Energy-Constrained Multi-UAV Coverage Path Planning for an Aerial Imagery Mission Using Column Generation

  • Younghoon ChoiEmail author
  • Youngjun Choi
  • Simon Briceno
  • Dimitri N. Mavris


This paper presents a new Coverage Path Planning (CPP) method for an aerial imaging mission with multiple Unmanned Aerial Vehicles (UAVs). In order to solve a CPP problem with multicopters, a typical mission profile can be defined with five mission segments: takeoff, cruise, hovering, turning, and landing. The traditional arc-based optimization approaches for the CPP problem cannot accurately estimate actual energy consumption to complete a given mission because they cannot account for turning phases in their model, which may cause non-feasible routes. To solve the limitation of the traditional approaches, this paper introduces a new route-based optimization model with column generation that can trace the amount of energy required for all different mission phases. This paper executes numerical simulations to demonstrate the effectiveness of the proposed method for both a single UAV and multiple UAV scenarios for CPP problems.


Coverage path planning Multi-UAV missions Column generation Energy-constrained optimization 


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This paper is a major enhancement of the ICUAS 2018 accepted paper.


  1. 1.
    Acar, E., Choset, H., nad, P.N., Atkar, A.R., Hull, D.: Morse decompositions for coverage tasks. Int. J. Robot. Res. 21(4), 331–344 (2002)CrossRefGoogle Scholar
  2. 2.
    Atkar, P., Greenfield, A., Conner, D., Choset, H., Rizzi, A.: Uniform coverage of automotive surface patches. Int. J. Robot. Res. 24(11), 87–102 (1988)Google Scholar
  3. 3.
    Avellar, G.S.C., Pereira, G.A.S., Pimenta, L.C.A., Iscold, P.: Multi-UAV routing for area coverage and remote sensing with minimum time. Sensors 15, 27783–27803 (2015)CrossRefGoogle Scholar
  4. 4.
    Barnhart, C., Johnson, E.L., Nemhauser, G.L., Savelsbergh, M.W.P., Vance, P.H.: Branch-and-price: column generationn for solving huge integer probrams. INFORMS 46(3), 316–329 (1998)zbMATHGoogle Scholar
  5. 5.
    Barrientos, A., Colorado, J., del Cerro, J., Martinez, A., Rossi, C., Sanz, D., Valente, J.: Aerial remote sensing in agriculture: A practical approach to area coverage and path planning for fleets of mini aerial robots. J. Field Robot. 28(5), 667–689 (2011)CrossRefGoogle Scholar
  6. 6.
    Cao, Z., Huang, Y., Hall, E.: Region filling operations with random obstacle avoidance for mobile robotics. J. Robot. Syst. 5(2), 87–102 (1988)CrossRefGoogle Scholar
  7. 7.
    Choi, Y., Choi, Y., Briceno, S., Mavris, D.N.: Coverage path planning for a UAS imagery mission using column generation with a turn penalty. In: The 2018 International Conference on Unmanned Aircraft Systems. Dallas (2018)Google Scholar
  8. 8.
    Choi, Y., Choi, Y., Briceno, S., Mavris, D.N.: Three-dimensional uas trajectory optimization for remote sensing in an irregular terrain environment. In: The 2018 International Conference on Unmanned Aircraft Systems. Dallas (2018)Google Scholar
  9. 9.
    Choi, Y., Jimenez, H., Mavris, D.N.: Two-layer obstacle collision avoidance with machine learning for more energy-efficient unmanned aircraft trajectories. Robot. Auton. Syst. 98, 158–173 (2017)CrossRefGoogle Scholar
  10. 10.
    Choset, H., Pignon, P.: Coverage path planning: the boustrophedon decomposition. In: Proceedings of the International Conference on Field and Service Robotics. Canberra (1997)Google Scholar
  11. 11.
    Dantzig, G.B., Wolfe, P.: Decomposition principle for linear programmings. Oper. Res. 8, 101–111 (1960)CrossRefzbMATHGoogle Scholar
  12. 12.
    Desaulniers, G., Desrosiers, J., Solomon, M.M. (eds.): Column Generation. Springer, Berlin (2005)Google Scholar
  13. 13.
    Desrochers, M., Desposiers, J., Solomon, M.: A new optimization algorithm for the vehicle routing problem with time windows. Oper. Res. 40(2), 342–354 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Desrochers, M., Soumis, F.: A column generation approach to the urban transit crew scheduling problem. Transp. Sci. 23(1), 1–13 (1989)CrossRefzbMATHGoogle Scholar
  15. 15.
    Feillet, D., Dejax, P., Gendreau, M., Gueguen, C.: An exact algorithm for the elementary shortest path problem with resource constraints: application to some vehicle routing problem. Networks 44(3), 216–229 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Franco, C.D., Buttazzo, G.: Coverage path planning for UAVs photogrammetry with energy and resolution constraints. J. Intell. Robot. Syst. 83, 445–462 (2016)CrossRefGoogle Scholar
  17. 17.
    Galceran, E., Carreras, M.: A survey on coverage path planning for robotics. Robot. Auton. Syst. 61, 1258–1276 (2013)CrossRefGoogle Scholar
  18. 18.
    Clarke, G., Wright, J.W.: Scheduling of vehicles from a central depot to a number of delivery points. Oper. Res. 12(4), 568–581 (1964)CrossRefGoogle Scholar
  19. 19.
    Gilmore, P.C., Gomory, R.E.: A linear programming approach to the cutting-stock problem. Oper. Res. 9(6), 849–859 (1961)MathSciNetCrossRefzbMATHGoogle Scholar
  20. 20.
    Gilmore, P.C., Gomory, R.E.: A linear programming approach to the cutting-stock problem-part 2. Oper. Res. 11(6), 863–888 (1963)CrossRefzbMATHGoogle Scholar
  21. 21.
    Huang, W.H.: Optimal line-sweep-based decompositions for coverage algorithms. In: Proceedings of the 2001 IEEE International Conference on Robotics & Automation. Seoul (2001)Google Scholar
  22. 22.
    Jünger, M., Liebling, T., Naddef, D., Nemhauser, G., Pulleyblank, W., Reinelt, G., Rinaldi, G., Wolsey, L. (eds.): 50 Years of Integer Programming 1958-2008: From the Early Years to the State-of-the-Art. Springer, Berlin (2010)Google Scholar
  23. 23.
    Jin, J., Tang, L.: Optimal coverage path planning for arable farming on 2D surfaces. Trans. ASABE 53(1), 283–295 (2010)CrossRefGoogle Scholar
  24. 24.
    Kara, I.: Arc based integer programming formulations for distance constrained vehicle routing problem. In: LINDI 2011 - 3rd IEEE International Symposium on Logistics and Industrial Informatics. Budapest (2011)Google Scholar
  25. 25.
    Khan, A., Noreen, I., Habib, Z.: On complete coverage path planning algorithms for non-holonomic mobile robots: survey and challenges. J. Inf. Sci. Eng. 33, 101–121 (2017)MathSciNetGoogle Scholar
  26. 26.
    Leishman, J.G. (ed.): Principles of Helicopter Aerodynamics, 2nd edn. Cambridge University Press, Cambridge (2006)Google Scholar
  27. 27.
    Li, Y., Chen, H., Er, M.J., Wang, X.: Coverage path planning for UAVs based on enhanced exact cellular decomposition method. Mechatronics 21, 876–885 (2011)CrossRefGoogle Scholar
  28. 28.
    Maza, I., Ollero, A.: Multiple UAV cooperative searching operation using polygon area decomposition and efficient coverage algorithms. In: Alami, R., Chatila, R., Asama, H. (eds.) Distributed Autonomous Robotic Systems, vol. 6, pp 221–230. Springer Japan, Tokyo (2007)Google Scholar
  29. 29.
    Mufalli, F., Batta, R., Nagi, R.: Simultaneous sensor selection and routing of unmanned aerial vehicles for complex mission plans. Comput. Oper. Res. 39, 2787–2799 (2012)CrossRefzbMATHGoogle Scholar
  30. 30.
    Nam, L.H., Huang, L., Li, X.J., Xu, J.F.: An approach for coverage path planning for UAVs. In: 2016 IEEE 14th International Workshop on Advanced Motion Control (AMC) (2016)Google Scholar
  31. 31.
    Nedjati, A., Izbirak, G., Vizvari, B., Arkat, J.: Complete coverage path planning for a multi-UAV response system in post-earthquake assessment. Robotics, 26(5) (2016)Google Scholar
  32. 32.
    Okasanen, T., Visala, A.: Coverage path planning algorithms for agricultural field machines. J. Field Robot. 26(8), 651–668 (2009)CrossRefzbMATHGoogle Scholar
  33. 33.
    Russell, C., Jung, J., Willink, G., Glasner, B.: Wind tunnel and hover performance test results for multicopter UAS vehicles. In: AHS 72nd Annual Forum. West Palm Beach (2016)Google Scholar
  34. 34.
    Torres, M., Pelta, D.A., Verdegay, J.L., Torres, J.C.: Coverage path planning with unmanned aerial vehicles for 3D terrain reconstruction. Expert Syst. Appl. 55, 441–451 (2016)CrossRefGoogle Scholar
  35. 35.
    Toth, P., Vigo, D. (eds.): Vehicle Routing - Problems, Methods, and Applications, 2nd edn. Society for Industrial and Applied Mathematics, Philadelphia (2014)Google Scholar
  36. 36.
    Valente, J., Sanz, D., Cerro, J.D., Barrientos, A., de Frutos, M.A.: Near-optimal coverage trajectories for image mosaicing using a mini quad-rotor over irregular-shaped fields. Precision Agric. 14, 115–132 (2013)CrossRefGoogle Scholar
  37. 37.
    Viet, H., Dang, V., Laskar, M., Chung, T.: BA*: An online complete coverage algorithm for cleaning robots. Int. J. Appl. Intell. Neural Netw. Complex Problem Solving Technol. 39, 217–235 (2013)Google Scholar
  38. 38.
    Zelinsky, A., Jarvis, R., Byrne, J., Yuta, S.: Planning paths of complete coverage of an unstructured environment by a mobile robot. In: Proceedings of International Conference on Advanced Roboitcs, pp. 533–538 (1993)Google Scholar
  39. 39.
    Zillies, J., Westphal, S., Thakur, D., Kumar, V., Pappas, G., Scheidt, D.: A column generation approach for optimized routing and coordination of a UAV fleet. In: 2016 IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR). EPFL, Lausanne (2016)Google Scholar

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© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Aerospace EngineeringGeorgia Institute of Technology North AvenueAtlantaUSA

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