This project is carried out in the field of motion planning of AGVs for the company AGVE with the aim to automate the obstacle free trajectory generation process. The process of manually generating splines in AutoCAD to achieve obstacle avoidance is replaced by the automatic generation of paths by running a python script. Artificial potential field algorithm is implemented in the python script to achieve obstacle avoidance. Clothoid curve is used to create feasible trajectories for obtaining obstacle free paths. The developed program is tested and proved suitable on three scenarios including a real-life problem encountered by the company. The output obstacle free path can be manipulated using three factors which are alpha, moving the neighbours and manipulating the scaling factor for potential fields in the APF algorithm.

Yes

The development of a "Next Generation Manufacturing System" is currently an active area of research worldwide. The research described in this dissertation addresses one sub-element within this research area; namely, the demonstration of a decentralized, automated production architecture. The goal of the work is to increase the ability of a manufacturing enterprise to respond to rapid technological and market change in the post-2000 global economy. The research is comprised of three objectives; definition of a decentralized organizational structure of autonomous production activities, implementation of the defined organization in a real world manufacturing environment, and a comparison of historical (centralized architecture) performance data and decentralized performance data. To accomplish these objectives, the proposed production architecture is implemented at a real world manufacturing site and performance data are acquired and tested against a stated hypothesis. The research entails the modification of a selected electronics module assembly activity in the following ways: 1) comprehensive automation of assembly processes; 2) simplification of production practice through a minimization of operator interaction and a reduction of assembly transaction points requiring operator intervention; and 3) restructuring of organizational functions resulting in decentralization and operational autonomy. The null hypothesis was successfully rejected and it was shown that the implementation of automation, simplification, and decentralization resulted in an enhancement of production performance (i.e., a reduction in throughput time, labor cost, overhead cost, and total product cost) without degrading production quality. A test of the null hypothesis based on the data indicates a statistically significant (i.e., p less than or equal to 0.05) reduction in throughput time, labor cost, overhead cost, and total product cost while no statistically significant difference in the before and after production quality data was shown. A possible interpretation of these results is that the implementation of automation, simplification, and decentralization did result in a reduction in the labor cost, overhead cost, and total product cost and did not result in a degradation in production quality. / Ph. D.

Type 1 Diabetes (T1D) is an irreversible chronic autoimmune disease that affects millions in the United States. Individuals with T1D rely on biomedical technologies to manage their disability and to stay alive. The increased use of and reliance on automated technologies creates complex entanglements between human bodies, technologies and external factors including digital infrastructures creating what I term as "biotechnological organism." This U.S.-based study focuses on the most advanced biomedical technology used to manage T1D today, the Artificial Pancreas System (APS), to demonstrate how seemingly liberating automated biomedical technologies can entangle, subjugate, and confine those they aim to free. This study features the analysis of two distinct social groups by focusing on their risk discourses and risk reduction efforts. The first group is a community of regulatory experts represented by the American Diabetes Association (ADA). It provides an important perspective grounded in evidence-based science, established norms, and professional standards of medicine, healthcare, and research. The second group is the Do-It-Yourself (DIY) biological community represented by DIY innovators, patients, caregivers, and advocates. It provides a different but equally important perspective shaped by affective dimensions that reflect a phenomenological experience with biomedical technologies. The combination of these two perspectives along with the improved understanding of this disability, the complexity of entanglements between humans and machines, differing approaches to health automation and knowledge production practices elucidates important social, economic, and political issues. The significance of this work lies in its examination of how the improved understanding of health automation efforts can help inform policy and healthcare decisions. / Doctor of Philosophy / Type 1 Diabetes (T1D) is a chronic condition when the pancreas does not make enough insulin necessary for the body to allow blood glucose (blood sugar) to enter cells and produce energy. This disability affects millions in the United States. Individuals with T1D rely on biomedical technologies to manage their blood glucose levels and need to inject insulin to stay alive. The increased use of and reliance on automated technologies is encouraged to reduce the risks of health complications and reduce the demands of T1D management. But automated biomedical technologies also pose additional burdens related to technological use, maintenance, data overload, decision-making, and risk. This U.S.-based study focuses on the most advanced biomedical technology used to manage T1D today, the Artificial Pancreas System (APS). I coin the term "biotechnological organism" to describe the complex relationship between humans and biomedical technologies. The study demonstrates how seemingly liberating automated biomedical technologies can burden those they aim to free from the demands of disease. This study features the analysis of two distinct groups by focusing on their risk perceptions and risk reduction efforts. The first group is regulatory experts represented by the American Diabetes Association (ADA). This group provides an important perspective grounded in evidence-based science, established norms, and professional standards of medicine, healthcare, and research. The second group is the Do-It-Yourself (DIY) biological community, which includes DIY innovators, patients, caregivers, and advocates. This group provides a different but equally important perspective shaped by the diverse lived experiences of people using biomedical technologies. The improved understanding of differing approaches to health automation and knowledge production practices within these two groups elucidates important social, economic, and political issues. This work aims to understand how health automation efforts can help inform policy and healthcare decisions.

A methodology is developed to determine cost"effective hierarchical computer control network designs for flexible manufacturing systems. By modeling the hierarchical control system (HCS) as a resource allocation problem, an optimal hardware configuration is identified using dynamic programing. Being independent of specific computer hardware technology, the model can address present and future automated manufacturing systems. A simulation model is developed to evaluate operational dynamics of the specified system configuration, analyze HCS component performance characteristics, and evaluate hardware and software in real simulation time. The model also simulates continuous system dynamics, as found in optimal adaptive control systems. / Ph. D.

This thesis introduces a novel framework for enabling intelligent machines and robots with the fifth-generation (5G) cellular network technology. Autonomous robots, such as Unmanned Aerial Vehicles (UAVs), Autonomous Guided Vehicles (AGVs), and more, can notably benefit from multi-agent collaboration, human supervision, or operation guidance, as well as from external computational units such as cloud edge servers, in all of which a framework to utilize reliable communication infrastructure is needed. Autonomous robots are often employed to alleviate humans by operating demanding missions such as inspection and data collection in harsh environments or time-critical operations in industrial environments - to name a few. For delivering data to other robots to maximize the effectiveness of the considered mission, for executing complex algorithms by offloading them into the edge cloud, or for including a human operator/supervisor into the loop, the 5G network and its advanced Quality of Service (QoS) features can be employed to facilitate the establishment of such a framework. This work focuses on establishing a baseline for integrating various time-critical robotics platforms and applications with a 5G network. These applications include offloading computationally intensive Model Predictive Control (MPC) algorithms for trajectory tracking of UAVs into the edge cloud, adapting data sharing in multi-robot systems based on network conditions, and enhancing network-aware surrounding autonomy components. We have identified a set of key performance indicators (KPIs) crucially affecting the performance of network-dependent robots and applications. We have proposed novel solutions and mechanisms to meet these requirements, which aim to combine traditional robotics techniques to enhance mission reliability with the exploitation of 5G features such as the QoS framework. Ultimately, our goal was to develop solutions that adhere to the essential paradigm of co-designing robotics with networks. We thoroughly evaluated all presented research using real-life platforms and 5G networks.

Precision agriculture allows farmers to efficiently use their resources with site-specific applications. The current work looks to computer vision for the data collection method necessary for such a smart field, including cameras sensitive to visual (430-650~nm), near infrared (NIR,750-900~nm), shortwave infrared (SWIR,950-1700~nm), and longwave infrared (LWIR,7500-16000~nm) light. Three areas are considered in the study: image segmentation, multispectral image registration, and the feature tracking of a stressed plant. The accuracy of several image segmentation methods are compared. Basic thresholding on pixel intensities and vegetation indices result in accuracies below 75% . Neural networks (NNs) and support vector machines (SVMs) label correctly at 89% and 79%, respectively, when given only visual information, and final accuracies of 97% when the near infrared is added. The point matching methods of Scale Invariant Feature Transform (SIFT) and Edge Orient Histogram (EOH) are compared for accuracy. EOH improves the matching accuracy, but ultimately not enough for the current work. In order to track the image features of a stressed plant, a set of basil and catmint seedlings are grown and placed under drought and hypoxia conditions. Trends are shown in the average pixel values over the lives of the plants and with the vegetation indices, especially that of Marchant and NIR. Lastly, trends are seen in the image textures of the plants through use of textons. / Master of Science

As the IEC 61850 digital substation standard becomes progressively adopted by utilities throughout the world, entirely computerized methods will completely replace traditional strategies for monitoring the power system. Although newer techniques offer enhanced efficiency and controllability, their reliability is not as established as that of conventional practices. Modern approaches require extensive validation and analysis before they can be implemented on a widespread basis. One specific area of interest is the performance of protection systems that utilize voltage and current samples digitized directly at their source. This research presents a complete test bench for evaluating sampled values-based protection schemes and measures their efficacy under several different operating conditions. It is shown that the novel system operates correctly for the situations it is expected to, with minimal latency compared to traditional practices. / Master of Science / Power system infrastrcutures are changing rapidly from analog arrangements to entirely digital methods. This offers numeous benefits such as increased efficiency, lower cost, higher accuracy, and even improved safety. However, digital implementations do not have an as proven track record as compared to conventional practices, leading to concerns about their reliaiblity. This research tests the performance an en entirely digital protection scheme by using various hardware components. The results are analyzed and show that the novel scheme operates correctly, albeit with a slight delay as compared to traditional methods.

Thesis advisor: Anant Nyshadham / Thesis advisor: Kit Baum / The dissertation consists of three independent explorations of labor market dynamics in developing countries. I first investigate how minimum wages affect employment and investment decision of firms in India and how they can lead to accelerated automation and offshoring. Then, I investigate how managers of garment production lines in India's largest ready-made garment producer establish informal agreements to deal with worker absenteeism shocks. Finally, I study how Indonesian households learn about their productivity in different sectors of the economy and show that they often spend years, if not decades, in sectors where they are less productive which depresses their earning potentials, but they converge to their most productive sector over time. In the first chapter, "Effect of Minimum Wages on Automation and Offshoring Decisions of Firms: Evidence from India", I study the effect of India's local minimum wages on the production structure of firms in the formal economy. I compile data on the country's numerous minimum wages which vary at the state, year, and industry level, and show that changes to these wages have important effects on firm-level capital investment and employment of different types of employees. The effects depend on the firms' ability to automate and offshore certain tasks. Using a difference-in-difference approach, I show that firms in the average industry, that is, firms in industries neither intensive in routine nor offshorable tasks, continue to invest in machinery and computers at a rate of 8% per year following a minimum wage hike. However, they substitute payroll workers with managers and contract workers less likely to be bound by the minimum wage. Firms in industries intensive in routine tasks that are easier to automate invest 6.1% more in machinery and 4% more in computers, at the expense of payroll workers. Firms in industries intensive in tasks easier to do remotely continue to invest in machinery and computers, but the rate of investment in computers falls by 6.2% following a minimum wage hike, and payroll worker employment falls as well. This suggests that some tasks that combine workers and computers, like data analysis, may be offshored. These results support the predictions of a task-based production model, and indicate that minimum wages have a strong effect on the structure of production at the firm level, leading some towards increased rates of automation and offshoring. In the second chapter, "Absenteeism, Productivity, and Relational Contracts Inside the Firm", joint with other researchers, we study relational contracts among managers using unique data that tracks transfers of workers across teams in Indian ready-made garment factories. We focus on how relational contracts help managers cope with worker absenteeism shocks, which are frequent, often large, weakly correlated across teams, and which substantially reduce team productivity. Together these facts imply gains from sharing workers. We show that managers respond to shocks by lending and borrowing workers in a manner consistent with relational contracting, but many potentially beneficial transfers are unrealized. This is because managers' primary relationships are with a very small subset of potential partners. A borrowing event study around main trading partners' separations from the firm reinforces the importance of relationships. We show robustness to excluding worker moves least likely to reflect relational borrowing responses to idiosyncratic absenteeism shocks. Counterfactual simulations reveal large gains to reducing costs associated with forming and maintaining additional relationships among managers. In the last chapter, "Learning, Selection, and the Misallocation of Households Across Sectors", joint with other researchers, we study the role of labor misallocation (i.e., suboptimal sorting of households across sectors) in explaining low productivity in developing countries. We estimate a generalized earnings equation with dynamic correlated random coefficients, allowing households to learn about their relative productivity across the agricultural and non-agricultural sectors. Estimates show that households sort across sectors on comparative advantage, but learn and converge slowly over time, with many households spending substantial time in a suboptimal sector. Roughly 33% of households are misallocated to start, earning 64% less on average than they could have if they were properly sorted across sectors. Our approach nests several alternative models which can be ruled out, including those without dynamics and/or heterogeneity in relative productivity across sectors. We also evaluate alternative interpretations for the dynamic sorting we observe in the data such as saving out of financial constraints and skill accumulation or learning by doing. / Thesis (PhD) — Boston College, 2022. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Economics.

This thesis develops a framework to guide the incremental design and implementation of a Computer-Integrated Manufacturing System (CIMS). The framework is premised upon the facts that: (1) CIMS design is accomplished through a series of evaluation decisions sequenced through time and (2) evaluation is accomplished by decomposing the entire manufacturing organization into its essential activities and transactions. The effects of computerization on these activities and transactions are determined and these effects are then related to impacts on a set of selected evaluation criteria. Formal methods for benefit quantification are not included. The user of this framework is required to: (1) specify a set of relevant evaluation criteria, (2) define essential activities and transactions of their organization, and (3) derive organization-specific affect/impact relationships. The framework structures these activities for the user and suggests a series of matrices that can be used to guide the user through the steps of the framework. Use of the framework is demonstrated as various aspects of an implementation decision currently being faced by a manufacturing organization are analyzed. The implementation decision concerns whether to implement a computerized production planning and scheduling system and aspects of the decision which are considered include impacts on organizational flexibility, responsiveness, and skills. The required changes in authority relationships and the assignment of task responsibilities are also considered. Using results from this case study, the usefulness and appropriateness of the framework was assessed. Although there was no quantitative measure available, the client deemed the framework useful for analyzing and guiding their implementation decision. Suggested improvements to the framework are presented. / Master of Science