This thesis presents the research and development behind the integration of an autonomous propulsion system for a four-wheeled Ground Penetrating Radar (GPR) measurement unit, previously requiring manual operation. In order to ease the administration of the complex new system, Robot Operating System (ROS) 2 was used as middleware, where an implementation of Light Detection And Ranging (LiDAR) 3D-SLAM (Simultaneous Localization And Mapping) served to secure precise localization of the Unmanned Ground Vehicle (UGV) and mapping of its environment. This, with the ultimate goal of enabling accurate survey execution along paths optimized for various dynamic indoor- and outdoor environments. From a more general point of view, this work can also act as a hardware- and software selection guide for similar projects, especially if stricter physical limitations apply and the autonomous system is not considered the primary system, but the majority of the internal enclosed space of the UGV is reserved for higher purpose equipment or storage requirements. In this first prototype iteration, the mapping accuracy of the autonomous system reached centimeter precision and the execution of surveys in grid- and spiral patterns yielded position accuracies of 5(2) cm and 6(4) cm, respectively. These results are indeed very promising and show the proof of concept needed to enter the next development phase.

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Farmworkers in Apopka, FL, have been subjected to overhead pesticide exposure since the 1940s. Pesticides including Paraquat (PQ), Metribuzin and Aldicarb were sprayed onto the field while farmworkers worked. In "Fed Up: The High Cost of Cheap Food," farmworkers recalled the physical toll these conditions took on their bodies, blaming pesticides for their diseases, such as chronic kidney disease (CKD). While established that pesticides, specifically PQ, may be involved in some forms of Parkinson's disease, no explicit connection has been identified for SLE, CKD, and other diseases experienced by farm workers. This study evaluated whether pesticides could contribute to kidney disease. We quantified the fluorescence of reactive oxygen species (ROS) following varying PQ exposure in human embryonic kidney 293 (HEK293) cells using a microplate reader. Dosages of 75 and 150 µM were chosen based on previous literature. We also measured expression of KD biomarkers KIM-1 and NGAL upon PQ exposure with RT-qPCR. Glutathione-S-transferase pi 1 (GSTP1) served as an indicator of ROS. We predicted that ROS would increase with increasing PQ concentration, as would the fold change in the expression of the mRNA biomarker levels. The results showed a trend of increased expression of NGAL, KIM-1 and GSTP1 as PQ concentration increased. This study suggests metabolic panels may be an option when assessing patient health, especially patients susceptible to kidney disease. Future in vitro and in vivo examinations of these biomarkers are needed to clinically correlate physiological concentrations of these pesticides, and progression of kidney disease.

To map the subsurface Ground Penetrating Radar (GPR) can be used in a non-invasive way. It is currently done manually by pushing a wheeled device on a handlebar. This thesis suggests an alternative method using an integrated autonomous solution. To ac- complice that: several sensors were fused to give the robot perception of the world, the ability to localize itself within it, and plan a path to reach the goal. Detecting algorithms were implemented and tested to ensure the robot could handle a dynamic and compli- cated world. The results showed that the robot could independently navigate in a grid pattern conducting GPR surveys while avoiding obstacles and finding a safe route. All this will allow for collecting GPR data with precise localization measurements and in paths more detailed than a human operator could. In addition, it enables the operator to be at a safe distance in dangerous environments and to search large areas.

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Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. This manuscript focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to queue spillbacks, which disrupt traffic flows across the entire network and increase congestion. While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles, cannot adequately adjust to dynamic traffic conditions. This manuscript presents a technology-agnostic adaptive real-time server based approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. We also provide timeliness guarantee bounds by analyzing the travel time delays, hence making our approach more dependable and predictable. The proposed approach was validated in simulations and on a realistic hardware testbed with robots mimicking human driving behaviors. Compared to the pre-timed traffic control and an adaptive scheduling based traffic control, our algorithm is able to avoid spillbacks under highly dynamic traffic conditions and improve the average crossing delay in most cases by 10--50 %. / Master of Science / Safe and efficient traffic control remains a challenging task with the continued increase in the number of vehicles, especially in urban areas. This manuscript focuses on traffic control at intersections, since urban roads with closely spaced intersections are often prone to congestion that blocks other intersection upstream, which disrupt traffic flows across the entire network. While various intelligent traffic control solutions exist for autonomous systems, they are not applicable to or ineffective against human-operated vehicles or mixed traffic. On the other hand, existing approaches to manage intersections with human-operated vehicles, cannot adequately adjust to dynamic traffic conditions. This work presents a technologyagnostic adaptive approach to dynamically determine signal timings at an intersection based on changing traffic conditions and queue lengths (i.e., wait times) to minimize, if not eliminate, spillbacks without unnecessarily increasing delays associated with intersection crossings. We also provide theoretical bounds to guarantee the performance of our approach in terms of the travel delays that may incur on the vehicles in the system, hence making our approach more dependable and predictable. The proposed approach was validated in simulations and on a realistic hardware testbed which uses robots to mimic human driving behaviour in an urban environment. Comparisons with widely deployed and state-of-the-art traffic control techniques show that our approach is able to minimize spillbacks as well as improve on the average crossing delay in most cases.