Abstract
Technological advances have opened up the possibility of using unmanned aerial vehicles (UAVs) in diverse environments. The mining industry has been looking for solutions to handle periodic inspections of the belt conveyors that transport iron ore. The state of the art indicates the use of UAVs for this task as an attractive, low-cost and safe alternative, allowing for a significant increase in security. A new concise mixed-integer linear programming (MILP) model is developed to address UAV routing and charging station planning for belt conveyor inspection. We conduct computational tests covering a real conveyor belt system in Brazil to validate the model in practical applications. The loading terminal possesses approximately 120 km of belt conveyors, leading to 230 inspection points. Instances of different sizes were generated by randomly sampling a subset of these points and using two different drone specifications. The results show that the new optimization modeling satisfies the problem requirements and is a significant contribution to the automation of inspection in the mining industry.
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