Leveraging sUAS for Infrastructure Network Exploration and Failure Isolation
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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.
KeywordsUnmanned aerial systems Network inspection and repair operations Localization Failure identification Vehicle routing problems
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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.
- 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.Federal Aviation Administration: Authorizations Granted via Section 333 Exemptions. https://www.faa.gov/uas/beyond_the_basics/section_333/333_authorizations/ (2016)
- 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.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.Krefta, L.: Asset knowledge and integrity management earthquake playbook, gas pipeline integrity management program pacific gas electric company (2015)Google Scholar
- 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.Laporte, G., Semet, F.: Classical Heuristics for the Capacitated Vrp. In: The Vehicle Routing Problem, pp. 109–128. SIAM (2002)Google Scholar
- 22.Larson, R.C., Odoni, A.R.: Urban operations research. Monograph Dynamic Ideas (1981)Google Scholar
- 23.Leachtenauer, J.C., Driggers, R.G.: Surveillance and reconnaissance imaging systems: modeling and performance prediction. Artech House (2001)Google Scholar
- 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.Mohan, A., Poobal, S.: Crack detection using image processing: A critical review and analysis. Alexandria Engineering Journal (2017)Google Scholar
- 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.Pipeline and Hazardous Materials Safety Administration: National Pipeline Performance Measures. https://phmsa.dot.gov/pipeline/library/data-stats/performance-measures
- 29.Prosser, P., Shaw, P.: Study of greedy search with multiple improvement heuristics for vehicle routing problems (1996)Google Scholar
- 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.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.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.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