In this article, a novel approach is presented for dynamic route planning of unmanned aerial vehicles (UAVs) for
efficient data collection in industrial environments using UAVs connected to 5G and ultra-low power device networks.
The proposed approach is based on bio-inspired algorithms, which draw inspiration from the behavior of animals and
plants. The algorithm uses a combination of genetic algorithm (GA) and rapid exploration random tree (RRT) to
optimize the UAV’s route and reduce energy consumption, while complying with the UAV’s flight limitations,
considering the energy consumed by the UAV. This solution focuses on achieving higher energy efficiency and better
data collection capability in industrial environments. The combination of GA and RRT is capable of finding optimal
routes for data collection, reducing the UAV’s energy consumption while ensuring that all flight constraints are met.
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