Nanocomposites have been widely used to improve material properties. Nanoscale reinforcement materials in vat photopolymerization resins improve the hardness, tensile strength, impact strength, elongation, and electrical conductivity of the printed products. This paper presents a literature review on the effects of reinforcement materials on nanocomposite properties. Additionally, preprocessing techniques, printing processes, and postprocessing techniques of nanocomposites are discussed. The nanocomposite properties are summarized based on their applications in the mechanical, electrical and magnetic, and biomedical industries. Future research directions are proposed to improve the material properties of printed nanocomposites.

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Autonomous navigation in unstructured off-road environments is greatly improved by semantic scene understanding. Conventional image processing algorithms are difficult to implement and lack robustness due to a lack of structure and high variability across off-road environments. The use of neural networks and machine learning can overcome the previous challenges but they require large labeled data sets for training. In our work we propose the use of hyperspectral images for real-time pixel-wise semantic classification and segmentation, without the need of any prior training data. The resulting segmented image is processed to extract, filter, and approximate objects as polygons, using a polygon approximation algorithm. The resulting polygons are then used to generate a semantic map of the environment. Using our framework. we show the capability to add new semantic classes in run-time for classification. The proposed methodology is also shown to operate in real-time and produce outputs at a frequency of 1Hz, using high resolution hyperspectral images.