Journal of Intelligent & Robotic Systems

, Volume 93, Issue 1–2, pp 385–413 | Cite as

Leveraging sUAS for Infrastructure Network Exploration and Failure Isolation

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


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.


Unmanned 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.


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Copyright information

© Springer Nature B.V. 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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