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Mission Planning for Multiple UAVs in a Wind Field with Flight Time Constraints

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Abstract

The use of unmanned aerial vehicles (UAVs) has become common in a wide range of applications due to their agility and maneuverability. UAV mission planning is a challenging task, especially when dealing with multiple UAVs. In this paper, we consider a typical mission planning problem in which a fleet of UAVs must visit a set of fixed waypoints. Each waypoint has an associated reward that represents its relative importance. The problem consists in assigning a UAV to each waypoint, considering that each UAV has a flight time constraint. Also, we propose a model to solve the mission planning problem for multiple UAVs in the presence of a wind field. The goal is to maximize the total reward collected and minimize total flight time. Numerical experiments are carried out to illustrate the application of the proposed model in different instances of the multiple UAV mission planning problem. The results show that, when wind field is not taken into account, the error in the estimated total flight time required for the UAVs to complete their missions is about 13%. Also, if UAVs have flight time constraints, neglecting the wind field may cause the loss of some UAVs. The solutions provided by the proposed model allow the UAVs to complete their missions and return to the base safely.

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References

  • Afonso, R. J. M., Galvão, R. K. H., & Kienitz, K. H. (2012). Multitask trajectory planning based on predictive control. In Proceedings of the 2012 UKACC international conference on control (pp. 515–522).

  • Azevedo, G. T., Assis, P. A. Q., Pinto, V. P., & Galvão, R. K. H. (2018). Pseudospectral optimisation of UAV trajectories for minimal battery consumption in the presence of a wind field. In2018 UKACC 12th international conference on control (CONTROL) (pp. 272–276). https://doi.org/10.1109/CONTROL.2018.8516807.

  • Bernard, M., Kondak, K., Maza, I., & Ollero, A. (2011). Autonomous transportation and deployment with aerial robots for search and rescue missions. Journal of Field Robotics, 28(6), 914–931. https://doi.org/10.1002/rob.20401.

    Article  Google Scholar 

  • Bodin, L., Golden, B., Assad, A., & Ball, M. (1983). Routing and scheduling of vehicles and crews: The state of the art. Computers and Operations Research, 10(2), 63–211. https://doi.org/10.1016/0305-0548(83)90030-8.

    Article  MathSciNet  Google Scholar 

  • Cândido, A. S., Galvão, R. K. H., & Yoneyama, T. (2014). Control and energy management for quadrotor. In 2014 UKACC international conference on control (CONTROL) (pp. 343–348). https://doi.org/10.1109/CONTROL.2014.6915164.

  • Causa, F., Fasano, G., & Grassi, M. (2018). Multi-UAV path planning for autonomous missions in mixed GNSS coverage scenarios. Sensors,. https://doi.org/10.3390/s18124188.

    Article  Google Scholar 

  • Freitas, E., & Carvalho, J. R. H. (2015). Genetic algorithm approach for a class of multi-criteria, multi-vehicle planner of UAVs. In A. Gaspar-Cunha, C. H. Antunes, & C. C. Coello (Eds.), Evolutionary multi-criterion optimization, 8th international conference (EMO 2015), lecture notes in computer science (Vol. 9019, pp. 234–248). Guimarães: Springer.

  • Gualtieri, G., & Secci, S. (2012). Methods to extrapolate wind resource to the turbine hub height based on power law: A 1-h wind speed vs. weibull distribution extrapolation comparison. Renewable Energy, 43(Supplement C), 183–200. https://doi.org/10.1016/j.renene.2011.12.022.

    Article  Google Scholar 

  • Lawrance, N., & Sukkarieh, S. (2010). Simultaneous exploration and exploitation of a wind field for a small gliding UAV. In AIAA guidance, navigation, and control conference.

  • Liu, X., Gao, L., Guan, Z., Song, Y., & Zhang, R. (2016). A multi-objective optimization model for planning unmanned aerial vehicle cruise route. International Journal of Advanced Robotic Systems, 13(3), 116.

    Article  Google Scholar 

  • Liu, Y., Zhang, X., Zhang, Y., & Guan, X. (2019). Collision free 4D path planning for multiple UAVs based on spatial refined voting mechanism and PSO approach. Chinese Journal of Aeronautics, 32(6), 1504–1519. https://doi.org/10.1016/j.cja.2019.03.026.

    Article  Google Scholar 

  • Muppirala, T. (2013). Finding optimal path using optimization toolbox. Retrieved December 20, 2019, from https://www.mathworks.com/videos/finding-optimal-path-using-optimization-toolbox-68958.html.

  • Neumann, P. P., & Bartholmai, M. (2015). Real-time wind estimation on a micro unmanned aerial vehicle using its inertial measurement unit. Sensors and Actuators A: Physical, 235(Supplement C), 300–310. https://doi.org/10.1016/j.sna.2015.09.036.

    Article  Google Scholar 

  • Pferschy, U., & Staněk, R. (2017). Generating subtour elimination constraints for the TSP from pure integer solutions. Central European Journal of Operations Research, 25(1), 231–260. https://doi.org/10.1007/s10100-016-0437-8.

    Article  MathSciNet  MATH  Google Scholar 

  • Pinto, V. P., Galvão, R. K. H., Rodrigues, L. R., & Gomes, J. P. P. (2017). Controle e replanejamenteo de misssão de um quadrirotor baseado na disponibilidade energética da bateria. In XIII Simpósio Brasileiro de Automação Inteligente (pp. 1023–1030). Porto Alegre, RS, Brasil (in Portuguese).

  • Rodrigues, L. R., Gomes, J. P. P., & Alcântara, J. F. L. (2017a). Embedding remaining useful life predictions into a modified receding horizon task assignment algorithm to solve task allocation problems. Journal of Intelligent & Robotic Systems, 90, 133–145. https://doi.org/10.1007/s10846-017-0649-8.

    Article  Google Scholar 

  • Rodrigues, L. R., Gomes, J. P. P., Pinto, V. P., & Galvão, R. K. H. (2017b). Use of teaching-learning based optimization for filter parameter tuning in the prognostics of a quadrotor battery. In Annual conference of the prognostics and health management society (Vol. 8, pp. 1–11).

  • Rodriguez, L., Cobano, J. A., & Ollero, A. (2016a). Wind characterization and mapping using fixed-wing small unmanned aerial systems. In International conference on unmanned aircraft systems (pp. 178–184). IEEE.

  • Rodriguez, L., Cobano, J. A., & Ollero, A. (2016b). Wind field estimation and identification having shear wind and discrete gust features with a small UAS. In International conference on intelligent robots and systems (pp. 5638–5644). IEEE.

  • Shao, Z., Yan, F., Zhou, Z., & Zhu, X. (2019). Path planning for multi-UAV formation rendezvous based on distributed cooperative particle swarm optimization. Applied Sciences,. https://doi.org/10.3390/app9132621.

    Article  Google Scholar 

  • Souza, J. P. C., Marcato, A. L. M., de Aguiar, E. P., Jucá, M. A., & Teixeira, A. M. (2019). Autonomous landing of UAV based on artificial neural network supervised by fuzzy logic. Journal of Control, Automation and Electrical Systems,. https://doi.org/10.1007/s40313-019-00465-y.

    Article  Google Scholar 

  • Valenti, M., Bethke, B., How, J. P., Farias, D. P., & Vian, J. (2007) Embedding health management into mission tasking for UAV teams. In Proceedings of the America control conference (pp. 5777–5783). New York: IEEE.

  • Yang, S., Wei, N., Jeon, S., Bencatel, R., & Girard, A. (2017) Real-time optimal path planning and wind estimation using Gaussian process regression for precision airdrop. In 2017 American control conference (ACC) (pp. 2582–2587). https://doi.org/10.23919/ACC.2017.7963341.

  • Yoo, C., Fitch, R., & Sukkarieh, S. (2016). Online task planning and control for fuel-constrained aerial robots in wind fields. The International Journal of Robotics Research, 35(5), 438–453. https://doi.org/10.1177/0278364915595278.

    Article  Google Scholar 

  • Zhang, K., Chen, J., Chang, Y., & Shi, Y. (2016). EKF-based LQR tracking control of a quadrotor helicopter subject to uncertainties. In IECON 2016—42nd annual conference of the IEEE industrial electronics society (pp. 5426–5431). https://doi.org/10.1109/IECON.2016.7794149.

  • Zheng, X., Wang, F., & Li, Z. (2018). A multi-UAV cooperative route planning methodology for 3d fine-resolution building model reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing, 146, 483–494. https://doi.org/10.1016/j.isprsjprs.2018.11.004.

    Article  Google Scholar 

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Acknowledgements

The authors acknowledge the support of the São Paulo Research Foundation—FAPESP (Grant 2011/17610-0) and the Brazilian National Council for Scientific and Technological Development—CNPq (Grants 423023/2018-7, 305048/2016-3, 303714/2014-0 and 303393/2018-1). This study was also financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001 (PNPD Postdoctoral Grant).

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Authors and Affiliations

  1. Institute for Engineering and Sustainable Development, University of International Integration of the Afro-Brazilian Lusophony (Unilab), Redenção, CE, Brazil

    Vandilberto P. Pinto

  2. Postgraduate Program in Electrical and Computer Engineering, Federal University of Ceará, Sobral, CE, Brazil

    Vandilberto P. Pinto

  3. Electronic Engineering Division, Instituto Tecnológico de Aeronáutica, São José dos Campos, SP, Brazil

    Roberto K. H. Galvão & Leonardo R. Rodrigues

  4. Computer Science Department, Federal University of Ceará, Fortaleza, CE, Brazil

    João Paulo P. Gomes

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  1. Vandilberto P. Pinto
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  2. Roberto K. H. Galvão
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  3. Leonardo R. Rodrigues
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  4. João Paulo P. Gomes
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Correspondence to Vandilberto P. Pinto.

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Pinto, V.P., Galvão, R.K.H., Rodrigues, L.R. et al. Mission Planning for Multiple UAVs in a Wind Field with Flight Time Constraints. J Control Autom Electr Syst 31, 959–969 (2020). https://doi.org/10.1007/s40313-020-00609-5

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