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

Academic Editor: Giuseppe Carbone

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  • 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.

    Mathematics Subject Classification: Research article.

    Citation:

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  • 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

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    DownLoad: CSV

    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%
     | Show Table
    DownLoad: CSV

    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
     | Show Table
    DownLoad: CSV

    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
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