Developing Computational Intelligence Curriculum Materials to Advance Student Learning for Robot Control and Optimization
- Conference
- Location
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Portland, Oregon
- Publication Date
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June 23, 2024
- Start Date
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June 23, 2024
- End Date
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July 12, 2024
- Conference Session
- Tagged Division
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Systems Engineering Division (SYS)
- Permanent URL
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https://peer.asee.org/47162
Paper Authors
Tingjun Lei Mississippi State University
Dr. Tingjun Lei is currently a Postdoctoral Research Fellow in the Department of Electrical and Computer Engineering at the Mississippi State University (MSU). He received his Ph.D. degree in electrical and computer engineering with the Department of Electrical and Computer Engineering, Mississippi State University, Mississippi State, MS, USA., in 2023, his M.S. degree in electrical and computer engineering from the New York Institute of Technology, Old Westbury, NY, USA, in 2016, and the B.S. degree in intelligent transportation engineering from Shanghai Maritime University, Shanghai, China, in 2014. He was Graduate Teaching Assistant for ECE1013 Foundations in ECE, ECE1022 Foundations in Design, ECE4713/6713 Computer Architecture, and ECE4753/6753 Introduction to Robotics at the undergraduate level and as a guest lecturer delivered graduate-level courses, ECE 8743 Advanced Robotics and ECE8833 Computational Intelligence. He received the ECE Best Graduate Researcher Award from the Department of Electrical and Computer Engineering, Mississippi State University in 2023. He received the Research Travel Award from Bagley College of Engineering, Mississippi State University in 2023. His two papers have been selected and featured as cover articles on Intelligence & Robotics Journal. He won six oral and poster presentation awards at multiple conferences. Dr. Lei received the Best Paper Award in 2022 International Conference on Swarm Intelligence. Dr. Lei serves as Youth Editorial Board Member of Intelligence and Robotics. Dr. Lei has served on the technical program committee for numerous international conferences, such as IEEE-CEC, IEEE-IJCNN, ICSI, and PRIS, etc. Dr. Lei has extensively published journal and conference papers in robotics, intelligent systems, and engineering education areas. His research interests include engineering education, robotics and autonomous systems, human robot interaction, deep learning, intelligent transportation systems, and evolutionary computation.
Timothy Sellers
Mississippi State University
orcid.org/0000-0001-8344-9804
Timothy Sellers received the B.S. degree in robotics and automation technology and applied science in electro-mechanical engineering from the Alcorn State University, Lorman, MS, USA in 2020. He is currently pursuing a Ph.D. degree in the Department of Electrical and Computer Engineering at Mississippi State University, Mississippi State, MS, USA. He is currently a Graduate Teaching Assistant for Senior Design II (ECE4542/ECE4522) and was for Advance Circuits (ECE3434) at the undergraduate level and as guest lecturer delivered graduate-level courses, Advanced Robotics (ECE 8743) and Computational Intelligence (ECE 8833). He received the ECE Outstanding Teaching Assistant Award from the Department of Electrical and Computer Engineering, Mississippi State University in 2021. He received the Research Travel Award from Bagley College of Engineering, Mississippi State University in 2024. He has also received the Bagley College of Engineering Student Hall of Fame award in 2024. He won three poster presentation awards at multiple conferences. Mr. Sellers has served on the technical program committee for numerous international conferences and journals, such as IJMLC, ICSI, and PRIS, etc. He has extensively published journal and conference papers in engineering education and robotics fields. His research interests include engineering education, robotics and autonomous systems, human robot interaction, deep learning, and computational intelligence.
Chaomin Luo
Mississippi State University
orcid.org/0000-0002-7578-3631
Dr. Chaomin Luo (Senior Member, IEEE) holds a Ph.D. degree in electrical and computer engineering from the Department of Electrical and Computer Engineering at the University of Waterloo, Canada in 2008. He also earned an M.Sc. degree in engineering systems and computing from the University of Guelph, Canada in 2002, and a B.Sc. in electrical engineering from Southeast University. Currently, he is an Associate Professor in the Department of Electrical and Computer Engineering at Mississippi State University. His research interests include engineering education, intelligent systems, control and automation, robotics, and autonomous systems. He is Associate Editor in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2019). He is Tutorials Co-Chair in the 2020 IEEE Symposium Series on Computational Intelligence. Dr. Luo was the recipient of the Best Paper Awards in IEEE International Conference on Information and Automation, International Conference on Swarm Intelligence, and SWORD Conference. His research interests include Robotics, Autonomous Systems, and Control and Automation. Dr. Luo is an IEEE senior member, INFORMS, and ASEE member. Dr. Luo is active nationally and internationally in his research field. He was the Program Co-Chair in 2018 IEEE International Conference on Information and Automation (IEEE-ICIA’2018). He was the Plenary Session Co-Chair in the 2021 and 2019 International Conference on Swarm Intelligence, and he was the Invited Session Co-Chair in the 2017 International Conference on Swarm Intelligence. He was the General Co-Chair of the 1st IEEE International Workshop on Computational Intelligence in Smart Technologies (IEEE-CIST 2015), and Journal Special Issues Chair, IEEE 2016 International Conference on Smart Technologies (IEEE-SmarTech), Cleveland, OH, USA. He was Chair and Vice Chair of IEEE SEM - Computational Intelligence Chapter and was a Chair of IEEE SEM - Computational Intelligence Chapter and Chair of Education Committee of IEEE SEM. He has organized and chaired several special sessions on topics of Intelligent Vehicle Systems and Bio-inspired Intelligence in reputed international conferences such as IJCNN, IEEE-SSCI, IEEE-CEC, IEEE-CASE, and IEEE-Fuzzy, etc. He has extensively published in reputed journals and conference proceedings, such as IEEE Transactions on Industrial Electronics, IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on SMC, IEEE Transactions on Cybernetics, IEEE-ICRA, and IEEE-IROS, etc. Dr. Luo serves as Associate Editor of IEEE Transactions on Cognitive and Developmental Systems, International journal of Robotics and Automation, and Associate Editor of International Journal of Swarm Intelligence Research (IJSIR).
Zhuming Bi
Purdue University
orcid.org/0000-0002-8145-7883
Zhuming Bi (Senior Member, IEEE) received the Ph.D. degree from the Harbin Institute of Technology, Harbin, China, in 1994, and the Ph.D. degree from the University of Saskatchewan, Saskatoon, SK, Canada, in 2002. He has international work experience in Mainland China, Hong Kong, Singapore, Canada, UK, Finland, and USA. He is currently a professor of Mechanical Engineering with Purdue University Fort Wayne, Fort Wayne, IN, USA. His current research interests include robotics, mechatronics, Internet of Things (IoT), digital manufacturing, automatic robotic processing, and enterprise information systems. He has published 6 research books and over 180 journal publications in these fields.
Gene Eu Jan Tainan National University of the Arts
Gene Eu Jan (M’00) received the B.S. degree in electrical engineering from National Taiwan University, Taipei, Taiwan, in 1982 and the M.S. and Ph.D. degrees in electrical and computer engineering from the University of Maryland, College Park, MD, USA, in 1988 and 1992, respectively.
He has been a Professor with the Departments of Computer Science and Electrical Engineering, National Taipei University, New Taipei City, Taiwan since 2004, where he also served as the Dean of the College of Electrical Engineering and Computer Science from 2007 to 2009. Currently, he is the president of Tainan National University of the Arts. He has published more than 270 articles related to parallel
computer systems, interconnection networks, path planning, electronic design automation,
and VLSI systems design in journals, conference proceedings, and books.
Abstract
Nature-inspired intelligence, an integral component of computational intelligence curriculum. The integration of nature-inspired intelligence methodologies with robotics has become increasingly prominent in research and education. Among its diverse applications, the utilization of these methods for optimizing robot path planning and enhancing motion control stands out as a significant advancement in the field of computational intelligence. However, the incorporation of these concepts into computational intelligence curricula presents a noteworthy pedagogical challenge. Nature-inspired intelligence draws inspiration from the behaviors and strategies observed in natural systems, including animals, plants, and ecological processes. These approaches strive to create path planning algorithms that are both efficient and adaptive by mimicking the principles found in nature. In this research, a pedagogy of sparrow-dissection and scaffolding (SDS) integrated with a flipped learning and a milestone on-going project-based method is developed to assist students to comprehend, create, and implement nature-inspired intelligence models for robot path planning optimization and control. In our graduate-level Computational Intelligence curriculum, we introduce students to various nature-inspired intelligence methods such as particle swarm optimization (PSO), genetic algorithms (GA), and bat algorithms (BA). These methods are provided along with their source codes, serving as a 'sparrow' for students to dissect and explore how nature-inspired intelligence can be applied to optimize robot path planning. Working collaboratively with students, we guide them through the process of revising and customizing the provided source codes for the purpose of robot path planning. Integrating flipped learning and a project-based approach with multiple milestones can establish a dynamic and captivating learning environment. Within this flipped classroom model, students receive nature-inspired intelligence algorithm materials before class, including reading assignments and online resources. This pre-class preparation empowers students to review these materials at their own convenience, enabling them to build a solid foundation in nature-inspired intelligence methods. These ongoing projects, integrated with the flipped learning pedagogy, serve a dual purpose. They not only aim to improve students' comprehension of nature-inspired intelligence algorithms for robot path planning but also to nurture and sharpen their problem-solving, critical thinking, and problem analysis skills. During in-class time, we utilize flipped learning activities to introduce and discuss these ongoing projects. We provide project guidelines and objectives, and we organize students into groups to collaborate on projects related to nature-inspired intelligence algorithms for robot path planning. We also elaborately design and provide various practice exercises after each lecture. This hybrid pedagogy empowers students to take ownership of their learning in nature-inspired intelligence algorithms, build problem-solving skills and connect these algorithms to practice in robot path planning. It promotes active engagement, critical thinking, and collaborative learning, making the educational experience more dynamic and meaningful for students. Based on students' performance in homework assignments, Q&A sessions, exams, self-assessment surveys, and their feedback in the official university course evaluation, along with a comparison to the instructor's other teaching experiences, the hybrid pedagogy adopted was highly effective in facilitating meaningful learning of nature-inspired intelligence models. The final assessment and evaluation of this innovative hybrid pedagogy for delivering computational intelligence education, informed by valuable insights, further confirms its effectiveness.
Lei, T., & Sellers, T., & Luo, C., & Bi, Z., & Jan, G. E. (2024, June), Developing Computational Intelligence Curriculum Materials to Advance Student Learning for Robot Control and Optimization Paper presented at 2024 ASEE Annual Conference & Exposition, Portland, Oregon. https://peer.asee.org/47162
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