Advertisement

Adaptive Multiple Task Assignments for UAVs Using Discrete Particle Swarm Optimization

  • Kun ChenEmail author
  • Qibo Sun
  • Ao Zhou
  • Shangguang Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11253)

Abstract

The forest fire is an extremely dangerous natural disaster. The traditional fire-fighting equipment have great difficulty in performing firefighting in mountain terrain. Unmanned aerial vehicles (UAVs) are coming into a popular form in forest firefighting. In view of the suddenness of forest fires, the adaptive and dynamic firefighting task assignment for UAV is of great significance, and the current firefighting task assignment cannot address this issue. This paper proposed an adaptive and dynamic multiple task assignment method for UAVs. Firstly, the adaptive and dynamic firefighting task assignment is formulated as an optimization problem. Secondly, an assignment algorithm is proposed to solve the problem by extending the particle swarm optimization (PSO) algorithm. Finally, the experiment results verify the effectiveness of the proposed algorithm.

Keywords

UAV Forest firefighting Task assignment Particle swarm optimization 

Notes

Acknowledgment

This research is supported in part by NSFC (61571066, 61602054), and Beijing Natural Science Foundation under Grant No. 4174100 (BNSF, 4174100).

References

  1. 1.
    Kou, K.-H., Yu, J.-Y., Wang, G., Zhang, F.-X.: Task assignment and route planning method of cooperative attack for manned/unmanned aerial vehicles. In: 2017 IEEE International Conference on Unmanned Systems (ICUS), pp. 168–176. IEEE (2017)Google Scholar
  2. 2.
    Yuan, C., Zhang, Y., Liu, Z.: A survey on technologies for automatic forest fire monitoring, detection, and fighting using unmanned aerial vehicles and remote sensing techniques. Can. J. For. Res. 45(7), 783–792 (2015)CrossRefGoogle Scholar
  3. 3.
    Shima, T., et al.: Multiple task assignments for cooperating uninhabited aerial vehicles using genetic algorithms. Comput. Oper. Res. 33(11), 3252–3269 (2006)CrossRefGoogle Scholar
  4. 4.
    Skulimowski, A.M.J.: Anticipatory control of vehicle swarms with virtual supervision. In: Hsu, C.-H., Wang, S., Zhou, A., Shawkat, A. (eds.) IOV 2016. LNCS, vol. 10036, pp. 65–81. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-51969-2_6CrossRefGoogle Scholar
  5. 5.
    Zhou, S., Yin, G., Wu, Q.: UAV cooperative multiple task assignment based on discrete particle swarm optimization. In: 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC), vol. 2, pp. 81–86. IEEE (2015)Google Scholar
  6. 6.
    Bello-Orgaz, G., Ramirez-Atencia, C., Fradera-Gil, J., Camacho, D.: GAMPP: genetic algorithm for UAV mission planning problems. In: Novais, P., Camacho, D., Analide, C., El Fallah Seghrouchni, A., Badica, C. (eds.) Intelligent Distributed Computing IX. SCI, vol. 616, pp. 167–176. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-25017-5_16CrossRefGoogle Scholar
  7. 7.
    Phan, C., Liu, H.H.: A cooperative UAV/UGV platform for wildfire detection and fighting. In: 7th International Conference on System Simulation and Scientific Computing (ICSC), pp. 494–498 (2008)Google Scholar
  8. 8.
    Ghamry, K.A., Kamel, M.A., Zhang, Y.: Multiple UAVs in forest fire fighting mission using particle swarm optimization. In: 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pp. 1404–1409. IEEE (2017)Google Scholar
  9. 9.
    Ghamry, K.A., Zhang, Y.: Cooperative control of multiple UAVs for forest fire monitoring and detection. In: 2016 12th IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), pp. 1–6. IEEE (2016)Google Scholar
  10. 10.
    Huang, H., Zhu, D., Ding, F.: Dynamic task assignment and path planning for multi-AUV system in variable ocean current environment. J. Intell. Robot Syst. 74(3–4), 999–1012 (2014)CrossRefGoogle Scholar
  11. 11.
    Jiang, X., Zhou, Q., Ye, Y.: Method of task assignment for UAV based on particle swarm optimization in logistics. In: Proceedings of the 2017 International Conference on Intelligent Systems, Metaheuristics & Swarm Intelligence, pp. 113–117. ACM (2017)Google Scholar
  12. 12.
    Oh, G., et al.: Market-based task assignment for cooperative timing missions in dynamic environments. J. Intell. Robot. Syst. 87(1), 97–123 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.The State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina

Personalised recommendations