Abstract
The paper proposes multi-agent technology and a prototype system with together-acting UAVs for joint survey missions. The prototype makes it possible to connect UAVs in a united swarm, proposes coordinated flight plans and adaptively re-configures plans due to disruptive events. The approach to organization of program agents within a prototype subsystem is described. A series of simulation experiments and several flight tests were conducted to evaluate the effectiveness of the distributed scheduling mechanism. The aim of the current and future developments is creation of complex solutions for coordinated management of UAVs for precise agriculture.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Commercial Drone Industry Trends. https://blog.dronedeploy.com/commercial-drone-industry-trends-aae2010ff349. Accessed 30 Mar 2017
Skobelev, P., Budaev, D., Brankovsky, A., Voschuk. G.: Multi-agent tasks scheduling and control for coordinated actions of unmanned aerial vehicles acting in group. Int. J. Des. Nat. Ecody. 13(1), 39–45 (2018)
Heterogeneous Aerial Reconnaissance Team. https://en.wikipedia.org/wiki/Heterogeneous_Aerial_Reconnaissance_Team. Accessed 3 Nov 2017
CODE Takes Next Steps toward More Sophisticated, Resilient, and Collaborative Unmanned Air Systems. http://www.doncio.navy.mil/CHIPS/ArticleDetails.aspx?ID=7908
Service Academies Swarm Challenge Recap Teaser. https://www.youtube.com/watch?v=igz2dmDLOZY
Pinedo, M.: Scheduling: Theory, Algorithms, and System. Springer, New York (2008). https://doi.org/10.1007/978-1-4614-2361-4
Voß, S.: Meta-heuristics: The State of the Art. In: Nareyek, A. (ed.) LSPS 2000. LNCS (LNAI), vol. 2148, pp. 1–23. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45612-0_1
Binitha, S., Sathya, S.: A survey of bio inspired optimization algorithms. Int. J. Soft Comput. Eng. 2(2), 2231–2307 (2012)
Rzevski, G., Skobelev, P.: Managing Complexity. WIT Press, Boston (2014)
Skobelev, P.: Multi-agent systems for real time adaptive resource management. In: Leitão, P., Karnouskos, S. (eds.) Industrial Agents: Emerging Applications of Software Agents in Industry, pp. 207–230. Elsevier (2015)
Santamaria, E., Segor, F., Tchouchenkov, I., Schoenbein, R.: Rapid aerial mapping with multiple heterogeneous unmanned vehicles. Int. J. Adv. Syst. Meas. 6(3–4), 384–393 (2013)
Franco, C., Buttazzo, G.: Energy-aware coverage path planning of UAVs. In: Proceedings of Autonomous Robot Systems and Competitions IEEE International Conference (ICARSC), pp. 111–117 (2015)
Kamrani, F., Ayani, R.: Using on-line simulation for adaptive path planning of UAVs. In: Proceeding DS-RT 2007 Proceedings of the 11th IEEE International Symposium on Distributed Simulation and Real-Time Applications, pp. 167–174 (2007)
Acknowledgments
The work was supported by the Ministry of Education and Science of the Russian Federation in the framework of agreement №14.574.21.0183. The unique identification number: RFMEFI57417X0183.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Skobelev, P., Budaev, D., Gusev, N., Voschuk, G. (2018). Designing Multi-agent Swarm of UAV for Precise Agriculture. In: Bajo, J., et al. Highlights of Practical Applications of Agents, Multi-Agent Systems, and Complexity: The PAAMS Collection. PAAMS 2018. Communications in Computer and Information Science, vol 887. Springer, Cham. https://doi.org/10.1007/978-3-319-94779-2_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-94779-2_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-94778-5
Online ISBN: 978-3-319-94779-2
eBook Packages: Computer ScienceComputer Science (R0)