This technology utilizes a genetic algorithm to maximize the movement efficiency of robots within a greenhouse. By considering variables such as inter-bed distance, base station locations, and bed cut points, it generates a population of greenhouse layouts and determines the optimal configuration by setting robot travel time as the fitness function.
When greenhouse layouts are fixed, robots often face excessive travel times back to base stations during tasks like pesticide spraying or harvesting, which is exacerbated by limited battery capacity and restricted movement ranges.
This technology employs a genetic algorithm to repeatedly initialize, mutate, crossover, and recombine greenhouse layout populations, ultimately calculating the optimal aisle and bed configuration to minimize the time required for a robot to travel from any point in the greenhouse back to its base station.
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