Abstract
This work presents an obstacle detection algorithm using quadrotor AR. Drone 2.0 in a controlled environment with programming in OpenCV, ROS (Robot Operating System), and Python. The algorithm is based on the color object feature to determine it as an obstacle. It is the first step for future work where the drone is allowed to perform autonomous flights and obstacle avoidance. The ROS development environment makes it possible to adjust both the drone's linear and angular speed. The OpenCV management library allows processing the image obtained in real-time thanks to the cv_bridge package. The obstacle detection scenario is simulated using ROS-Gazebo, and then the algorithm is implemented in a real controlled environment. The results obtained were good; an obstacle detection system was successfully implemented using open-source tools.
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