When rover mission planners are laying out the path for their rover, they use a combination of stereo images and statistical and geological data in order to plot a course for the vehicle to follow for its mission. However, there is a lack of detailed images of the lunar surface that indicate the specific presence of hazards, such as craters, and the creation of such crater maps is time-consuming. There is also little known about how varying lighting conditions caused by the changing solar incidence angle affects perception as well. This paper addresses this issue by investigating how varying the incidence angle of the sun affects how well the human and AI can detect craters. It will also see how AI can accelerate the crater-mapping process, and how well it performs relative to a human annotating crater maps by hand. To accomplish this, several sets of images of the lunar surface were taken with varying incidence angles for the same spot and were annotated both by hand and by an AI. The results are observed, and then the AI performance was rated by calculating its resulting precision and recall, considering the human annotations as being the ground truth. It was found that there seems to be a maximum incidence angle for which detect rates are the highest, and that, at the moment, the AI’s detection of craters is poor, but it can be improved. With this, it can inform future and more expansive investigations into how lighting can affect the perception of hazards to rovers, as well as the role AI can play in creating these crater maps.

This dissertation describes a novel, low cost strategy of using particle streak (track) images for accurate micro-channel velocity field mapping. It is shown that 2-dimensional, 2-component fields can be efficiently obtained using the spatial variation of particle track lengths in micro-channels. The velocity field is a critical performance feature of many microfluidic devices. Since it is often the case that un-modeled micro-scale physics frustrates principled design methodologies, particle based velocity field estimation is an essential design and validation tool. Current technologies that achieve this goal use particle constellation correlation strategies and rely heavily on costly, high-speed imaging hardware. The proposed image/ video processing based method achieves comparable accuracy for fraction of the cost. In the context of micro-channel velocimetry, the usability of particle streaks has been poorly studied so far. Their use has remained restricted mostly to bulk flow measurements and occasional ad-hoc uses in microfluidics. A second look at the usability of particle streak lengths in this work reveals that they can be efficiently used, after approximately 15 years from their first use for micro-channel velocimetry. Particle tracks in steady, smooth microfluidic flows is mathematically modeled and a framework for using experimentally observed particle track lengths for local velocity field estimation is introduced here, followed by algorithm implementation and quantitative verification. Further, experimental considerations and image processing techniques that can facilitate the proposed methods are also discussed in this dissertation. Unavailability of benchmarked particle track image data motivated the implementation of a simulation framework with the capability to generate exposure time controlled particle track image sequence for velocity vector fields. This dissertation also describes this work and shows that arbitrary velocity fields designed in computational fluid dynamics software tools can be used to obtain such images. Apart from aiding gold-standard data generation, such images would find use for quick microfluidic flow field visualization and help improve device designs.