Gas Leak Inspection Using Thermal, Visual and Depth Images and a Depth-Enhanced Gas Detection Strategy

Rangel, Johannes; Garzón, Julian; Sofrony, Jorge; Kroll, Andreas

doi:10.16924/riua.v0i42.40

Cited by 5 publications

(9 citation statements)

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“…The ongoing improvements in sensor technology and on-board processing of data are likely to further improve the prospects of using sUAS for infrastructure inspection over manual surveys. For example, recent advances in gas detectors and infrared (IR) sensors [30] can be potentially useful for on-board detection of leak events. To integrate the sUAS into the current monitoring and inspection processes followed by utilities, we need to address the following questions:…”

Section: Motivationmentioning

confidence: 99%

“…Step 1 Weighted Set Cover. In a localization set from the same pipeline network which consists of 36 nodes (monitoring locations) and 37 edges (components), an optimal solution of the weighted set cover is S = {1, 3,5,7,8,12,13,14,18,19,20,22,24,26,30, 32}, of which 20 is the base node. This step immediately reduces the overall problem size by limiting the number of monitoring locations to consider from the original 36 to 16.…”

Section: Example Problemmentioning

confidence: 99%

“…The following set P of initial simple routes is created following the three cases described earlier: (20,7,1,8,5,3,20), (20,14,12,13,20), (20,22,24,19,18,26,30,32,20).…”

Section: Example Problemmentioning

confidence: 99%

“…In the relocation procedure, node 19 is deleted from the third simple route and inserted into the second. The maximum travel time is reduced from the 0.95 hours associated with the initial route pair (20,14,12,13,20), (20,22,24,19,18,26,30,32,20) to 0.91 hours associated with the new route pair (20,19,14,12,13,20), (20,22,24,18,26,30,32,20). The final routes after this procedure are: (20,7,1,8,5,3,20), (20,19,14,12,13,20), (20,22,24,18,26,30,32,20).…”

Section: Example Problemmentioning

confidence: 99%

“…First, nodes 18 and 19 are exchanged, followed by 18 and 26, and then 26 and 30. The final routes after this procedure are: (20,7,1,8,5,3,20), (20,30,14,12,13,20), (20,22,24,19,18,26,32,20). The duration of the routes are now 0.84, 0.67, and 0.83 hours respectively.…”

Section: Example Problemmentioning

confidence: 99%

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Leveraging sUAS for Infrastructure Network Exploration and Failure Isolation

Lee

Dahan

Weinert

et al. 2018

J Intell Robot Syst

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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.

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Section: Motivationmentioning

confidence: 99%

Section: Example Problemmentioning

confidence: 99%

“…The following set P of initial simple routes is created following the three cases described earlier: (20,7,1,8,5,3,20), (20,14,12,13,20), (20,22,24,19,18,26,30,32,20).…”

Section: Example Problemmentioning

confidence: 99%