Matteo Tonin
Design and simulation of a machine learning-based approach for an autonomous UAV for use in Agriculture 4.0 applications.
Rel. Giorgio Guglieri, Nicoletta Bloise, Stefano Primatesta. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Aerospaziale, 2023
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Abstract
The aim of this thesis is to design, build and simulate an algorithm which provides guidance and control for autonomous UAVs in Agriculture 4.0 within the PRIN project “New technical and operative solutions for the use of drones in Agriculture 4.0”. The simulation scenario includes several targets which can represent plants, vegetables, or other vegetation where treatments deployed by the UAV is needed. The main objective of the developed algorithm is to guide the UAV, identifying and reaching all possible targets autonomously using information derived by depth cameras and other sensors. Another objective of the project is the application of the final algorithm with limited GPS or noisy signal conditions.
This, in fact, is one of the most critical working scenarios and traditional guidance based on mission definition through waypoints could fail to achieve an acceptable level of positioning precision depending on the UAV’s mission profile
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