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Rolling Horizon Path Planning of an Autonomous System of UAVs for Persistent Cooperative Service: MILP Formulation and Efficient Heuristics

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Abstract

A networked system consisting of unmanned aerial vehicles (UAVs), automated logistic service stations (LSSs), customer interface software, system orchestration algorithms and UAV control software can be exploited to provide persistent service to its customers. With efficient algorithms for UAV task planning, the UAVs can autonomously serve the customers in real time. Nearly uninterrupted customer service may be accomplished via the cooperative hand-off of customer tasks from weary UAVs to ones that have recently been replenished at an LSS. With the goal of enabling the autonomy of the task planning tasks, we develop a mixed integer linear programming (MILP) formulation for the problem of providing simultaneous. UAV escort service to multiple customers across a field of operations with multiple sharable LSSs. This MILP model provides a formal representation of our problem and enables use in a rolling horizon planner via allowance of arbitrary UAV initial locations and consumable reservoir status (e.g., battery level). As such, it enables automation of the orchestration of system activities. To address computational complexity, we develop efficient heuristics to rapidly derive near optimal solutions. A receding horizon task assignment (RHTA) heuristic and sequential task assignment heuristic (STAH) are developed. STAH exploits properties observed in optimal solutions obtained for small problems via CPLEX. Numerical studies suggest that RHTA and STAH are 45 and 2100 times faster than solving the MILP via CPLEX, respectively. Both heuristics perform well relative to the optimal solution obtained via CPLEX. An example demonstrating the use of the approach for rolling horizon planning is provided.

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References

  1. Valenti, M., Dale, D., How, J., Vian, J.: Mission health management for 24/7 persistent surveillance operations. In: Proceedings of the AIAA Guidance, Control and Navigation Conference, Myrtle Beach, SC, August (2007)

  2. Bethke, B., Valenti, M., How, J.: UAV task assignment. IEEE Robot. Autom. Mag. 15(1), 39–44 (2008)

    Article  Google Scholar 

  3. Nigam, N., Bieniawski, S., Kroo, I., Vian, J.: Control of multiple UAVs for persistent surveillance: algorithm flight test results. IEEE Trans. Control Syst. Technol. 20(5), 1236–1251 (2012)

    Article  Google Scholar 

  4. Ure, N.K., Chowdhary, G., Toksoz, T., How, J.P, Vavrina, M.A., Vian, J.: An automated battery management system to enable persistent missions with multiple aerial vehicles. IEEE/ASME Trans. Mechatron. 20(1), 275–286 (2015)

    Article  Google Scholar 

  5. Kemper, P.F., Suzuki, K.A.O., Morrison, J.R.: UAV consumable replenishment: design concepts for automated service station. J. Intell. Robot. Syst. 61(1), 369–397 (2011)

    Article  Google Scholar 

  6. Suzuki, K.A.O., Kemper, P.F., Morrison, J.R.: Automated battery replacement system for UAVs: analysis and design. J. Intell. Robot. Syst. 65(1), 563–586 (2012)

    Article  Google Scholar 

  7. Swieringa, K.A., Hanson, C.B., Richardson, J.R., White, J.D., Hasan, Z., Qian, E., Girard, A.: Autonomous battery swapping systems for small-scale helicopters. In: Proceedings of the IEEE International Conference on Robotics and Automation(ICRA), Anchorage, Alaske, pp. 3335–3340 (2010)

  8. Godzdanker, R., Rutherford, M.J., Valavanis, K.P.: ISLAND: a self-leveling landing platform for autonomous miniature UAVs. In: Proceedings IEEE/ASME International Conference on Advanced Intelligent Mechatronics, Budapest, Hungary, pp. 170–175 (2011)

  9. Fujii, K., Higuchi, K., Pekimoto, J.: Endless flyer: a continuous flying drone with automatic battery replacement. In: Proceedings of the IEEE International Conference on Ubiquitous Intelligence & Computing and IEEE International Conference on Autonomic & Trusted Computing, Sorrento Peninsula, Italy, pp. 216–223 (2013)

  10. Kim, W., Ji, K., Ambike, A.: Real-time operating environment for networked control systems. IEEE Trans. Autom. Sci. Eng. 3(3), 287–296 (2005)

    Google Scholar 

  11. Geramifard, A., Redding, J., Roy, N., How, J.P.: UAV cooperative control with stochastic risk models, pp. 3393–3398 (2011)

  12. Cocchioni, F., Pierfelice, V., Benini, A., Mancini, A., Frontoni, E., Zingaretti, P., Ippoliti, G., Longhi, S.: Unmanned ground and aerial vehicles in extended range indoor and outdoor missions (2014)

  13. Sharma, R.: Observability based control for cooperative localization. In: 2014 International Conference on Unmanned Aircraft Systems (ICUAS), Orlando, pp. 134–139 (2014)

  14. Ponda, S.S., Johnson, L.B., Geramifard, A., How, J.P.: Cooperative mission planning for multi-UAV teams Handbook of Unmanned Aerial Vehicles, pp. 1447–1490 (2014)

  15. Geramifard, A., Redding, J., How, J.P.: Intelligent cooperative control architecture: a framework for performance improvement using safe learning. J. Intell. Robot. Syst. 73(1), 83–103 (2013)

    Article  Google Scholar 

  16. Ure, N.K., Chowdhary, G., How, J.P., Vavrina, M.A., Vian, J.: Health aware planning under uncertainty for UAV missions with heterogeneous teams. In: Proceedings European Control Conference, Zurich, Switzerland, pp. 3312–3319 (2013)

  17. Shima, T., Schumacher, C.: Assignment of cooperating UAVs to simultaneous tasks using genetic algorithm. In: Proceedings of the AIAA Guidance, Navigation, and Control Conference and Exhibit, San Francisco (2005)

  18. Zeng, J., Yang, X., Yang, L., Shen, G.: Modeling for UAV resource scheduling under mission synchronization. J. Syst. Eng. Electron. 21(5), 821–826 (2010)

    Article  Google Scholar 

  19. Weinstein, A.L., Schumacher, C.: UAV scheduling via the vehicle routing problem with time windows. In: Proceedings of the AIAA Infotech@Aerospace 2007 Conference and Exhibit, Rohnert Park, California (2007)

  20. Kim, Y.S., Gu, D.W., Postlethwaite, I.: Real-time optimal mission scheduling and flight path selection. IEEE Trans. Autom. Control 52(6), 1119–1123 (2007)

    Article  MathSciNet  Google Scholar 

  21. Alidaee, B., Wang, H., Landram, F.: A note on integer programming formulations of the real-time optimal scheduling and flight selection of UAVS. IEEE Trans. Control Syst. Technol. 17(4), 839–843 (2009)

    Article  Google Scholar 

  22. Shetty, V.K., Sudit, M., Nagi, R.: Priority-based assignment and routing of a fleet of unmanned combat aerial vehicles. Comput. Oper. Res. 35(6), 1813–1828 (2008)

    Article  MATH  Google Scholar 

  23. Kim, J., Song, B.D., Morrison, J.R.: On the scheduling of systems of heterogeneous UAVs and fuel service stations for long-term mission fulfillment. J. Intell. Robot. Syst. 70(1), 347–359 (2013)

    Article  Google Scholar 

  24. Song, B.D., Kim, J., Kim, J., Park, H., Morrison, J.R., Shim, D.H.: Persistent UAV service: an improved scheduling formulation and prototypes of system components. J. Intell. Robot. Syst. 74(1), 221–232 (2014)

    Article  Google Scholar 

  25. Kim, J., Morrison, J.R.: On the concerted design and scheduling of multiple resources for persistent UAV operations. J. Intell. Robot. Syst. 74(1), 479–498 (2014)

    Article  Google Scholar 

  26. Kim, J., Shim, D.H., Morrison, J.R.: Tablet PC-based visual target-following system for quadrotors. J. Intell. Robot. Syst. 74(1), 85–95 (2014)

    Article  Google Scholar 

  27. Sundar, K., Rathinam, S.: Algorithms for routing an unmanned aerial vehicle in the presence of refeuling depots. IEEE Trans. Autom. Sci. Eng. 11(1), 287–294 (2014)

    Article  Google Scholar 

  28. Song, B.D., Kim, J., Morrison, J.R.: Towards real time scheduling for persistent UAV service: A rolling horizon MILP approach, RHTA and the STAH heuristic, pp. 505–516 (2014)

  29. Toth, P., Vigo, D.: Models, relaxations and exact approaches for the capacitated vehicle routing problem. Discret. Appl. Math. 123(1–3), 487–512 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  30. Longo, H., Aragao, M.P., Uchoa, E.: Solving capacitated arc routing problems using a transformation to the CVRP. Comput. Oper. Res. 33(6), 1823–1837 (2006)

    Article  MATH  Google Scholar 

  31. Ralphs, T.K., Kopman, L., Pulleyblank, W.R., Trotter, L.E.: On the capacitated vehicle routing problem. Math. Program. 94(2–3), 343–359 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  32. Ho, W., Ho, G.T.S., Ping, J., Lau, H.C.W.: A hybrid genetic algorithm for the multi-depot vehicle routing problem. Eng. Appl. Artif. Intell. 21(4), 548–557 (2008)

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Industrial and Systems Engineering, KAIST, 373-1 Guseong-dong, Yuseong-gu, Daejeon, 305-701, South Korea

    Byung Duk Song, Jonghoe Kim & James R. Morrison

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  1. Byung Duk Song
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  2. Jonghoe Kim
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  3. James R. Morrison
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Correspondence to James R. Morrison.

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Song, B.D., Kim, J. & Morrison, J.R. Rolling Horizon Path Planning of an Autonomous System of UAVs for Persistent Cooperative Service: MILP Formulation and Efficient Heuristics. J Intell Robot Syst 84, 241–258 (2016). https://doi.org/10.1007/s10846-015-0280-5

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  • DOI: https://doi.org/10.1007/s10846-015-0280-5

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