Robotics and Machine Learning Applications Laboratory

Objectives

1. Acquire basic knowledge about sensors and actuators in robotics and educational robotics.
2. Know and know how to use different types of hardware support for sensors, actuators, and microcontrollers.
3. Know and know how to use some communication protocols between devices
4. Know and know how to use software for robot simulation and programming.
5. Know and know how to use Artificial Intelligence (A.I.) and Machine Learning (M.L.) software with robotics applications.

Program

1. Introduction to microcontrollers (e.g. Arduino, ESP) and communication protocols.
2. Actuators and sensors: Their use in the laboratory context.
3. Software for robotic simulation and programming and A.I and M.L. libraries.
4. Hardware and software experimentation.
(a) Use of robotic simulation and learning platforms (e.g.: ROS, Webots, Open AI).
(b) Use of available hardware for creating robotic and communication devices.
(c) Experimentation in didactic and competition contexts.

Teaching Methodologies

The curricular unit has a practical nature, so the activities will be essentially practical. The teacher presents the concepts that support the laboratory activity. Trainees carry out practical activities individually and in groups applying the concepts covered and, in groups, explore different hardware and software, using the materials available in the laboratory. The final assessment consists of an individual assessment applied educative robotics or related subject (TD) and a group assessment of the laboratory exploration activities developed (EL).
AF = 0.5* TD + 0.5 * EL
Trainees pass the subject if AF is greater than or equal to 9.5. Otherwise, they can take a written exam.

Bibliography

Ben-Ari, M., Mondana, F. (2018). Elements of Robotics, Springer Open.
Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2.ª ed.). O’Reilly Media.