The present work examines whether user's trust of and reliance on automation, were affected by the manipulations of user's perception of the responding agent. These manipulations included agent reliability, agent type, and failure salience. Previous work has shown that automation is not uniformly beneficial; problems can occur because operators fail to rely upon automation appropriately, by either misuse (overreliance) or disuse (underreliance). This is because operators often face difficulties in understanding how to combine their judgment with that of an automated aid. This difficulty is especially prevalent in complex tasks in which users rely heavily on automation to reduce their workload and improve task performance. However, when users rely on automation heavily they often fail to monitor the system effectively (i.e., they lose situation awareness - a form of misuse). However, if an operator realizes a system is imperfect and fails, they may subsequently lose trust in the system leading to underreliance. In the present studies, it was hypothesized that in a dual-aid environment poor reliability in one aid would impact trust and reliance levels in a companion better aid, but that this relationship is dependent upon the perceived aid type and the noticeability of the errors made. Simulations of a computer-based search-and-rescue scenario, employing uninhabited/unmanned ground vehicles (UGVs) searching a commercial office building for critical signals, were used to investigate these hypotheses. Results demonstrated that participants were able to adjust their reliance and trust on automated teammates depending on the teammate's actual reliability levels. However, as hypothesized there was a biasing effect among mixed-reliability aids for trust and reliance. That is, when operators worked with two agents of mixed-reliability, their perception of how reliable and to what degree they relied on the aid was effected by the reliability of a current aid. Additionally, the magnitude and direction of how trust and reliance were biased was contingent upon agent type (i.e., 'what' the agents were: two humans, two similar robotic agents, or two dissimilar robot agents). Finally, the type of agent an operator believed they were operating with significantly impacted their temporal reliance (i.e., reliance following an automation failure). Such that, operators were less likely to agree with a recommendation from a human teammate, after that teammate had made an obvious error, than with a robotic agent that had made the same obvious error. These results demonstrate that people are able to distinguish when an agent is performing well but that there are genuine differences in how operators respond to agents of mixed or same abilities and to errors by fellow human observers or robotic teammates. The overall goal of this research was to develop a better understanding how the aforementioned factors affect users' trust in automation so that system interfaces can be designed to facilitate users' calibration of their trust in automated aids, thus leading to improved coordination of human-automation performance. These findings have significant implications to many real-world systems in which human operators monitor the recommendations of multiple other human and/or machine systems.

Technological advancements have resulted in highly automated systems that are featured in many kinds of tools and devices, such as self-driving cars, autopilot in airplanes, and much more. Such systems have enabled tools to plan, decide, and act autonomously. This breakthrough resulted in a new manner of interacting with tools, known as "Human-Robot Joint Action" or "human-AI interaction," in which people and automated tools share control over the tasks that must be performed. However, little is known about the impact of such interactions on people and their sense of agency (SoA) as well as how much autonomy to grant to tools. As a result, the objective of this systematic review is to investigate and understand how automated tools affect human SoA, and if tools with different levels of automation affect our SoA differently. A search in two databases, Scopus, and MEDLINE EBSCO was conducted, and 8 articles were included. The findings suggest that the more automated the tool is, the less SoA participants experience, and that varied levels of automation may impact human SoA depending on the nature of the task. However, this topic is still in its infancy and more research is needed.

The main theme of this research will be to develop solutions to the widely known 3-part question in mobile robotics comprising of "Where am I" "Where should I be" and "How do I get there". This can be achieved by implementing automation algorithms. Automation algorithms or control algorithms are vital components of any autonomous vehicle. Design and development of both prototype and full-scale control algorithms for a Long-Airdox Full Dimension Continuous Haulage system will be the main focus. Automation is a highly complex task, which aims at achieving increased levels of equipment efficiency by eliminating errors that arise due to human interference. Achieving a fully autonomous operation of a machine involves a variety of high-level interlaced functions that work in harmony, and at the same time perform functions that mimic the human operator. Automation has expanded widely in the field of mobile robotics, thus leading to the development of autonomous robots, automated guided vehicles and other autonomous vehicles. An indispensable element of an autonomous vehicle is a navigation system that steers it to a required destination. The vehicle must be able to determine its relationship to the environment by sensing, and also must be able to decide what actions are required to achieve its goal(s) in the working environment. The goal of this research is to demonstrate a fully autonomous operation of the Continuous Haulage System, and to establish its potential advantages. / Master of Science

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Thesis advisor: Mehmet Ekmekci / The increased adoption of automation into various occupations strikes fear into many hearts, as people worry that robots will be able to perform their jobs more efficiently, leading to their inevitable unemployment. However, economists predict that this new technology will actually lead to a higher level of employment, although it will cause the aggregate labor share to decrease, forcing wages to decline. This paper focuses on ways in which we can recover a portion of the labor share and mitigate the decline of wages through the development of human capital via the educational system. Moreover, it seeks to understand ways in which we can exploit the jobs that will be created by automation by proposing methods to alter the educational structure to emphasize the new skills that are becoming more pertinent. My analysis provides evidence that by developing methods to encourage higher levels of educational attainment through dual degree and accelerated master’s programs, as well by decreasing the cost-exclusivity of higher education, we can both prepare future generations and reskill the current generation to succeed in this economic environment. / Thesis (BA) — Boston College, 2019. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Arts and Sciences Honors Program. / Discipline: Economics.

This thesis research aims to develop a virtual model utilizing a simulation robot cell comprising one or multiple robots and establishing seamless communication with CMAS (Configurable Multi-Agent System) for control purposes. The successful implementation of this setup yielded significant benefits, particularly in pre-risk assessment for the robot cell in Plug and Produce (P&amp;P) operations. By leveraging the virtual simulation prior to actual deployment, the identification and prevention of collision scenarios within the robot's paths were effectively achieved. The simulation was conducted using ABB Robot Studio software, which was seamlessly connected to CMAS software through the REST API protocol. This integration facilitated efficient data exchange and real-time control, enhancing the overall performance and safety of the robot cell. / <p>21 hp</p>

Industry 4.0 is associated with the technological complexity of designing frameworks for factory automation. In that case, since the robots are used in factory operations, this paper proposes a framework that can be used for (near) real-time applications (RTA) in the robotic system. Real-time application is a time constraint that works within a time frame, making it essential to set up a high-speed system in data computation and processing. Monitoring sensors are exposed to different physical variables, such as noise and vibration temperature, from the system, which leads to inefficient data and delay in the data computations. Adaptive Sampling Algorithm A.S.A. was used to reduce the amount of data to be computed. A self-adaptive software (i.e., Rainbow framework) was used to implement the algorithm—Hardware-in-the-loop (HiL) simulation technique used in the simulation of A.S.A. The hardware used in this scenario is the Speedgoat real-time target machine. The proposed framework was tested in an implementation scenario where the robot system had high latency, above 10ms, and had to be reduced to 5ms and below. The results showed fewer samples were collected from the test signal after implementing the algorithm. Hence reducing high latency and increasing real-time application in robot systems. In summary, the proposed framework could be used to develop real-time applications in robotic systems for Industry 4.0.

To increase production and reduce intermediate storage in the factory a new flexible packaging machine is needed. The main purpose of this project is to design a new flexible and modular packaging machine and to create a new framework to use when designing new machines. Current research shows that no framework or practical guide for designing flexible and modular machines exist. A new framework is developed based on a literature study on flexibility in the industry with past, present, and future concepts. The proposed framework is a step-by-step tree/flowchart where each step has a set of rules/guidelines to follow. Along with the flowchart a help section is written to guide the user through the design steps. Existing industry standards are investigated to find a suitable structure for connecting the modules and the machine in the project. Results show that few standards exist in the connectivity structure for the industry.  To lower maintenance costs and increase operational reliability, predictive maintenance using edge computing is investigated. Using edge computing allows the machine to take advantage of predictive maintenance while being offline which makes it suitable for a moveable machine. Evaluation of predictive maintenance show that it still needs more research, and it is complex to implement, which makes it expensive for non-critical machines.The results of this project are a new proposed framework that was used in the project to design a new packaging machine and a flexible PLC program that is easily adaptable for changing modules. The proposed framework fills a gap in the practical usage of known concepts and was used while designing the new machine to evaluate its usability.

The ever-increasing size and complexity of communication networks today complicate Network Operation Centers (NOC) to function efficiently in manually operated tasks such as network status detection, network fault localization, cost-aware traffic engineering, failure management, and network quality assurance. These tasks have traditionally been managed by expert technicians who make decisions on when and where to take which actions based on specific network rules. Due to the complexity of the process, NOC actions are still performed manually. However, automating this process could be a valuable input for network providers and service operators. In this context, we developed an Artificial Intelligence based (AI-based) action recommendation engine (ARE) which, as its name suggests, recommends the best available operational expenditure aware (OPEX-aware) action, either with (Stateful ARE) or without (Stateless ARE) measuring the network state. Our experimental results show that Stateful ARE can recommend the suitable action and yield up to 99% accuracy. This high accuracy percentage is due to the correct classification of the Normal state, which represents 64.5% of the dataset, and its corresponding action of Do Nothing, which accounts for 68.3% of all actions While Stateful ARE’s overall accuracy is satisfactory, it was unable to achieve this performance in minority classes, and it suffered from performance degradation due to state classification process. Therefore, we introduced Stateless ARE, which recommends actions without measuring the network state. The initial results of Stateless ARE using a Feed Forward Neural Network (FFNN) did not exceed Stateful ARE’s performance. The classification accuracy of minority classes were still around 89% and 93%, but it outperformed the static network, indicating that it could be improved with further optimization techniques. Based on this insight, we introduced state-of-the-art Transformer model as Stateless ARE model. The transformer model significantly improved the accuracy of the minority classes to 97% and 99%, which other methodologies struggled to classify. This result shows that the transformer model can be an effective tool in improving the performance of action recommendation engines.

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