Skip to main content
SpringerLink
Log in

A Real-Time Rerouting Method for Drone Flights Under Uncertain Flight Time

  • Published:
Journal of Intelligent & Robotic Systems Aims and scope Submit manuscript

Abstract

This paper proposes a method for real-time rerouting drone flights under uncertain flight times. The battery runtime that remains of a drone in real-time may be insufficient to complete its flight mission. This may be due to external factors, such as unexpected severe weather or obstacles that move into the drone’s flight path. Under unexpected situations, such as these, the drone cannot safely return to its depot, as planned. To ensure that the drone makes a safe return and that the flight mission is a success, there must be a real-time rerouting process for a drone’s flight path in response to unforeseeable circumstances. Hence, this paper proposes a real-time rerouting process consisting of two optimization models that generate an optimal alternative flight path for a drone that has insufficient remaining battery runtime. The first model is used to find an optimal flight path to visit all remaining target waypoints. If the first model fails to obtain a feasible solution, the second model is implemented to find an optimal flight path to minimize the number of unvisited waypoints. To confine the total flight (travel) time to the insufficient battery runtime, both models include the constraint associated with uncertain flight (travel) times between waypoints. To capture this uncertainty, this paper proposes a chance constrained programming (CCP) approach under the assumption of a known mean, variance, and flight time intervals. Numerical examples show how the proposed rerouting process works, and the CCP method results in more conservative solutions as compared to the deterministic approach.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Kim, S.J., Lim, G.J., Cho, J., Côté, M.J.: Drone-aided healthcare services for patients with chronic diseases in rural areas. J. Intell. Robot. Syst. 88(1), 163–180 (2017). https://doi.org/10.1007/s10846-017-0548-z

    Article  Google Scholar 

  2. Kim, S.J., Lim, G.J.: Drone-aided border surveillance with an electrification line battery charging system. J. Intell. Robot. Syst. 92(3–4), 657–670 (2018). https://doi.org/10.1007/s10846-017-0767-3

    Article  Google Scholar 

  3. Lim, G.J., Kim, S., Cho, J., Gong, Y., Khodaei, A.: Multi-UAV pre-positioning and routing for power network damage assessment. IEEE Trans. Smart Grid 9(4), 3643–3651 (2018). https://doi.org/10.1109/TSG.2016.2637408

    Article  Google Scholar 

  4. Kim, S.J., Lim, G.J.: A hybrid battery charging approach for drone-aided border surveillance scheduling. Drones 2(4), 38 (2018)

    Article  Google Scholar 

  5. Murray, C.C., Chu, A.G.: The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transp. Res. Part C: Emerg. Technol. 54, 86–109 (2015)

    Article  Google Scholar 

  6. Boutilier, J.J., Brooks, S.C., Janmohamed, A., Byers, A., Buick, J.E., Zhan, C., Schoellig, A.P., Cheskes, S., Morrison, L.J., Chan, T.C.: Optimizing a drone network to deliver automated external defibrillators. Circulation CIRCULATIONAHA–116 (2017)

  7. Singireddy, S.R.R., Daim, T.U.: Technology roadmap: Drone Delivery–Amazon Prime Air. In: Infrastructure and Technology Management, pp 387–412. Springer (2018)

  8. Torabbeigi, M., Lim, G.J., Kim, S.J.: Drone delivery scheduling optimization considering payload-induced battery consumption rates. Journal of Intelligent & Robotic Systems, 1–17. https://doi.org/10.1007/s10846-019-01034-w(2019)

  9. Colomina, I., Molina, P.: Unmanned aerial systems for photogrammetry and remote sensing: A review. ISPRS J. Photogramm. Remote. Sens. 92, 79–97 (2014)

    Article  Google Scholar 

  10. Amici, S., Turci, M., Giulietti, F., Giammanco, S., Buongiorno, M., La Spina, A., Spampinato, L.: Volcanic environments monitoring by drones mud volcano case study. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., 5–10 (2013)

  11. Cruz, H., Eckert, M., Meneses, J., Martínez, J.-F.: Efficient forest fire detection index for application in Unmanned Aerial Systems (UASs). Sensors 16(6), 893 (2016)

    Article  Google Scholar 

  12. Dabros, A., Pyper, M., Castilla, G.: Seismic lines in the boreal and arctic ecosystems of North America: Environmental impacts, challenges, and opportunities. Environ. Rev. 26(2), 214–229 (2018)

    Article  Google Scholar 

  13. Ceccarelli, N., Enright, J.J., Frazzoli, E., Rasmussen, S.J., Schumacher, C.J.: Micro UAV path planning for reconnaissance in wind. In: American Control Conference, 2007. ACC’07, pp 5310–5315. IEEE (2007)

  14. DJI: DJI GS pro. https://www.dji.com/(LastaccessedonJuly112008)https://www.dji.com/(LastaccessedonJuly112008) (2018)

  15. McHale, J.: Ground control stations for Unmanned Aerial Vehicles (UAVs) are becoming networking-hub cockpits on the ground for U.S. unmanned forces. https://www.militaryaerospace.com/(LastaccessedonJuly112018) (2010)

  16. Howard, C.: UAV command, control & communications, Military & Aerospace Electronics, militaryaerospace.com.

  17. Kim, S.J., Lim, G.J., Cho, J.: Drone flight scheduling under uncertainty on battery duration and air temperature. Comput. Industr. Eng. 117, 291–302 (2018). https://doi.org/10.1016/j.cie.2018.02.005

    Article  Google Scholar 

  18. Avellar, G.S., Pereira, G.A., Pimenta, L.C., Iscold, P.: Multi-UAV routing for area coverage and remote sensing with minimum time. Sensors 15(11), 27783–27803 (2015)

    Article  Google Scholar 

  19. Kundu, A., Matis, T.I.: A delivery time reduction heuristic using drones under windy conditions. In: IIE Annual Conference. Proceedings, Institute of Industrial and Systems Engineers (IISE), pp 1864–1869 (2017)

  20. Charnes, A., Cooper, W.W.: Chance-constrained programming. Manag. Sci. 6(1), 73–79 (1959)

    Article  MathSciNet  Google Scholar 

  21. Nemirovski, A., Shapiro, A.: Convex approximations of chance constrained programs. SIAM J. Optim. 17 (4), 969–996 (2006)

    Article  MathSciNet  Google Scholar 

  22. Zaghian, M., Lim, G.J., Khabazian, A.: A chance-constrained programming framework to handle uncertainties in radiation therapy treatment planning. Eur. J. Oper. Res. 266(2), 736–745 (2018)

    Article  MathSciNet  Google Scholar 

  23. Lin, Y., Saripalli, S.: Sampling-based path planning for uav collision avoidance. IEEE Trans. Intell. Transp. Syst. 18(11), 3179–3192 (2017)

    Article  Google Scholar 

  24. Yao, P., Wang, H., Su, Z.: Real-time path planning of unmanned aerial vehicle for target tracking and obstacle avoidance in complex dynamic environment. Aerosp. Sci. Technol. 47, 269–279 (2015)

    Article  Google Scholar 

  25. Gordon, G., Tibshirani, R.: Karush-kuhn-tucker conditions. Optimization 10(725/36), 725 (2012)

    Google Scholar 

  26. Wu, T.-H., Low, C., Bai, J.-W.: Heuristic solutions to multi-depot location-routing problems. Comput. Oper. Res. 29(10), 1393–1415 (2002)

    Article  Google Scholar 

  27. Kulkarni, R., Bhave, P.R.: Integer programming formulations of vehicle routing problems. Eur. J. Oper. Res. 20(1), 58–67 (1985)

    Article  MathSciNet  Google Scholar 

  28. DJI: Phantom 4 Pro. https://www.dji.com/phantom-4-pro, (Last accessed on December 14, 2016) (2017)

  29. Yang, W., Xu, H.: Distributionally robust chance constraints for non-linear uncertainties. Math. Program. 155(1–2), 231–265 (2016)

    Article  MathSciNet  Google Scholar 

  30. Calafiore, G.C., El Ghaoui, L.: On distributionally robust chance-constrained linear programs. J. Optim. Theory Appl. 130(1), 1–22 (2006)

    Article  MathSciNet  Google Scholar 

  31. Friedman, L., Golenko-Ginzburg, D., Sinuany-Stern, Z.: Determining control points of a production system under a chance constraint. Eng. Costs Product. Econ. 18(2), 139–144 (1989)

    Article  Google Scholar 

  32. GAMS Development, Corporation, General Algebraic Modeling System (GAMS) Release 24.9.2, Washington, DC, USA, http://www.gams.com/ (2017)

  33. IBM: CPLEX Optimizer. http://www.ibm.com/ (2015)

Download references

Author information

Authors and Affiliations

  1. Republic of Korea Army, Daejeon, 34079, South Korea

    Seon Jin Kim

  2. Department of Industrial Engineering, University of Houston, Houston, TX, 77204, USA

    Gino J. Lim

Authors
  1. Seon Jin Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gino J. Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Seon Jin Kim.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, S.J., Lim, G.J. A Real-Time Rerouting Method for Drone Flights Under Uncertain Flight Time. J Intell Robot Syst 100, 1355–1368 (2020). https://doi.org/10.1007/s10846-020-01214-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10846-020-01214-z

Keywords

Search

Navigation