Statement of Research Interests Javad Omrani

Statement of Research Interests Javad Omrani My interest lies in solving the fundamental challenges associated with autonomous systems and data manipulation for those systems. A dissonance between artificial intelligence and control theory is preventing the development of a holistic whole in robotics. Artificial Intelligence requires high computation costs to ensure robustness, whereas control theory lacks the cognitive reasoning required for the solution of perception and path planning problems. I wish to explore the question of whether we can develop a theory that promotes the convergence of AI and control theory, combining both its advantages while excluding both its disadvantages. In conjunction with machine learning algorithms, robots are capable of recognizing their environment and patterns and interaction reliably with them. The use of intelligent methods can allow us to learn from past control processes, adapt to dynamic environments, and estimate unknown robotic models. In addition, index quantification and feature learning can help to make the evaluation process more objective. Furthermore, developing the science of autonomous decision-making that goes beyond teleoperation and makes full use of robotic technology will be a key component of the future. My research aims to advance the science of autonomous robotic systems in two ways- by utilizing novel control schemes practically to create autonomous robotic systems that eliminate or minimize the adverse effect of uncertainties, and make the smart robotic systems accomplish their tasks with at least interference of human operators to using innovative procedures. It was an honor to be a part of Prof. Majid Moghaddam's robotic team at Tarbiat Modares University, which provided me with an opportunity to simulate and implement various control schemes and be an expert in hardware and software employed by robotic systems. For my Master's thesis project I used LabView to design, implement, and test the feasibility of various controllers for a rehabilitation robotic system. Interacting with patients in a manner that is safe and assessing their disability is the most important aspect of rehabilitation robotic systems. Adapting actuator's forces and torques and planning trajectory based on different patients' needs, with widely differing abilities, was my biggest challenge. Thus, I presented a time delay model reference adaptive controller and an innovative adaptive trajectory that was adjusted during therapy in accordance with the patient's progress. In addition, academic courses and experimental tests have given me insight into such instruments as sensors, drivers, and communication protocols between robots and computers, which are vital to working in the real world. And what’s more, to investigate my proposal, I used Python and Matlab to simulate the robotic system. Moreover, My peer-reviewed paper entitled "Nonlinear Time Delay Estimation Based Model Reference Adaptive Impedance Control for an Upper-Limb Human-Robot Interaction" was published (November 1, 2021) in Sage journal. My experience with communication protocols, sensors, and control procedures is not limited to my academic background. Upon graduating, I joined a knowledge-based company producing and designing laboratory equipment, where I gained expertise in the design of control processes and programming. Throughout these experiences, many exciting opportunities arose, such as collaborating with engineers from different majors and fields of study, and taught me the dedication to research due to the deadlines and budget restrictions. However, the most interesting part was learning how to adapt image processing and video processing systems to the equipment. I used grayscale image processing tools (Convolutional Neural Networks (CNN)) to investigate the physical characteristics of liquids, specifically crude oil. I have been involved in academic and research endeavors over the years; for example, to continue developing my programming ability, I enrolled in several Python programs including Machine Learning courses, specifically Reinforcement Learning. My next step is to pursue a doctorate in an area related to autonomous robotic systems. This will provide me with insight into the vast possibilities offered by robotics and programming. Additionally, I believe that the combination of new materials with machine learning algorithms, image processing tools, and. . . will boost the usefulness of robots, enabling better environment-robot interaction, and data acquisition. Access to your interesting and practical projects will give me an edge in the world of tomorrow's technology. My motivation to pursue education is not just an academic challenge, but also an application of knowledge, and contribution to humankind. What intrigues me about the research at the Mechanical and Manufacturing Engineering Department of the University of Calgary has to do with their experimental tests, which will lead to the use of robotics in everyday life. I would appreciate it if I had the opportunity to pursue my graduate studies at your institution and I am able to justify your faith in me.