Exploring DJI Tello as an Affordable Drone Solution for Research and Education

Drones have gained significant attention across various fields; however, the high cost of advanced models may restrict their widespread use in education and research. This paper investigates the potential of affordable drones by presenting two studies on a specific low-cost model, the DJI Tello, to evaluate systems for autonomous detection, tracking, and trajectory execution. The first study introduces a real-time object detection and tracking system utilizing AI to detect and classify objects from the drone's camera feed. This system guides the drone to track and land on a moving ground robot carrying the target object. The study compares two tracking methods-distance-based and velocity-based-and presents significant results demonstrating the applicability of this drone model for object tracking. The second study enhances the drone's autonomy by integrating an external localization system (OptiTrack) to overcome hardware limitations and testing three trajectory execution algorithms. Results indicate that frequent corrections from OptiTrack significantly improve the precision of the drone's movements. By integrating detection, tracking, and external localization systems, this research demonstrates that low-cost drones like the DJI Tello EDU can perform real-time autonomous tasks effectively. This makes them valuable tools for research and educational settings The code for the experiments and the studies has been released to the community. ¹¹https://github.com/ropertUZ/tello_UZ.