doktorski rad
This thesis addresses the challenges of autonomous robotic systems for viticulture, specifically the challenges of automating tasks in vineyards. The study focuses on the development and functionalities of the Autonomous All-terrain Mobile Manipulator (ATMM), its positioning, navigation methods, and energy consumption metrics.
A comprehensive method for relative positioning of the robot with respect to the vine trunk is presented as the first contribution. Building on existing methods, the approach uses a neural network that analyzes RGB camera images to detect the vine trunk. It is assumed that the height of the vine is a uniform and known feature of the vine in an organized vineyard. Since the size of the detected object is known, the position of the vine trunk can be determined. This method, evaluated in the Jazbina vineyard, shows that the uniform size of the cultivated plants can provide important localization data to the mobile robots, allowing satisfactory localization accuracy. Navigation forms the core of the second contribution. The study describes the approaches of navigation of mobile manipulators in semi-structured vineyard environments. Starting with a formal description of the vineyard, the challenges of the environment for autonomous navigation are outlined. Key aspects such as global navigation, center line navigation in the aisle, and end row detection are highlighted. The combination of color and depth camera information for navigation for suckering tasks is explained. Experimental results demonstrate the feasibility
and efficiency of autonomous ATMM navigation in vineyards. Finally, energy consumption in such environments is discussed. The study presents methods to estimate the energy consumption of robots during specific operations in vineyards, supported by a geospatial data-based approach. By creating topological 3D models of vineyards, estimates of energy consumption during different operations, especially spraying and suckering, are presented and evaluated for selected vineyards, which form the third contribution.
Mobile Manipulator, Semi-structured Environment, Autonomous Navigation, Energy Consumption
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