  Grasping and releasing objects would cause oscillations to delivery drones in
the warehouse. To reduce such undesired oscillations, this paper treats the
to-be-delivered object as an unknown external disturbance and presents an
image-based disturbance observer (DOB) to estimate and reject such disturbance.
Different from the existing DOB technique that can only compensate for the
disturbance after the oscillations happen, the proposed image-based one
incorporates image-based disturbance prediction into the control loop to
further improve the performance of the DOB. The proposed image-based DOB
consists of two parts. The first one is deep-learning-based disturbance
prediction. By taking an image of the to-be-delivered object, a sequential
disturbance signal is predicted in advance using a connected pre-trained
convolutional neural network (CNN) and a long short-term memory (LSTM) network.
The second part is a conventional DOB in the feedback loop with a feedforward
correction, which utilizes the deep learning prediction to generate a learning
signal. Numerical studies are performed to validate the proposed image-based
DOB regarding oscillation reduction for delivery drones during the grasping and
releasing periods of the objects.
