Including Image-based Perception in Disturbance Observer for Warehouse Drones

TL;DR

This paper introduces an innovative image-based disturbance observer for warehouse drones that predicts and compensates for oscillations caused by object handling, enhancing stability during delivery tasks.

Contribution

It integrates deep learning-based disturbance prediction with traditional DOB to proactively reduce drone oscillations during object grasping and releasing.

Findings

Numerical studies show improved oscillation reduction.

The method effectively predicts disturbances before they occur.

Enhanced drone stability during object manipulation.

Abstract

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.