordinated planning and actions management of heterogeneous UAV groups in real time.


adaptability, coordinated control, drones, dynamic rescheduling, intelligence, multi-agent systems, real time, UAV, Unmanned aerial vehicle

1. Introduction
2. Problem Statement
3. UAV Group Management
4. Results
5. Conclusion

This work was supported by Russian Foundation for Basic Research, project number 16-01-00759.


[1] Michael, L., Pinedo scheduling: theory, algorithms, and system, Springer, New York, p. 673, 2008.

[2] Vos, S., Meta-heuristics: the state of the art in local search for planning and scheduling, A. Nareyek (Ed.). Springer-Verlag, Berlin, pp. 1–23, 2001.

[3] Binitha, S. & Sathya, S.S., A survey of bio inspired optimization algorithms. International Journal of Soft Computing and Engineering (IJSCE), 2(2), pp. 137–151, ISSN: 2231-2307, 2012.

[4] Rzevski, G. & Skobelev, P., Managing complexity. WIT Press, Boston, 2014.

[5] Skobelev, P., Multi-agent systems for real time adaptive resource management. In Industrial agents: emerging applications of software agents in industry. Paulo Leitão, Stamatis Karnouskos (Ed.). Elsevier, Amsterdam, pp. 207–230, 2015.

[6] Santamaria, E., Segor, F., Tchouchenkov, I. & Schoenbein, R., Rapid aerial mapping with multiple heterogeneous unmanned vehicles. International Journal on Advances in Systems and Measurements, 6(3–4), pp. 384–393, 2013.

[7] Di Franco, C. & Buttazzo. G., Energy-aware coverage path planning of UAVs. Autonomous robot systems and competitions (ICARSC), 2015 IEEE International Conference, pp. 111–117, 2015.

[8] Kamrani, F., Using on-line simulation in UAV path planning. Licentiate Thesis in Electronics and Computer Systems, KTH, Stockholm, Sweden, 2007.

[9] Ergezer, H. & Leblebicioğlu, K., 3D path planning for multiple UAVs for maximum information collection. Journal of Intelligent & Robotic Systems, 73(1–4), pp. 737–762, 2014.

[10] Baxter, J.W., Horn, G.S. & Leivers, D.P., Fly-by-agent: controlling a pool of uavs via a multi-agent system. The 27th SGAI International Conference on Artificial Intelligence, 21(3), pp. 232–237, 2008.

[11] Koo, T.J. & Shahruz, S.M., Formation of a group of unmanned aerial vehicles (UAVs). American Control Conference (ACC), 2001, pp. 69–74, 2001.

[12] Austin, R., Unmanned aircraft systems UAVs design, development and deployment, 1st ed. Wiley Aerospace Series, United Kingdom, pp. 221–226, 2010.