1. Acquire general knowledge about Artificial Intelligence (AI).
2. Know how to characterize a robot and identify its components.
3. Know and know how to use reactive and control algorithms applied to a robot.
4. Know and know how to use AI algorithms used in robotics.
5. Know how to use simulation platforms in Robotics and AI.
1. Introduction to AI. Concept of Agent.
2. Identify how to create a search tree in a state space.
3. Introduction to Robotics. The concept of a generic robot. Types of robots.
4. Knowing what optimization algorithms are and how to use them.
5. Uncertainty in AI, concepts of probability, conditional probability. Know how to build Bayesian networks.
6. Robotic and AI simulation platforms.
The course will have an theoretical and an experimental component. In the expository component, the teacher will present the contents with application examples. In the experimental component, trainees are invited to use a simulation platform for testing and programming proposed algorithms.
The final assessment (FA) is made up of an assessment component consisting of asynchronous activities (AA), a group assessment component (TG) in project work format (minimum mark 10) and an assessment test (TA) (minimum mark 9.0).
FA = 0.35 * AA + 0.25 * TG + 0.40 * TA
Students have a positive assessment if AF is greater than or equal to 9.5. If they have a lower value, they will have to take a final written exam.
Ben-Ari, M., Mondana, F. (2018). Elements of Robotics, Springer Open.
Murphy, R. (2019). Introduction to AI Robotics. Second Edition. Massachusetts Institute of Technology.
Russell, S. and Norvig, P. (2020). Artificial Intelligence. A Modern Approach. 4rd edition. Prentice Hall.
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