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Multi-Lab-Driven Learning Method Used for Robotics ROS System Development

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2017 ASEE Annual Conference & Exposition


Columbus, Ohio

Publication Date

June 24, 2017

Start Date

June 24, 2017

End Date

June 28, 2017

Conference Session

Curricular Issues in Computing and Information Technology Programs II

Tagged Division

Computing & Information Technology

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Paper Authors


Chaomin Luo University of Detroit Mercy Orcid 16x16

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Dr. Chaomin Luo received his Ph.D. in Department of Electrical and Computer Engineering at University of Waterloo, Canada in 2008, where he was awarded Postgraduate Scholarship (PGS) from the Natural Sciences and Engineering Research Council (NSERC) of Canada; received the Best Student Paper Presentation Award at the SWORD’2007 Conference, earned his M.Sc. in Engineering Systems and Computing at University of Guelph, Canada, and his B.Eng. degree in Radio Engineering from Southeast University, China. He is currently an Associate Professor, Department of Electrical and Computer Engineering, at University of Detroit Mercy, Michigan, USA. He was awarded Faculty Research Awards in 2009, 2010, 2014, 2015, and 2016 at University of Detroit Mercy, Michigan, USA. His research interests include engineering education, robotics and automation, control, autonomous systems, computational intelligence and machine learning.

Dr. Luo 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), USA. He was the Publicity Chair in the 2011 IEEE International Conference on Automation and Logistics. He was on the Conference Committee in the 2012 International Conference on Information and Automation and International Symposium on Biomedical Engineering and also the Publicity Chair in the 2012 IEEE International Conference on Automation and Logistics. Also, he was Chair and Vice Chair of IEEE SEM - Computational Intelligence Chapter and is currently a Chair of IEEE SEM - Computational Intelligence Chapter and Chair of Education Committee of IEEE SEM.

Dr. Luo serves as the Editorial Board Member of International Journal of Complex Systems – Computing, Sensing and Control; Associate Editor of International journal of Robotics and Automation (IJRA); and Associate Editor of International Journal of Swarm Intelligence Research (IJSIR). He has organized and chaired several special sessions on topics of Intelligent Vehicle Systems and Bio-inspired Intelligence in IEEE reputed international conferences such as IEEE-IJCNN, IEEE-SSCI, etc. He was the Panelist in the Department of Defense, USA, 2015-2016, 2016-2017 NDSEG Fellowship program, and National Science Foundation, USA, GRFP program, 2016-2017.

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Jiawen Wang University of Detroit Mercy

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Dr. Jiawen Wang holds a doctoral degree in Educational Psychology and Educational Technology from Michigan State University. All his interests lie in research of how to make learning happen. His interest in recent years include pedagogies in engineering education.

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Wenbing Zhao Cleveland State University Orcid 16x16

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Dr. Zhao is a Full Professor at the Department of Electrical Engineering and Computer Science, Cleveland State University (CSU). He earned his Ph.D. at University of California, Santa Barbara in 2002. Dr. Zhao has a Bachelor of Science degree in Physics in 1990, and a Master of Science degree in Physics in 1993, both at Peking University, Beijing, China. Dr. Zhao also received a Master of Science degree in Electrical and Computer Engineering in 1998 at University of California, Santa Barbara. Dr. Zhao joined CSU faculty in 2004. He is currently serving as the director of the Master of Science in Electrical Engineering, and the Chair of the Graduate Program Committee in the Department of EECS, the ABET coordinator for the BS in Computer Science Program, and a member of the faculty senate at CSU. Dr. Zhao has authored a research monograph titled: “Building Dependable Distributed Systems” published by Scrivener Publishing, an imprint of John Wiley and Sons. Furthermore, Dr. Zhao published over 150 peer-reviewed papers on fault tolerant and dependable systems (three of them won the best paper award), computer vision and motion analysis, physics, and education. Dr. Zhao’s research is supported in part by the US National Science Foundation, the US Department of Transportation, Ohio State Bureau of Workers’ Compensation, and by Cleveland State University. Dr. Zhao has served on the organizing committee and the technical program committee for numerous international conferences. Dr. Zhao is an Associate Editor for IEEE Access, an Academic Editor for PeerJ Computer Science, and is a member of the editorial board for International Journal of Parallel Emergent and Distributed Systems, International Journal of Distributed Systems and Technologies, International Journal of Performability Engineering, International Journal of Handheld Computing Research. Dr. Zhao is a senior member of IEEE.

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Lei Wang Anhui Polytechnic University

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Lei Wang received the Ph.D. degree in mechanical and electronic engineering from Nanjing University of Aeronautics and Astronautics, Nanjing, China, in 2010. From November 2010 till
date he works in Anhui Polytechnic University, Wuhu, China. He is an Associate Professor at Anhui Polytechnic University. His current research interests include engineering education, intelligent manufacturing system, job shop scheduling and mobile robot path planning.

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The Robot Operating System (ROS), a collection of tools, libraries, and conventions, is a powerful framework for programming robot software, and ROS-based mobile robot systems are becoming increasingly significant in human life. ROS has therefore been extensively taught in robotics program in electrical engineering programs. However, although it is a low-cost solution to allowing students to perform a variety of simulations and validating new algorithms before implementing them on an actual mobile robot, teaching ROS so that students can use it efficiently and effectively is a challenging task. Regular electrical engineering courses on ROS may focus on theories but neglect hands-on opportunities. Traditional lab-driven pedagogy may provide hands-on opportunities on ROS itself but may still not bring students close enough to the actual application of ROS to their major robot projects in their electrical engineering education. We argue that the best way to learn a tool is to use the tool to solve problems that the tool is designed to solve, and we therefore argue that the knowledge we want our students to learn about the tool is the technological content knowledge (TCK), with which we suggest that we need to create learning opportunities that allow students to construct their knowledge of the technology/tool (the T) in close relation to the content/robot programming (the C).

In this paper we report the multi-lab-driven method (MLDM) that we use to help our students to construct their TCK of ROS in the context of designing an autonomous mobile robot system. A sequence of well-prepared multiple labs were assigned to students to cover various topics in the ROS such as navigation, mapping, SLAM, path planning, image processing, and localization, all of which were associated to the actual robot project and could be reviewed and further explored throughout the multiple labs over the semester. A variety of labs that reflect the ROS experiments and assist students in better understanding robotics programming were elaborately managed. Based on students’ performance on various milestone assignments, lab reports, presentations, and the final robot project, students’ input to the official course evaluation administered by the college/university, and a comparison to the instructor’s previous years of teaching experience, we propose that the MLDM is effective in helping students to learn ROS efficiently and meaningfully in the real world of engineering projects.

Luo, C., & Wang, J., & Zhao, W., & Wang, L. (2017, June), Multi-Lab-Driven Learning Method Used for Robotics ROS System Development Paper presented at 2017 ASEE Annual Conference & Exposition, Columbus, Ohio. 10.18260/1-2--28692

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