Navigation Algorithms for Unmanned Ground Vehicles in Precision Agriculture Applications

With the rapid growth of the world population over the past years the agriculture industry is asked to respond properly to the exponential augmentation of global demand for food production. To do so, it is easy to infer that the agriculture output must be increased rapidly. However, due to environmental resources limitations, this task is not straightforward, therefore, new techniques aimed to maximize the efficiency of every single land in a sustainable way are required. To this purpose, as days pass, more and more researchers are focusing their attention on the development of innovative smart farming and precision agriculture technologies by introducing automation and robotics into the agricultural processes. This thesis is aimed to provide a low cost solution for an autonomous navigation in an unknown outdoor environment using only a camera sensor. More specifically, it presents two algorithms able to successfully carry out an autonomous navigation in vineyards adopting machine learning and computer vision techniques. Such algorithms have been developed for applications in vineyards but they can also be employed in orchards and in any other analogous scenarios. The work proposed in this thesis proved to be very reliable when performing motion planning by elaborating the images acquired by the camera. Nonetheless, it is to be considered as a starting point for further research in this sector.