Gaussian Process-based Learning Control of Aerial Robots for Precise Visualization of Geological Outcrops

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.