Gaussian Process-based Learning Control of Aerial Robots for Precise Visualization of Geological Outcrops
Mohit Mehndiratta, Erdal Kayacan · 2020
To generate 3D virtual maps of outcrops in geoscience, manual flights of aerial robots are often employed which is challenging due to various reasons: 1) piloted flight over curved/uneven surfaces requires auto-focusing, 2) wind disturbances make it difficult to precisely maintain the desired overlap, and 3) hiring a skilled pilot is expensive as the process requires hours of data collection. In this work, we propose to fully automate the visualization process using a learning-based control framework, i.e., position tracking nonlinear model predictive controller in conjunction with Gaussian process (GP)-based disturbance regression which facilitates a precise tracking of the generated path. Thanks to the long-short term memory feature of the designed GP model, the disturbance forces are accurately estimated even for increasing magnitude levels and time-periods. The simulation and real-world tests manifest that the proposed method could provide a time- and cost-saving yet reliable visualization framework.