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Researchers have developed two algorithms to help fleets of drones inspect electricity infrastructure more efficiently, potentially cutting costs and reducing risks to human workers. They provide details in the International Journal of Systems, Control and Communications.

The work offers solutions to two inspection tasks. The first is checking individual components on power transmission towers, such as insulators, and the second is surveying transmission lines for hazards. If unmanned aerial vehicles (UAVs), drones, are to be used for these two tasks, then multiple drones operating from a single base to cover routes would be optimal, but there is a need to minimize total flight distance.

For component inspections, the researchers developed an improved genetic algorithm. This is a computational method inspired by natural selection that progressively refines possible solutions. It combines a technique called 2-opt, which improves routes by reversing sections of a flight path, with an adaptive mutation rate. Mutations introduce changes into the possible solutions, which helps the algorithm to avoid poor routes.

For transmission-line inspections, the team developed a separate adaptive genetic algorithm to allocate line segments among drones and minimize unnecessary travel.

Tests on maps of different sizes compared its performance with a conventional genetic algorithm. These showed improvements in solution quality and efficiency over earlier approaches. The new methods also produced more stable results for component inspections and reduced flight distances while optimizing the number of drones deployed for line surveys.

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