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

Generating 3D field models, commonly known as virtual outcrops, is getting increasingly popular amongst geoscientific researchers. While an aerial robot is often employed for the mapping process that involves taking a series of overlapping images, the manual data collection gets challenging due to various reasons, namely: 1) piloted flight over a straight path for outcrops having curved/uneven surface results in distorted images due to consistent auto-focusing, 2) wind disturbances make it difficult even for skilled pilots to precisely maintain the desired overlap, and 3) hiring of a skilled pilot is expensive as the outcrop generation requires hours of data collection. To tackle these challenges, we propose to fully automate the data acquisition process in this work. In that vein, firstly, the designed navigation algorithm takes care of the trajectory generation for mapping. Whereas, the proposed learning-based control framework, i.e. position tracking nonlinear model predictive controller in conjunction with Gaussian process-based disturbance regression, 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. (video)

Fuzzy Neural Network-Based Learning Control of Unmanned Aerial Vehicles

The performance of a model-based controller is quite satisfactory in case of having a precise mathematical model of the system as well as when uncertainties and parameter changes are negligible. However, for the case of small size UAVs, it is a difficult, time consuming and expensive task to obtain the precise model of the system. Moreover, such small UAVs are more vulnerable to transient environmental conditions which will destabilise them. In such cases, a model-based controller with a learning capability or a model-free controller is preferable; both methods will be implemented throughout this project. For the online learning purpose in the former method, the combination of artificial neural networks and fuzzy logic controllers will be implemented for the control of small size UAVs. In the latter method, artificial neural networks or fuzzy neural networks will be preferred. The most significant advantage of learning capability is that it is able to expand the operating envelope that cannot be modelled precisely in real-life. (video)