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February  2021, 1(1): 32-46. doi: 10.3934/steme.2021003

Interactive MATLAB based project learning in a robotics course: Challenges and achievements

1. 

Mechatronics Engineering, Department of Mechanical Engineering, American University of Sharjah, P.O Box 26666, Sharjah, UAE

* Correspondence: lromdhane@aus.edu; Tel: +971-6-5152497

Academic Editor: Giuseppe Carbone

Received  October 2020 Revised  January 2021 Published  February 2021

This paper illustrates the conducted efforts for deploying an interactive project-based learning for robotics course using MATLAB. This project is part of a first course on robotics at the graduate level. The course combines both the theoretical and practical aspects to achieve its goals. The course consists of a set of laboratory sessions ends with a class project, these labs experimentally illustrate the modeling, simulation, path-planning and control of the Robot, using the robotics toolbox under MATLAB tools as well as physical interaction with the different robot platforms. The interaction between the student and the physical robot platforms is finally addressed in the class project; in this project, two tasks are considered. The first one is to control a 5DoF robot manipulator to perform a pick and place task. Initially the task is simulated under MATLAB robotics toolbox; the robot is commended to pick objects from initially known poses and stacks them in target poses. Furthermore, the robot manipulator in the second part of the project, with the aid of a vision system, is commended to work as an autonomous robotic arm that picks up colored objects, and then places them in different poses, based on their identified colors. The demonstrated results from the course evolution and assessment tools reflect the benefits of high-level deployment of robot platform in interactive project based learning to increase the students' performance in the course, about 100% and 75% of the student groups successfully completed the required tasks in the project first part and second part respectively.

Citation: Lotfi Romdhane, Mohammad A. Jaradat. Interactive MATLAB based project learning in a robotics course: Challenges and achievements. STEM Education, 2021, 1 (1) : 32-46. doi: 10.3934/steme.2021003
References:
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https://www.quanser.com/products/omni-bundle/ [accessed September 2020] Google Scholar

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Y. Kim, . Control systems lab using a LEGO Mindstorms NXT motor system, IEEE Transactions on Education, 54 (2010), 452-461.   Google Scholar

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N. Spolaôr and F.B.V. Benitti, . Robotics applications grounded in learning theories on tertiary education: A systematic review, Computers & Education, 112 (2017), 97-107.   Google Scholar

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S. González-GarcíaJ. Rodríguez-ArceG. Loreto-Gómez and V.M. Montaóo-Serrano, Teaching forward kinematics in a robotics course using simulations: Transfer to a real-world context using LEGO mindstormsTM, International Journal on Interactive Design and Manufacturing (IJIDeM), 14 (2020), 773-787.   Google Scholar

[34]

Kobzev, A., Monakhov, Y., Lekareva, A. (2019). Using Matlab software package in the curriculum of Mechatronics and Robotics. The IEEE International Conference on Industrial Engineering, Applications and Manufacturing, pp. 1-6. Google Scholar

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N.M.F. Ferreira, . A generalized Matlab/ROS/Robotic platform framework for teaching robotics, Robotics in Education: Current Research and Innovations, 1023 (2019), 159.   Google Scholar

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M.A.K. JaradatM. Al-Fandi and M.T. Nasir, . Automatic control for a miniature manipulator based on 3D vision servo of soft objects, Mechatronics, 22 (2012), 468-480.   Google Scholar

[39]

Al-Fandi, M., Jaradat, M.A., Abusaif, A., Yih, T.C. (2010). A real time vision feedback system for automation of a nano-assembly manipulator inside scanning electron microscope. The 7th IEEE International Multi-Conference on Systems, Signals and Devices, pp. 1-5. Google Scholar

show all references

References:
[1]

R. RonaldD.S. BloomJ. CarpinelliL. Burr-AlexanderL.S. Hirsch and H. Kimmel, Advancing the "E" in K-12 STEM education, The Journal of Technology Studies, 36 (2010), 53-64.   Google Scholar

[2]

S.E. Jung and E.S. Won, Systematic review of research trends in robotics education for young children, Sustainability, 10 (2018), 905.   Google Scholar

[3]

Bredenfeld, A., Hofmann, A., Steinbauer, G. (2010) Robotics in education initiatives in Europe - Status, shortcomings and open questions. Proceedings of International Conference on Simulation, Modeling and Programming for Autonomous Robots, November 15-16, 2010, Darmstadt (Germany). Google Scholar

[4]

D. Rus, , Teaching robotics everywhere, IEEE Robotics & Automation Magazine, 13 (2006), 15-94.   Google Scholar

[5]

M. CalnonC.M. Gifford and A. Agah, Robotics competitions in the classroom: Enriching graduate-level education in computer science and engineering, Global Journal of Engineering Education, 14 (2012), 6-13.   Google Scholar

[6]

D. Alimisis, , Educational robotics: Open questions and new challenges, Themes in Science & Technology Education, 6 (2013), 63-71.   Google Scholar

[7]

A. Eguchi, Educational robotics for promoting 21st century skills, Journal of Automation, Mobile Robotics & Intelligent Systems, 8 (2014), 5-11.   Google Scholar

[8]

Amy, E. (2015) Educational robotics as a learning tool for promoting rich environments for active learning (REALs). Handbook of Research on Educational Technology Integration and Active Learning, edited by Jared Keengwe, IGI Global, 19-47. Google Scholar

[9]

D.L. Zeidler, STEM education: A deficit framework for the twenty first century? A sociocultural socioscientific response, Cultural Studies of Science Education, 11 (2016), 11-26.   Google Scholar

[10]

Aroca, R.V., Watanabe, F.Y., De Ávila M.T., Hernandes, A. C. (2016) Mobile robotics integration in introductory undergraduate engineering courses. The XⅢ Latin American Robotics Symposium and IV Brazilian Robotics Symposium, 139-144. Google Scholar

[11]

S.S. Joshi, Development and implementation of a MATLAB simulation project for a multidisciplinary graduate course in autonomous robotics, Computer Applications in Engineering Education, 12 (2004), 54-64.   Google Scholar

[12]

C. Hamilton, Using MATLAB to advance the robotics laboratory, Computer Applications in Engineering Education, 14 (2007), 205-213.   Google Scholar

[13]

L. ŽlajpahB. Nemec and D. Omr en, MATLAB-based robot control design environment for research and education, Simulation Notes Europe, 20 (2010), 55-66.   Google Scholar

[14]

P. CorkeE. Greener and R. Philip, An innovative educational change: Massive open online courses in robotics and robotic vision, IEEE Robotics & Automation Magazine, 23 (2016), 81-89.   Google Scholar

[15]

Corke, P. (2013) Robotics, Vision and Control: Fundamental Algorithms in MATLAB (1st ed.). Springer. Google Scholar

[16]

https://www.quanser.com/products/omni-bundle/ [accessed September 2020] Google Scholar

[17]

https://www.kinovarobotics.com/en/knowledge-hub/mico-robotic-arm [accessed September 2020] Google Scholar

[18]

https://yujinrobot.com/ [accessed September 2020] Google Scholar

[19]

https://global.abb/group/en [accessed September 2020] Google Scholar

[20]

https://www.mathworks.com/ [accessed September 2020] Google Scholar

[21]

http://www.lynxmotion.com/ [accessed September 2020] Google Scholar

[22]

Azemi, A., Pauley, L.L. (2008). Teaching the introductory computer programming course for engineers using Matlab. The 38th IEEE Annual Frontiers in Education Conference. Google Scholar

[23]

Behrens, A., Atorf, L., Aach, T. (2010). Teaching practical engineering for freshman students using the RWTH-Mindstorms NXT Toolbox for MATLAB. Matlab-Modelling, Programming and Simulations, 41-65. Google Scholar

[24]

A. BehrensL. AtorfR. SchwannB. NeumannR. SchnitzlerJ. BalleT. HeroldA. TelleT.G. NollK. Hameyer and T. Aach, . MATLAB meets LEGO Mindstorms - A freshman introduction course into practical engineering, IEEE Transactions on Education, 53 (2009), 306-317.   Google Scholar

[25]

Lugaresi, G., Lin, Z., Frigerio, N., Zhang, M., Matta, A. (2019). Active learning experience in simulation class using a LEGO®-based manufacturing system. IEEE Winter Simulation Conference (WSC), pp.3307-3318. Google Scholar

[26]

A. Cruz-MartínJ.A. Fernández-MadrigalC. GalindoJ. González-JiménezC. Stockmans-Daou and J.L. Blanco-Claraco, . A LEGO Mindstorms NXT approach for teaching at data acquisition, control systems engineering and real-time systems undergraduate courses, Computers & Education, 59 (2012), 974-988.   Google Scholar

[27]

Y. Kim, . Control systems lab using a LEGO Mindstorms NXT motor system, IEEE Transactions on Education, 54 (2010), 452-461.   Google Scholar

[28]

J. DingZ. Li and T. Pan, . Control system teaching and experiment using LEGO Mindstorms NXT robot, International Journal of Information and Education Technology, 7 (2017), 309.   Google Scholar

[29]

Montés, N., Rosillo, N., Mora, M.C., Hilario, L. (2018). Real-time Matlab-Simulink-Lego EV3 framework for teaching robotics subjects. International Conference on Robotics and Education. Springer. Google Scholar

[30]

N. Spolaôr and F.B.V. Benitti, . Robotics applications grounded in learning theories on tertiary education: A systematic review, Computers & Education, 112 (2017), 97-107.   Google Scholar

[31]

https://www.quanser.com/products/qbot-2e/. [accessed September 2020] Google Scholar

[32]

Ackovska, N., Kirandziska, V. (2017). The importance of hands-on experiences in robotics courses. The 17th IEEE International Conference on Smart Technologies. Google Scholar

[33]

S. González-GarcíaJ. Rodríguez-ArceG. Loreto-Gómez and V.M. Montaóo-Serrano, Teaching forward kinematics in a robotics course using simulations: Transfer to a real-world context using LEGO mindstormsTM, International Journal on Interactive Design and Manufacturing (IJIDeM), 14 (2020), 773-787.   Google Scholar

[34]

Kobzev, A., Monakhov, Y., Lekareva, A. (2019). Using Matlab software package in the curriculum of Mechatronics and Robotics. The IEEE International Conference on Industrial Engineering, Applications and Manufacturing, pp. 1-6. Google Scholar

[35]

N.M.F. Ferreira, . A generalized Matlab/ROS/Robotic platform framework for teaching robotics, Robotics in Education: Current Research and Innovations, 1023 (2019), 159.   Google Scholar

[36]

Matlab Image Processing Toolbox, https://www.mathworks.com/help/matlab. [accessed September 2020] Google Scholar

[37]

Spong, M.W., Hutchinson, S., Vidyasagar, M. (2006). Robot Modeling and Control. John Wiley and Sons. Google Scholar

[38]

M.A.K. JaradatM. Al-Fandi and M.T. Nasir, . Automatic control for a miniature manipulator based on 3D vision servo of soft objects, Mechatronics, 22 (2012), 468-480.   Google Scholar

[39]

Al-Fandi, M., Jaradat, M.A., Abusaif, A., Yih, T.C. (2010). A real time vision feedback system for automation of a nano-assembly manipulator inside scanning electron microscope. The 7th IEEE International Multi-Conference on Systems, Signals and Devices, pp. 1-5. Google Scholar

Figure 1.  Initial and final poses of the blocs for Groups 1 and 2
Figure 2.  Initial and final poses of the blocs for Groups 3 and 4
Figure 3.  Robot simulation in MATLAB®
Figure 4.  Fourth angle calculation method
Figure 5.  Joint angles variation between the pick and place poses
Figure 6.  Results of the winning team (Team 1)
Figure 7.  Results obtained by Team 4
Figure 8.  Frames transformation from image space (orange) to robot workspace (yellow).
Figure 9.  Block diagram for the of vision system
Figure 9.  3 experiments where the blocs were detected based on their colors
Table 1.  List of Robots used in the Labs
Table 2.  Assessment rubrics
Rubric 1 Report and presentation: quality, scientific content, Matlab code… 30%
Rubric 2 Accuracy of the final pose of the blocs 35%
Rubric 3 Speed of execution of the task 20%
Rubric 4 Smoothness of the motion 15%
Rubric 5 (Bonus) Vision-based task 10%
Rubric 1 Report and presentation: quality, scientific content, Matlab code… 30%
Rubric 2 Accuracy of the final pose of the blocs 35%
Rubric 3 Speed of execution of the task 20%
Rubric 4 Smoothness of the motion 15%
Rubric 5 (Bonus) Vision-based task 10%
Table 3.  DH parameter of the robot
i Links θi(rad) $ {\mathit{d}}_{\mathit{i}} $(cm) $ {\mathit{\boldsymbol{\alpha}}}_{\mathit{\boldsymbol{i}}} $(rad) $ {\mathit{\boldsymbol{a}}}_{\mathit{\boldsymbol{i}}} $(cm) offset
1 Base $ {\theta }_{1} $ $ 6.5\pm 0.5 $ -90 0 -90
2 Shoulder $ {\theta }_{2} $ 0 0 $ 14.5\pm 0.5 $ -90
3 Elbow $ {\theta }_{3} $ 0 0 $ 18.5\pm 0.5 $ 90
4 Wrist $ {\theta }_{4} $ 0 -90 0 0
5 Gripper $ {\theta }_{5} $ 12.5 0 0 -90
i Links θi(rad) $ {\mathit{d}}_{\mathit{i}} $(cm) $ {\mathit{\boldsymbol{\alpha}}}_{\mathit{\boldsymbol{i}}} $(rad) $ {\mathit{\boldsymbol{a}}}_{\mathit{\boldsymbol{i}}} $(cm) offset
1 Base $ {\theta }_{1} $ $ 6.5\pm 0.5 $ -90 0 -90
2 Shoulder $ {\theta }_{2} $ 0 0 $ 14.5\pm 0.5 $ -90
3 Elbow $ {\theta }_{3} $ 0 0 $ 18.5\pm 0.5 $ 90
4 Wrist $ {\theta }_{4} $ 0 -90 0 0
5 Gripper $ {\theta }_{5} $ 12.5 0 0 -90
Table 4.  Grading of the different teams
Rubric 1 (30%) Rubric 2 (35%) Rubric 3 (20%) Rubric 4 (15%) Rubric 5 bonus (10%) Score (110%)
Team 1 5 5 5 5 5 110
Team 2 4 4 3 3 - 73
Team 3 3 3 3 3 3 66
Team 4 4 4 3 4 2 80
Rubric 1 (30%) Rubric 2 (35%) Rubric 3 (20%) Rubric 4 (15%) Rubric 5 bonus (10%) Score (110%)
Team 1 5 5 5 5 5 110
Team 2 4 4 3 3 - 73
Team 3 3 3 3 3 3 66
Team 4 4 4 3 4 2 80
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