  An autonomous drone flying near obstacles needs to be able to detect and
avoid the obstacles or it will collide with them. In prior work, drones can
detect and avoid walls using data from camera, ultrasonic or laser sensors
mounted either on the drone or in the environment. It is not always possible to
instrument the environment, and sensors added to the drone consume payload and
power - both of which are constrained for drones.
  This paper studies how data mining classification techniques can be used to
predict where an obstacle is in relation to the drone based only on monitoring
air-disturbance. We modeled the airflow of the rotors physically to deduce
higher level features for classification. Data was collected from the drone's
IMU while it was flying with a wall to its direct left, front and right, as
well as with no walls present. In total 18 higher level features were produced
from the raw data. We used an 80%, 20% train-test scheme with the RandomForest
(RF), K-Nearest Neighbor (KNN) and GradientBoosting (GB) classifiers. Our
results show that with the RF classifier and with 90% accuracy it can predict
which direction a wall is in relation to the drone.
