Deep neural networks to predict autonomous ground vehicle behavior on sloping terrain field

Conventional large agricultural machinery or implements are unsafe and unsuitable to operate on slopes $>6^{\circ }$ or 10%. Tractor rollovers are frequent on slopes, precluding farming on arable hills, uneven or highly sloped land. Therefore, a fleet of autonomous ground vehicles (AGV) is proposed to cultivate highly sloped land ($>6^{\circ }$). The fleet aims to expand agricultural land to the slopes and to strengths the human-robot collaboration in an unsafe sloped environment. However, the fleet's success largely depends on vehicle behavior models regarding traction, mobility, and energy consumption on varying slopes. The vehicle intelligent behavior models are essential and would solve multiple objectives ranging from simulations to path planning & navigation. Therefore, this study aimed to build a deep learning-based vehicle behavior models on sloping terrain. A standard drawbar test was performed on a single AGV operating on an actual sloped field at varying speeds and load conditions. The drawbar test quantified the AGV's behavior on slopes in metrics related to traction (traction efficiency), mobility (travel reduction), and energy consumption (power number). Deep learning-based models were developed from the experimental data to predict the AGV's behavior on slopes as a function of vehicle velocity, drawbar, and slope. A special model called the proposed model, which combined multiple deep neural networks with a mixture of Gaussians, was developed and trained with a hybrid training method. The proposed model consistently outperformed the other well-known machine learning models. This study explored the capabilities of machine learning algorithms to simulate the behavior of small-track vehicle or AGV on sloping terrain. The fleet aims to provide safer agriculture keeping human safety in focus, and the developed predictive vehicle behavior models would empower the fleet's operation on currently unsafe sloped terrain by assisting in vehicle path planning, route optimization, and decision making.     

Effects on print quality of varying acrylic binder types in water-based flexographic ink formulations

The ink industry is one of the world's largest markets due to the increasing demand for printing inks for the packaging industry. Flexography printing is a well-known promising technology for large-area printing due to its high printing speed and roll-to-roll capability to print economically on a variety of surfaces and is used in nearly in all areas of packaging printing. Water-based inks are considered non-toxic, odourless, and more environmentally friendly options compared to solvent-based inks. Therefore, in this article, the goal was to develop new water-based ink formulation with different acrylic binders for flexographic printing on commercial solid bleached sulphate (SBS) board. Five inks were formulated with four different acrylic binders and compared to a commercially available ink to study their performance. The developed inks were investigated with regard to their print qualities and print characteristics. It was found that the binder type influenced some print quality while the effect was not significant for others. Using flexography printed cyan inks, the ink formulated with the highest molecular weight had the lowest print density and the largest tonal value increase (TVI) observed between 40 and 60 tone values. The same ink had the largest mottle values and variation in topography. For values of print contrast and delta gloss at 75°, although differences were observed between average values, data had inconclusive variation and spread around averages, where no conclusive trends or effects of acrylic binder type on these response factors were observed. Print chroma and dot roundness results were equally close for all printed samples.