Dataset accompanying SEFi 2023 paper is a M.Tech project topic for Mechanical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Dataset accompanying SEFi 2023 paper Project Details
| Abstract |
This project looks at how multidisciplinary teaching programs can train future robotics engineers to design complex robot software. Modern robotics work requires combining mechanical design, control systems, and advanced software engineering. To study this, we examined educational data from curricula that mix these fields, focusing on how student groups connect software architectures with physical robots. Our method evaluates student performance, the software design patterns they use, and their collaborative problem‑solving approaches, all recorded in the SEFi 2023 dataset. We apply statistical analysis and educational assessment models to see how well project‑based learning teaches the Robot Operating System (ROS), modular software design, and hardware‑in‑the‑loop simulation. The findings give concrete advice for
building courses that link theoretical control theory with real‑world software implementation. They also highlight the main teaching challenges and successful interventions, offering a systematic way to improve multidisciplinary engineering education in robotics.
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| Reference Paper |
Dataset accompanying the SEFi 2023 paper: Educating Future Robotics Engineers In Multidisciplinary Approaches In Robot Software Design |
| Domain |
Mechatronics & Robotics |
| Sub-Domain |
Mechatronics & Robotics / Robotics |
| PDF Download |
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| Get Help |
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