2024 International Conference on Signal Processing and Intelligent Computing (SPIC 2024)
HOME > Keynote Speaker




Keynote Speaker 1: Prof. Dazhi Wang, Northeastern University, China


Brief Introduction: Dazhi Wang, professor, doctoral supervisor; currently director of the Institute of Intelligent Science and Electrical Engineering Technology, School of Information Science and Engineering, Northeastern University, and director of the Northeastern University-Schneider Electric Joint Laboratory. Currently mainly engaged in: intelligent robots and motion control, electric vehicle powertrain control, intelligent motor and big data platform technology, magnetic transmission technology, special motors, high-power power electronic converter technology, vehicle high-voltage safety technology, intelligent control theory and Research on applications, complex system modeling and control, condition monitoring and fault diagnosis technology, unmanned platform and aircraft control, etc. Undertaken or participated in more than 30 national key R&D plan projects, national key natural fund projects, national natural fund general projects, provincial and ministerial major special projects and horizontal topics; has been published in SCI and EI indexes in important domestic and foreign journals and conferences He has published more than 200 high-level academic papers and obtained more than 40 patents; he has won more than 20 national, provincial, and municipal teaching and scientific research awards; he has won titles such as "Shenyang Leading Talent" and "Shenyang Outstanding Scientific and Technological Workers". He currently serves as the vice chairman of the Liaoning Provincial Electrical Engineering Association, the deputy director of the Electrical Professional Committee of the Liaoning Provincial Education Steering Committee, and the chairman of the Electrical Transmission and Electrical Theory Professional Committee of the Liaoning Provincial Electrical Engineering Society.


Title: Modeling and Optimization Methods Research of Permanent magnet eddy-current Coupler


Abstract: Permanent magnet eddy current coupler is a new type of speed regulation and transmission device that can realize non-contact energy transfer between motor and load. It provides new possibilities for improving equipment efficiency and reducing energy consumption. Taking the disc-type permanent magnet eddy current coupler as the research object, the distribution of the air gap magnetic field intensity between the conductor disc rotor and the permanent magnet disc rotor and the distribution law of the eddy current density in the conductor disc were deeply studied. Accurate two-dimensional and three-dimensional analytical models of the disc-type permanent magnet eddy current coupler were constructed by different methods, and multiple key performance indicators were systematically calculated and analyzed. A multi-objective particle swarm algorithm based on an improved competition mechanism was proposed to optimize its structural parameters and performance indicators. The accuracy of the constructed model and the effectiveness of the optimization algorithm were verified through three-dimensional finite element simulation. These research works help to more accurately predict the performance of permanent magnet eddy current couplers and provide theoretical guidance for their optimal design.





Keynote Speaker 2: Senior Researcher Jiahui Yu, Binjiang Institute Of Zhejiang University, China


Brief Introduction: Selected as a national young talent, provincial leading talent, and Hangzhou Science and Technology Innovation Young Talent. He graduated from the Center for Machine Learning and Robotics at the University of Portsmouth, UK, and was selected for the UK ABCP Doctoral Award Program. He conducts basic theoretical research and application of results transformation for the integration and cross-border of biomedical and engineering artificial intelligence, and has published more than 40 academic articles in international journals/conferences such as the IEEE Transactions series. Related research has been successfully applied to digital pathology microscope systems, machine-assisted diagnosis and treatment systems for adolescent autism/depression/emotional disorders, and intelligent rehabilitation and assistance systems. He has presided over 3 national/provincial and ministerial projects, and participated in the key R&D plans of the Ministry of Science and Technology and the Hangzhou Innovation Team as a backbone. He has more than 10 invention patents/software copyrights and has won the third prize for scientific research innovation in the National Medical and Engineering Cross-Border Postdoctoral Academic Forum. He serves as a guest editor of several international journals such as Biomimetics and an organizing committee member of the Medical Robotics Branch of the ICIRA International Conference.


Title: Medical Embodied Intelligent Human Visual Perception


Abstract: "Unification of knowledge and action" is the scientific position of embodied intelligence. "Knowledge" is the basis of "action", that is, the perception system drives the "embodied" carrier to understand human activities and automatically execute instructions. Human visual perception is the core technology for the construction of medical embodied intelligence. It covers most medical activity scenarios such as abnormal/stereotyped behaviors, pointing behaviors, and social behavior training of patients. It is the most intuitive and cutting-edge method. Focusing on "Research on human visual perception and embodied intelligence construction under the coupling of complex medical factors", the key principles and methods of constructing human kinematic structure representation and modeling the correlation representation of world space elements are introduced.





Keynote Speaker 3: Prof. Caiyun Wu, Shenyang Ligong University, China


Brief Introduction: Professor, master's supervisor, PhD in control theory and control engineering, postdoctoral fellow in computer science and technology, visiting scholar at the University of Virginia, USA, innovative talent in Liaoning Province, outstanding talent in Liaoning Province, instructor of national college student innovation and entrepreneurship projects, outstanding master's degree thesis instructor, won the first prize in the Liaoning Province College Teachers Teaching Innovation Competition, 4 first and second prizes in Liaoning Province Teaching Achievement Awards, and 3 Liaoning Province Natural Science Academic Achievement Awards; as the project leader, he presided over and completed many national and provincial projects such as the National Natural Science Foundation of China, the National Postdoctoral Fund General Project, the Liaoning Provincial Department of Education Scientific Research Project, the Liaoning Provincial Department of Science and Technology Project, the Liaoning Provincial Outstanding Talent Program Project, and the Liaoning Provincial College Innovative Talent Project. As the main implementer, he participated in the National Natural Science Foundation key projects and many provincial projects; he published more than 30 SCI and EI papers as the first author.


Title: Research on active noise control system and algorithm


Abstract: Active noise control refers to the artificial generation of a series of secondary noises under the action of certain electroacoustic devices. The amplitude and frequency of this series of secondary noise sound waves are the same as those of the primary noise sound waves, but the phase is opposite. The primary and secondary noises interfere with each other in the anechoic area to achieve noise reduction. This report mainly studies active noise control systems and algorithms. First, for the case where noise signals are separated from useful signals, a single-channel feedforward active noise control system is designed. For the case where the information of noise signals is unavailable, an active noise control system based on wavelet transform is designed. Then, for the above two systems, several new adaptive filtering algorithms and nonlinear secondary sound channel identification algorithms are proposed, which can effectively improve the signal-to-noise ratio and improve system performance. Finally, an active noise control system simulation platform is designed to better simplify the simulation operation process and facilitate the analysis of the impact of algorithms on system performance.




Keynote Speaker 4: Researcher Haitao Luo, Shenyang Institute of Automation Chinese Academy of Sciences, China


Brief Introduction: Haitao Luo is a researcher at the Shenyang Institute of Automation, Chinese Academy of Sciences, a doctoral supervisor, and a visiting scholar at Clemson University in the United States. He has long been engaged in research in the field of space structure dynamics and structural optimization design. He has presided over key deployment projects of the Chinese Academy of Sciences, National Natural Science Foundation, Liaoning Provincial Fund, etc., and participated in 921 manned spaceflight, Chang'e lunar exploration project, deep space exploration satellite and other model projects as the person in charge of the structural subsystem. He has published more than 80 SCI/EI papers, 4 books, and more than 100 invention patents. He has won the first prize of Liaoning Province Technology Invention Award, the second prize of Science and Technology Award of China Vibration Engineering Society, the second prize of Science and Technology Award of China Simulation Society, and the second prize of Liaoning Province Natural Science Academic Achievement Award. He is currently a member of the Chinese Society of Vibration Engineering, an executive director of the Liaoning Provincial Society of Vibration Engineering, a top-notch young talent in Liaoning Province's "Xingliao Talents", an outstanding young scientific and technological worker in Liaoning Province, and a leading talent in Shenyang City.


Title: Research and development of sampling equipment for extraterrestrial object detection and engineering application


Abstract: In combination with space background projects that have been completed or are being undertaken, we will carry out research and development and design of detection and sampling equipment for the scientific exploration and sampling operations of extraterrestrial bodies such as the moon, Mars and asteroids, solve problems such as structural lightweight, rigid-flexible coupling dynamics, cushioning and vibration isolation, and adaptability to the space environment of the entire system, and carry out ground demonstration and verification to provide technical support and solutions for space engineering applications of extraterrestrial object detection and sampling.





Keynote Speaker 5: A. Prof. Yong Dai, Shenyang Ligong University, China


Brief Introduction: Yong Dai , male, born in 1989, graduated from Dalian Maritime University majoring in control theory and control engineering and received a doctorate in engineering. Postdoctoral fellow at Shenyang Institute of Automation, Chinese Academy of Sciences, working in the National Key Robot Research Laboratory. Currently working at the School of Automation and Electrical Engineering, Shenyang Ligong University, as an associate professor and a master's tutor. He has supervised more than 10 master's degree students and published dozens of academic papers. He has hosted and participated in many vertical scientific research projects at the school, municipal, provincial and national levels, as well as many horizontal enterprise projects. In recent years, Dai Yong's team has been engaged in research on related technical theories and low-cost mass production solutions such as autonomous vehicles, robotic arms, and underwater robots.


Title: Research on low-cost positioning technology for unmanned vehicles based on fusion of vision, inertial navigation and wheel speed information


Abstract: The report will focus on the international mainstream ORB-SLAM3 visual SLAM (simultaneous localization and mapping) framework. In view of the problem that pure visual ORB-SLAM3 will cause image blur and feature point loss in scenes with fast movement or large-scale rotation, this report proposes a combined visual SLAM solution for autonomous vehicles that combines a monocular fisheye camera with a low-cost inertial measurement unit IMU and a wheel speed odometer sensor. First, this report will report the use of inertial measurement unit IMU to assist visual SLAM and form visual inertial SLAM. However, IMU predicted poses will also have cumulative drift problems. Then, this report will further report how to introduce wheel speed odometer sensors to solve the drift problem of IMU. Secondly, this report will report the research team's experimental results in a 2-kilometer campus scene at Shenyang Ligong University. Finally, this report will summarize and look forward to visual SLAM technology in the field of autonomous vehicles.





Keynote Speaker 6: Prof. Yingke Xu, Zhejiang University, China


Brief Introduction: Yingke Xu, PhD, is a professor/doctoral supervisor/dean assistant of the School of Biomedical Engineering and Instrument Science, Zhejiang University, deputy director of the Intelligent Medical Technology and Equipment Research Center, Binjiang Research Institute, Zhejiang University, and deputy director of the Zhejiang Provincial Key Laboratory of Cardiovascular Detection Technology and Efficacy Evaluation. He graduated from Zhejiang University with a doctorate in biomedical engineering in 2008. From 2008 to 2012, he worked in the Yale University School of Medicine, USA, and served as a postdoctoral fellow and research scientist. He has been engaged in applied research in biophotonics, image artificial intelligence and biomedicine for a long time, and has published more than 80 SCI papers in journals such as Nature Methods, Nature Communications, and JCB. He serves as an adjunct professor at Yale University, a young editorial board member of the Chinese Academy of Engineering journal Engineering, etc. He has presided over more than ten projects, including the key R&D plan of the Ministry of Science and Technology, the National Natural Science Foundation, the major instrument project of the National Natural Science Foundation of China, and the Zhejiang Provincial Outstanding Youth Fund and key projects. He was selected for the Zhejiang Provincial Ten Thousand Talents Plan, the Provincial 151 Talent Project, and the Zhejiang Provincial Outstanding Youth Fund.


Title: Deep learning-driven fast super-resolution imaging of living cells


Abstract: The development of super-resolution optical imaging technology enables us to visualize and analyze dynamic activities within cells with unprecedented resolution, which is of great significance for biomedical research. Current super-resolution imaging methods still have certain shortcomings, especially their application in rapid imaging of living cells. For example, they usually require a large amount of raw image data, which leads to problems such as photobleaching and phototoxicity in imaging. In this report, we will introduce the computational imaging methods developed by the research group, and combine them with image artificial intelligence to achieve fast and high-quality super-resolution imaging, and promote the practical application of these technologies in living cells.





Keynote Speaker 7: Researcher Meng Han, Zhejiang University, China


Brief Introduction: Selected into the national talent plan and Zhejiang provincial talent plan, graduated from Georgia Institute of Technology & Georgia State University, received an MBA and a Ph.D. in computer science, presided over the National Key R&D Program (Young Scientist) project, the National Natural Science Foundation of China general project, Zhejiang Province "Pioneer" and "Leader" R&D project and CCF Enterprise Joint Fund project, etc., and is a senior member of IEEE. Focus on research related to artificial intelligence ecological governance, large model risk governance, model evaluation and enhancement. As of 2024, Dr. Han Meng has published more than 60 CCF A-level academic conferences and journal papers, book chapters and other publications including CCS, ISSTA, TDSC, etc., and won 9 best international conference/journal paper awards or best conference paper candidate awards. His academic achievements have been cited more than 3,000 times on Google Scholar.


Title: Research and exploration on artificial intelligence risk governance in the era of big models


Abstract: As AI systems gradually replace humans in making autonomous decisions in various fields, the importance of data and algorithms becomes increasingly prominent. Lack of security guarantees for analysis and judgment of signal processing and intelligent computing may lead to serious decision-making errors, and may even cause personal injury and social property losses. Therefore, AI security has become a focus of national infrastructure construction and has been included in the National Security Strategy Outline. In addition to classic decision-making AI, such as ChatGPT and MidJourney, the development of generative AI represented by large language models has also received widespread attention recently. These new technical forms and application scenarios bring huge business potential and opportunities, but also bring a series of new challenges, such as privacy leakage, recognition bias, prejudice and discrimination, and compliance and copyright issues.
This report will share how to make full use of the development of new technologies to ensure the security and trustworthiness of the AI ​​industry. We will share the research progress in the field of security for deep learning models and the rapidly developing large models, as well as the new opportunities it brings to scientific research and industry.





Keynote Speaker 8: A. Prof. Lu Liu, Northwestern Polytechnical University, China


Brief Introduction: Liu Lu, a national young talent (Young Yangtze River Scholar of the Ministry of Education), doctoral supervisor, School of Navigation, Northwestern Polytechnical University, is mainly engaged in the research and talent training of unmanned submersible cluster coordination, intelligent control theory and technology. He is a key member of the "Autonomous Underwater Vehicle" National Defense Science and Technology Innovation Team, a member of the Office of a Field of the Military Commission's Science and Technology Commission, the deputy chief designer of the command and control system of a major project of the Military Science and Technology Commission, the person in charge of a sub-project of the National Key R&D Program, a member of the Women Scientists Committee/Young Scientists Committee of the China Society of Shipbuilding Engineering, and was selected as a national young talent in special fields. He won the Young Scientist Nomination Award for a Powerful Ocean Nation and the First Prize for Technological Invention of the China Society of Shipbuilding Engineering. He presided over more than 10 national and provincial and ministerial scientific research projects and accepted/authorized 16 invention patents. He has published more than 50 high-level papers in this field, including more than 30 SCI-indexed papers as the first and corresponding author, and 3 selected as ESI Global Hot Papers/Highly Cited Papers, which have been highly recognized and positively evaluated by experts in related fields at home and abroad.


Title: Heuristic autonomous unmanned underwater vehicle swarm agile collaboration and intelligent control technology


Abstract: Intelligent unmanned submersible swarms are the key to deep-sea exploration, with advantages such as a wide operating range, high efficiency, and strong robustness. Research on swarm collaboration and intelligent control technology and controller design will enable submersibles to have agile joining and rapid task response capabilities, which is of great significance in deep-sea operations and other aspects. This report intends to focus on three typical mission scenarios: ocean exploration, observation, and operation, and introduce the research team's research progress in swarm agile collaboration and intelligent control technology, as well as related swarm equipment. It mainly includes the design of agile collaborative control architecture for mission-inspired submersible swarms, networking and optimization, agile collaborative control methods, and collaborative controller design with loose coupling of software and hardware. The research results can be widely used in military and civil fields, realizing the development of autonomous and controllable key systems in the field of swarm agile collaborative control and verification of typical application scenarios, and supporting the needs of deep-sea intelligent collaborative operations.





Keynote Speaker 9:  Prof. Zhaojie Ju, University of Portsmouth, UK


Brief Introduction: Zhaojie Ju is a professor at the University of Portsmouth, UK, chief scientist of the EU key project, director of the school's medical and wearable robotics research, and chairman of the IEEE SMC Portsmouth Chapter. He has published more than 270 articles in famous journals and international conferences, including more than 120 SCI papers. He has won 6 best paper awards and 1 ICRA best AE award. His research interests include machine intelligence, pattern recognition, and its applications in human-computer interaction and collaboration, robot skill learning, medical and wearable robots.


Title: Multimodal human-computer interaction in robot-assisted therapy systems


Abstract: In the assessment of communication disorders in children with autism, autonomous interaction capabilities are very important for robot-assisted therapy systems. The report will introduce a contactless multimodal sensing system that can automatically extract and fuse perceptual features such as body movement characteristics, facial expressions, sight lines, and environmental information, and map human behavior to the behavioral categories specified by the therapist to further evaluate the child's behavior. The experimental results show that the developed system can effectively extract characteristic data of children with autism, enhance the autonomy of the robot under the supervision of the therapist, and improve the effectiveness of behavioral assessment of children with autism.





Keynote Speaker 10:  Prof. Yalin Zheng, University of Liverpool, UK


Brief Introduction: Prof. Yalin Zheng is a professor of artificial intelligence in healthcare at the University of Liverpool, UK. He is an internationally renowned expert in machine learning, computer vision and medical image analysis. His research in ophthalmic imaging technology is at the forefront of the world, and his innovative work includes line-scan optical coherence tomography imaging technology, automatic analysis of blood vessels, and automatic screening and prediction systems for diabetic and malarial retinopathy. He has published more than 200 articles and multiple patents in high-level international journals and conferences, with a Google citation index of 42. He serves on the editorial board of 6 journals and is a reviewer of more than 10 national funding. In the past five years, he has obtained and presided over a total of more than 50 million yuan (RMB) in UK national funding and participated in nearly 150 million yuan (RMB) in EU projects. He is one of the founders of a newly established smart vision startup at the University of Liverpool and an honorary researcher at the Liverpool University Hospital Eye Center and Liverpool Heart and Chest Hospital.


Title: Progress and challenges of artificial intelligence in healthcare


Abstract: Healthcare is of paramount importance in maintaining overall well-being. Leveraging advancements in AI, we have pioneered innovative algorithms aimed at revolutionising the management of many diseases. Our AI-driven approach enables early detection, precise diagnosis, and personalised treatment plans, thereby significantly enhancing patient outcomes and quality of life. In this talk, I will begin by offering a concise overview of the clinical landscape, shedding light on major conditions and diseases. Following this, I will delve into our groundbreaking research, showcasing our latest work in the realms of diagnosis and prognosis of different health issues. Additionally, I will briefly discuss our ongoing digital twin work. Finally, I will outline current challenges and future aspirations, highlighting the evolving landscape of AI in healthcare.





Keynote Speaker 11: Prof. Giuseppe Carbone, University of Calabria, ITALY


Brief Introduction: Giuseppe Carbone is Professor at DIMEG, University of Calabria, since 2018. Since 2020 he is Chair of the IFToMM TC on Robotics and Mechatronics. He has got the Master and Ph.D. degree at University of Cassino (Italy) where he has been a Key Member of LARM (Laboratory of Robotics and Mechatronics) for about 20 years. He has carried out several periods of study and research abroad in Germany, Japan, China, South Korea, Brazil, Spain, UK, France, also delivering regular teaching courses in Spain and UK. The duties of his academic position include supervising or co- supervising students at bachelor, master, and PhD level as well as the supervision of several visiting scholars/researchers/professors from foreign countries. His research interests cover aspects of Robotics, Mechatronics, Engineering Design, Mechanics of Manipulation and Grasp with over 500 research paper outputs, 20 patents, and 16 Phd completions. 

He edited/co-edited four books that have been published by Springer and Elsevier International Publishers. He has been member of about 20 PhD evaluation Commissions and Viva in Italy, Spain, Finland, UK, Romania, Mexico. He has delivered Invited Keynote or Plenary speeches or Seminars at more than 30 International events. He has been principal Investigator (PI) or co-PI of more than 20 projects including 7th European Framework, H2020 funds, Horizon Europe collecting more than 7 million Euros in competitive funds. He serves in the editorial board of several international journals, including being Editor-in-Chief of ROBOTICA (Cambridge Univ. Press), Section Editor in Chief of Journal of Bionic Engineering (Springer), Machines (MDPI), Robotics (MDPI), Technical Editor of IEEE/ASME Transactions on Mechatronics, Associate editor of IEEE RA-L, ASME Journal of Autonomous Vehicles and Systems, Advanced Robotic Systems and many others.


Title: Multidisciplinarity in robotics applications: needs, challenges and case studies


Abstract: Robots play a vital role in various industries, from traditional industrial tasks to emerging service applications. This report advocates a systematic approach from the early design stages to improving robot performance and exploring new applications. It outlines a program focused on quantitative design specifications, simulation models, and optimized designs. The goal is to develop smart solutions that are cost-effective and user-friendly. Examples are given to illustrate the feasibility and practicality of this approach, covering a wide range of applications. The talk also highlighted the growing role of computer science, particularly in integrating machine learning (ML) and signal processing tools, as well as the use of generative tools to promote innovation and efficiency in robotic systems.





Keynote Speaker 12: Professor Ljiljana Trajkovic, School of Engineering Science, Faculty of Applied Sciences,Simon Fraser University, Canada


Brief Introduction: Ljiljana Trajkovic received the Dipl. Ing. degree from University of Pristina, Yugoslavia, the M.Sc. degrees in electrical engineering and computer engineering from Syracuse University, Syracuse, NY, and the Ph.D. degree in electrical engineering from University of California at Los Angeles. She is currently a professor in the School of Engineering Science, Simon Fraser University, Burnaby, British Columbia, Canada. Her research interests include communication networks and dynamical systems. She served as IEEE Division X Delegate/Director, President of the IEEE Systems, Man, and Cybernetics Society, and President of the IEEE Circuits and Systems Society. Dr. Trajkovic serves as Editor-in-Chief of the IEEE Transactions on Human-Machine Systems and Associate Editor-in-Chief of the IEEE Open Journal of Systems Engineering. She served as a Distinguished Lecturer of the IEEE Circuits and System Society and a Distinguished Lecturer of the IEEE Systems, Man, and Cybernetics Society. She is a Fellow of the IEEE.



Title: Data mining and machine learning research for analyzing network traffic


Abstract: Collecting and analyzing data from deployed networks is critical to understanding modern communications networks. Data mining and statistical analysis of network data are often used to determine traffic loads, analyze user behavior patterns, and predict future network traffic, while various machine learning techniques prove valuable for predicting abnormal traffic behavior. In the case study described, traffic traces collected from various deployed networks and the Internet are used to characterize and model network traffic, analyze Internet topology, and classify network anomalies.





Keynote Speaker 13: Associate Professor Ata Jahangir Moshayedi, Azad University of Khomeinishahr, Isfahan, Iran


Brief Introduction: Dr. Ata Jahangir Moshayedi, an Associate Professor at Azad University of Khomeinishahr, holds a PhD in Electronic Science from Savitribai Phule Pune University in India. He is a distinguished member of IEEE and ACM, as well as a Life Member of the Instrument Society of India and a Lifetime Member of the Speed Society of India. Additionally, he contributes to the academic community as a valued member of various editorial teams for international conferences and journals. Dr. Moshayedi's academic achievements are, marked by a portfolio of over 90 papers published across esteemed national and international journals and conferences along with 3 books on robotics (VR and mobile olfaction) and embedded systems. In addition to his scholarly publications, he has authored three books and is credited with two patents and nine copyrights, emblematic of his pioneering contributions to the field. His research interest includes Robotics and Automation/ Sensor modeling/Bio-inspired robot, Mobile Robot Olfaction/Plume Tracking, Embedded Systems / Machine vision-based Systems/Virtual reality, and Machine vision/Artificial Intelligence. Currently, Dr. Moshayedi is actively engaged in pioneering work at Jiangxi University, where he is developing a model for Automated Guided Vehicles (AGVs) and advancing the realm of Food Delivery Service Robots.



Title: Foresight Integration: Enhancing AGVs with Vision Systems and Machine Perception


Abstract: Service robots represent a transformative application of robotics that has had a profound impact on human life, spanning fields from healthcare to industry. These robots act as lifesavers and support systems, relieving humans from arduous tasks and repetitive work that may affect the accuracy of work execution. According to ISO 8373:2012, service robots include two main types: personal service robots (designed for use outside manufacturing) and professional service robots (used for non-commercial and commercial purposes). Operating on a spectrum from semi-autonomous to fully autonomous, these robots are increasingly being accepted as human assistants in a variety of applications and professions. Industries are increasingly integrating service robots into their production lines, marking a key shift in the context of the Industrial Revolution. The first revolution brought mechanization, followed by the second revolution powered by electricity. However, Industry 4.0 interweaves digital and Internet technologies, driving further innovation and development in the technology field. In this discussion, the focus is narrowed to AGVs (Automated Guided Vehicles) and MIRs (Mobile Industrial Robots) as exemplary service robots. The discussion delves into the modeling steps and simulation processes involved in its creation. Additionally, it uses various algorithms to scrutinize the performance of the designed AGV. This analysis is intended to provide guidance to researchers, providing insights and practical implementations for various control systems in modeled systems.