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Leveraging sUAS for Infrastructure Network Exploration and Failure Isolation

  • Andrew C. LeeEmail author
  • Mathieu Dahan
  • Andrew J. Weinert
  • Saurabh Amin
Article
  • 86 Downloads

Abstract

Large-scale infrastructures are prone to simultaneous faults when struck by a natural or man-made event. The current operating procedure followed by many utilities needs improvement, both in terms of monitoring performance and time to repair. Motivated by the recent technological progress on small Unmanned Aerial Systems (sUAS), we propose a practical framework to integrate the monitoring capabilities of sUAS into standard utility repair operations. A key aspect of our framework is the use of monitoring locations for sUAS-based inspection of failures within a certain spatial zone (called a localization set). This set is defined based on the alerts from fixed sensors or customer calls. The positioning of monitoring locations is subject to several factors such as sUAS platform, network topology, and airspace restrictions. We formulate the problem of minimizing the maximum time to respond to all failures by routing repair vehicles to various localization sets and exploring these sets using sUAS. The formulation admits a natural decomposition into two sub-problems: the sUAS Network Exploration Problem (SNEP); and the Repair Vehicle Routing Problem (RVRP). Standard solvers can be used to solve the RVRP in a scalable manner; however, solving the SNEP for each localization set can be computationally challenging. To address this limitation, we propose a set cover based heuristic to approximately solve the SNEP. We implement the overall framework on a benchmark network.

Keywords

Unmanned aerial systems Network inspection and repair operations Localization Failure identification Vehicle routing problems 

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Notes

Acknowledgements

A shorter version of this paper was presented at the 2017 International Conference on Unmanned Aircraft Systems (ICUAS). This article elaborates on the overall framework, computational study, and heuristic approach. The work of M. Dahan, S. Amin, and A. Weinert was supported by ICAST: Intelligent Constrained Autonomous Strategic Tasking, which received financial support from MTSI Inc. and MIT MIT Lincoln Laboratory. The project benefited from useful advice by Mike Munizzi. The support of National Science Foundation through grants CNS-1239054 and CNS-1453126 is also greatly acknowledged.

DISTRIBUTION STATEMENT A. Approved for public release. Distribution is unlimited. This material is based upon work supported by the United States Air Force under Air Force Contract No. FA8702-15-D-0001. Any opinions, findings, conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the United States Air Force.

References

  1. 1.
    Angalakudati, M., Balwani, S., Calzada, J., Chatterjee, B., Perakis, G., Raad, N., Uichanco, J.: Business analytics for flexible resource allocation under random emergencies. Manag. Sci. 60(6), 1552–1573 (2014)CrossRefGoogle Scholar
  2. 2.
    Boland, N., Clarke, L., Nemhauser, G., et al.: The asymmetric traveling salesman problem with replenishment arcs. Euro. J. Oper. Res. 123(2), 408–427 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Bräysy, O., Gendreau, M.: Vehicle routing problem with time windows, part i: route construction and local search algorithms. Transp. Sci. 39(1), 104–118 (2005)CrossRefGoogle Scholar
  4. 4.
    Chabot, D., Craik, S.R., Bird, D.M.: Population census of a large common tern colony with a small unmanned aircraft. PloS one 10(4), e0122,588 (2015)CrossRefGoogle Scholar
  5. 5.
    Chvatal, V.: A greedy heuristic for the set-covering problem. Math. Oper. Res. 4(3), 233–235 (1979)MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    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
  7. 7.
    Croes, G.A.: A method for solving traveling-salesman problems. Oper. Res. 6 (6), 791–812 (1958)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Dadkhah, N., Mettler, B.: Survey of motion planning literature in the presence of uncertainty: Considerations for uav guidance. J. Intell. Robot. Syst. 65(1-4), 233–246 (2012)CrossRefGoogle Scholar
  9. 9.
    Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manag. Sci. 6(1), 80–91 (1959)MathSciNetCrossRefzbMATHGoogle Scholar
  10. 10.
    Deng, C., Wang, S., Huang, Z., Tan, Z., Liu, J.: Unmanned aerial vehicles for power line inspection: a cooperative way in platforms and communications. J. Commun. 9(9), 687–692 (2014)CrossRefGoogle Scholar
  11. 11.
    Erdoġan, S., Miller-Hooks, E.: A green vehicle routing problem. Transport Res E-Log 48(1), 100–114 (2012)CrossRefGoogle Scholar
  12. 12.
    Federal Aviation Administration: Unmanned Aircraft Systems (UAS) Operational Approval (N 8900.227). https://www.faa.gov/documentLibrary/media/Notice/N_8900.227.pdf. Accessed: Sept 12, 2017 (2013)
  13. 13.
    Federal Aviation Administration: Authorizations Granted via Section 333 Exemptions. https://www.faa.gov/uas/beyond_the_basics/section_333/333_authorizations/ (2016)
  14. 14.
    Floyd, R.W.: Algorithm 97: shortest path. Commun. ACM 5(6), 345 (1962)CrossRefGoogle Scholar
  15. 15.
    Golden, B.L., Levy, L., Vohra, R.: The orienteering problem. Nav. Res. Logist. 34(3), 307–318 (1987)CrossRefzbMATHGoogle Scholar
  16. 16.
    Graham, R.L.: Bounds for certain multiprocessing anomalies. Bell Labs Tech. J. 45(9), 1563–1581 (1966)CrossRefzbMATHGoogle Scholar
  17. 17.
    Kentucky Water Resources Research Institute: Water Distribution System Research Database. http://www.uky.edu/WDST/database.html. [Online; accessed February 22, 2017] (2016)
  18. 18.
    Kopardekar, P., Rios, J., Prevot, T., Johnson, M., Jung, J., Robinson, J.: Unmanned Aircraft System Traffic Management (Utm) Concept of Operations. In: AIAA Aviation Forum (2016)Google Scholar
  19. 19.
    Krefta, L.: Asset knowledge and integrity management earthquake playbook, gas pipeline integrity management program pacific gas electric company (2015)Google Scholar
  20. 20.
    Laporte, G., Nobert, Y., Arpin, D.: Optimal Solutions to Capacitated Multidepot Vehicle Routing Problems. Université de Montréal, Centre de recherche sur les transports (1984)Google Scholar
  21. 21.
    Laporte, G., Semet, F.: Classical Heuristics for the Capacitated Vrp. In: The Vehicle Routing Problem, pp. 109–128. SIAM (2002)Google Scholar
  22. 22.
    Larson, R.C., Odoni, A.R.: Urban operations research. Monograph Dynamic Ideas (1981)Google Scholar
  23. 23.
    Leachtenauer, J.C., Driggers, R.G.: Surveillance and reconnaissance imaging systems: modeling and performance prediction. Artech House (2001)Google Scholar
  24. 24.
    Lusk, R.M., Monday, W.H.: An early survey of best practices for the use of small unmanned aerial systems by the electric utility industry. Manual ORNL/TM-2017/93 oak ridge national laboratory (2017)Google Scholar
  25. 25.
    Mohan, A., Poobal, S.: Crack detection using image processing: A critical review and analysis. Alexandria Engineering Journal (2017)Google Scholar
  26. 26.
    Murvay, P.S., Silea, I.: A survey on gas leak detection and localization techniques. J. Loss Prev. Process Ind. 25(6), 966–973 (2012)CrossRefGoogle Scholar
  27. 27.
    Otero, L.: Proof of concept for using unmanned aerial vehicles for high mast pole and bridge inspections (fdot report no. bdv 28 977–02) (2015)Google Scholar
  28. 28.
    Pipeline and Hazardous Materials Safety Administration: National Pipeline Performance Measures. https://phmsa.dot.gov/pipeline/library/data-stats/performance-measures
  29. 29.
    Prosser, P., Shaw, P.: Study of greedy search with multiple improvement heuristics for vehicle routing problems (1996)Google Scholar
  30. 30.
    Rangel, J., Garzón, J., Sofrony, J., Kroll, A.: Gas leak inspection using thermal, visual and depth images and a depth-enhanced gas detection strategy. Revista de Ingeniería 1(42), 8–15 (2015)CrossRefGoogle Scholar
  31. 31.
    Sela Perelman, L., Abbas, W., Koutsoukos, X., Amin, S.: Sensor placement for fault location identification in water networks. Automatica 72(C), 166–176 (2016).  https://doi.org/10.1016/j.automatica.2016.06.005 CrossRefzbMATHGoogle Scholar
  32. 32.
    Taillard, É.D., Laporte, G., Gendreau, M.: Vehicle routeing with multiple use of vehicles. J. Oper. Res. Soc. 47(8), 1065–1070 (1996)CrossRefzbMATHGoogle Scholar
  33. 33.
    Vigo, D.: A heuristic algorithm for the asymmetric capacitated vehicle routing problem. Eur. J. Oper. Res. 89(1), 108–126 (1996)CrossRefzbMATHGoogle Scholar
  34. 34.
    Ware, J., Roy, N.: An analysis of wind field estimation and exploitation for Quadrotor Flight in the urban canopy layer. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1507–1514. IEEE (2016)Google Scholar
  35. 35.
    Weinert, A., Campbell, S., Vela, A., Schuldt, D., Kurucar, J.: A Well Clear Recommendation for Small Unmanned Aircraft Systems Based on Unmitigated Collision Risk. Tech. rep., MIT Lincoln Laboratory (2017)Google Scholar
  36. 36.
    Whipple, S.D.: Predictive Storm Damage Modeling and Optimizing Crew Response to Improve Storm Response Operations. Ph.D. thesis, Massachusetts Institute of Technology (2014)Google Scholar
  37. 37.
    Williams, K.W., Gildea, K.M.: A review of research related to unmanned aircraft system visual observers. Federal Aviation Administration Final Report (2014)Google Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Civil and Environmental EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  2. 2.Center for Computational EngineeringMassachusetts Institute of TechnologyCambridgeUSA
  3. 3.Massachusetts Institute of Technology Lincoln LaboratoryLexingtonUSA

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