Designing Toy Robots To Help Autistic Children An Open Design Project For Electrical And Computer Engineering Education
Bibliographic record
Abstract
Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 2220 Designing Toy Robots to Help Autistic Children - An Open Design Project for Electrical and Computer Engineering Education Francois Michaud, André Clavet, Gérard Lachiver, Mario Lucas Université de Sherbrooke (Québec Canada) Abstract In our curricula, freshmen use an autonomous robotic platform to get introduced to fundamental concepts in Electrical and Computer Engineering. Using this platform, teams of students interested by the challenge are invited to apply knowledge acquired during their first year of studies by participating in a toy robot design contest. Initiated in 1999, the challenge is to design a mobile robot to help autistic children. The goal of this paper is to describe the contest, its organization, its pedagogic principles and its impacts in order to show how open design projects can create meaningful and exciting learning experiences for students in Electrical and Computer Engineering. I. Introduction The Department of Electrical and Computer Engineering at the Université de Sherbrooke offers two distinct bachelor engineering degrees, one in Electrical Engineering and one in Computer Engineering. In 1998, we initiated a pedagogical project in which Electrical and Computer Engineering (ECE) were introduced simultaneously to a large group of first-year undergraduate students registered in these two distinct programs. The primary goal of this project was to confirm early on the career choice of these students by putting them close to the reality of the profession and making them work on projects involving design and analysis abilities, autonomous learning, teamwork, communication skills and social considerations. We also wanted to create a stimulating and motivating learning environment, with a reasonable workload that favored the integration and the application of the engineering knowledge and skills. To accomplish this goal, we were looking for a project that could integrate these ideas in different courses with appropriate complexity, and also provide open challenges that push further the creativity and the ingenuity of the students. With that in mind, we developed an autonomous mobile robotic platform that we named ROBUS10. ROBUS was given to the students completely unassembled, and their first challenge was to build and test the robot by using the documentation provided9 . This process revealed to be very exciting for many students who were introduced, for the first time in their life, to electronics and instrumentation. Then, ROBUS was used in projects from six of the ten courses given during the first year10. For instance during the first semester, in the Logic Circuits course, students first designed a combinational logic circuit to make the robot move freely in the environment and turn away when it collided with an object. They also learned to use a Xilinx CPLD board to control the robot. The assignment was to design a system that could memorize a series of commands given from a keyboard, and play back these commands at the appropriate time. The task required to memorize the commands that made the robot follow a path drawn on the floor, and also the commands that made the robot avoid an obstacle of known dimensions. Then, the robot was placed at the start of the path and had to try to repeat it by having to avoid the obstacle (detected by infrared proximity sensors) placed somewhere on its way. During the second semester, the Introduction to Circuits and Microprocessors course allowed students to use a simple analog circuit to again make the robot move freely in the environment6. They also
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".