Drones are increasingly used in various fields such as agriculture, logistics, and disaster response due to their agility and versatility. In indoor plant factories, small drones are used to monitor crop conditions and collect environmental data. However, small drones require frequent recharging due to their limited battery capacity, making autonomous charging systems essential for uninterrupted operation of drones. This study proposes an autonomous charging station designed for small drones in indoor plant factories. The system employs a wired charging mechanism to enhance charging efficiency, and a 3-degree-of-freedom (DOF) pose alignment system, utilizing an XY plotter and turntable, to correct drone landing errors. The alignment system ensures that drones, landing with random positions and orientations, are automatically adjusted to the correct position for charging. Experiments demonstrated that the charging station successfully aligned and charged drones with a 93% success rate on the first attempt. Even in cases of failure, the system automatically retried until a 100% success rate was achieved. This autonomous drone charging system has the potential to significantly enhance operational efficiency in indoor plant factories and can be adapted for various drone models in future applications.

Drone is an innovative industry that can combine the application of various technologies in the fourth industrial era, such as big data, artificial intelligence, and ICT. Although the synergy effects of these technologies will be great in various industrial ecosystems, drones are vulnerable to gusts such as "building wind" or "valley wind". Herein, the frequency domain of a mini drone was identified and a model-based disturbance observer (DOBs) was applied to implement the drone robust resistance against gusts. The frequency response of the Parrot Mambo or mini drone was measured with multi-sine excitation and the system dynamic parameters were identified. Based on the identified model, DOBs were designed and applied to the drone’s altitude, position, and yaw control. The effectiveness of the DOBs was verified with a sinusoidal disturbance. With the model-based DOB, 84.5% of the drone altitude responses, 50.7% of x responses, 52.1% of y responses, and 79.7% of yaw responses against sinusoidal disturbances were reduced. Flight responses were measured against wind disturbances with changing speed and direction. With the model-based DOBs, the drone"s altitude decreased by 87.7%, the x position by 53.0%, the y position by 60.6%, and the yaw angle by 56.2%.