| Summary: | Aimed at the mission planning for cleaning photovoltaic panels in large-area photovoltaic plants with mobile cleaning robots, a district planning strategy is hereby proposed. The photovoltaic plants, considering the position of wind gaps, the illumination time, and other environmental factors, adopt a hierarchical mission planning based on the cleaning priority, and use the Hamilton graph to turn the cleaning problem of photovoltaic panels into a travelling salesman problem (TSP). Considering the disadvantages of low efficiency and early convergence of the genetic algorithm, an improved genetic algorithm, which includes the hybrid selection operator combining the tournament selection with the roulette wheel selection and the crossover operator based on the segmentation rule is thus put forward. The improved genetic algorithm is applied to plan the cleaning order of robots to clean the photovoltaic panels. The experimental results show that in comparison with the adaptive genetic algorithm, the improved genetic algorithm has a higher efficiency and better results.
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