Which method is commonly used for obstacle avoidance in UAVs during autonomous flight?

The use of computer vision for obstacle avoidance in UAVs during autonomous flight is highly effective because it allows the drone to perceive its environment in real-time. Computer vision utilizes cameras and advanced algorithms to detect and interpret the surroundings, identifying obstacles such as trees, buildings, and other aircraft. This method enables the UAV to make timely decisions based on visual input, facilitating safe navigation by altering its flight path to avoid collisions.

Computer vision is particularly advantageous in dynamic environments where obstacles may be unpredictable or move unexpectedly. Unlike geofencing, which relies on pre-defined boundaries and does not actively respond to immediate obstacles, or GPS tracking, which provides location data but lacks the ability to recognize physical barriers, computer vision offers a proactive approach to obstacle avoidance. Manual override, while useful in certain scenarios, does not provide the automation necessary for independent navigation.

Overall, computer vision stands out as a preferred method in autonomous UAVs for its ability to integrate real-time sensory information, enhancing flight safety and operational efficiency.