No description available.

This master's thesis project investigates the impact of hydrophobicity and topology on dry and wet macroscopic particle accumulation on surfaces. The report begins by outlining the challenges associated with this phenomenon and the existing theories and methods used to study it, including adhesion, surface engineering, the Lotus effect, and humidity. The method chapter presents the case study of grass accumulation for robotic lawn mowing application and describes experiments conducted to simulate the accumulation of grass during robotic lawn mowing. These experiments explore two different phenomena: the accumulation of grass that is already stuck to a surface and the process by which grass initially sticks to a surface. Experiments have been done to change the hydrophobicity level of certain specimens and investigate how hydrophobicity and humidity affect the stickiness of grass when already accumulated on a surface. In addition, experiments are conducted by throwing grass towards surfaces with different hydrophobicity and topology to determine the influence of these variables on accumulation. Finally, a physical field test is conducted. The findings indicate that hydrophobicity up to 108,2° water contact angle has a low impact on both static and dynamic grass accumulation. Increased hydrophobicity does not tend to decrease grass accumulation, even showing increased accumulation in some experiments. Humidity and the wetness of the grass are more important factors. Experiments have shown that a textured surface can decrease the accumulation, showing significant results in a controlled environment. However, these results did not transfer over in a significant way to a real robotic lawn mowing situation.

Machine vision is hot research topic with findings being published at a high pace and more and more companies currently developing automated vehicles. Robotic lawn mowers are also increasing in popularity but most mowers still use relatively simple methods for cutting the lawn. No previous work has been published on machine learning networks that improved between cutting sessions by automatically collecting data and then used it for training. A data acquisition pipeline and neural network architecture that could help the mower in avoiding collision was therefor developed. Nine neural networks were tested of which a convolutional one reached the highest accuracy. The performance of the data acquisition routine and the networks show that it is possible to design a object detection model that improves between runs.

An Experimental Diagnostic Tool (EDT) was developed to increase the efficiency of the diagnostic process of robotic lawn mowers which resulted in a 200% productivity increase when utilizing a proposed formula specifically designed for the diagnostic process of robotic lawn mowers. The tool does not only extract and process data from the robotic lawn mower wirelessly but also highlights potential faults through an intuitive and easy-to-use interface - empowering servicing technicians who perform the diagnostics to perform at a higher level. Further, efficiency is a term widely used in various domains and contexts when measuring the capacity of a process. Proposed general definitions of the term have been given by previous authors and researchers. However, due to a lack of universally set definitions which fit all situations, the term remains ambiguous when improvements to a specific process are needed. The unclear definition is due to the variations within each process affecting the definition of both the term itself, but also similar terms fundamentally connected to it such as productivity, performance, and profitability. The following report contains an investigation of exploratory research where the understanding of efficiency and its related concepts are analyzed within the after-sales diagnostic process of robotic lawn mowers.