Multi-wing UAVs are widely used for surveillance and reconnaissance, aerial photography and surveying, search and rescue missions, communication relay and environmental monitoring. Most of the current manual control of UAVs is based on visual feedback, so obstacles in the operating environment can cause interference. Therefore, other sensory feedback, such as haptics, is needed to effectively convey obstacle information.
To this end, a team of researchers in South Korea has developed a wearable drone controller with vibrotactile feedback and gesture recognition. Obstacles in the drone's course are signaled by a vibrating motor on the user's wrist. Gestures are sensed by the IMU, which sends commands to the drone after machine learning-based recognition. IMU-based hand motion capture is relatively free in terms of distance between the drone and the user and does not require the drone to actively try to acquire gesture commands.