Introduction to Intelligent Robotics

Code:

CC3046

Acronym:

CC3046

Level:

300

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2023/2024 - 2S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Bachelor in Artificial Intelligence and Data Science

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
L:IACD	56	study plan from 2021/22	3	-	6	48	162

Teaching language

Suitable for English-speaking students

Objectives

1. To understand the basic concepts of robotics and the context of artificial intelligence in robotics.
2. To study methods of perception and sensorial interpretation, which allow creating precise world estates and mobile robots’ localization methods.
3. To study the methods which allow mobile robots to move and navigate in familiar or unfamiliar environments using planning and navigation algorithms.
4. To understand and use the main machine learning algorithms for robotics.
5. To analyze the main national and international robotics competitions, the more realistic robot simulators and the more advanced robotic platforms available in the market.
6. To Improve the ability to communicate regarding scientific and technical issues and promote a healthy scientific approach.

Learning outcomes and competences

- Knowledge of intelligent robotics.
- Knowledge of sensory perception and interpretation.
- Knowledge of navigation.
- Knowledge and ability to apply computational learning algorithms for robots.
- Knowledge of the main robotics competitions, robotic simulators and robotic platforms.
- Ability to carry out scientific work in the area of intelligent robotics.
- Capacity to carry out a complete robotics project.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Knowledge of Programming (Python or C++) and Artificial Intelligence.

Program

1. Introduction to Intelligent Robotics (IR): Basic Concepts; Architectures for Robotic Agents; History, Evolution, and Current Trends in Intelligent Robotics.
2. Perception and Action: Sensors; Robot Vision; Sensor interpretation; Locomotion and Action; Actuator Types.
3. Localization, Mapping and Navigation: Localization and Mapping Methods; Path Planning; Obstacle Avoidance; Navigation.
4. Robot Learning: Supervised, Evolutionary, Reinforcement and Deep Reinforcement Learning for Robotics.
5. Robotics in the Future: Artificial Intelligence and Robotics in the Future.

Mandatory literature

Stuart Jonathan Russell; Artificial intelligence. ISBN: 978-1-292-40113-3
Sebastian Thrun; Probabilistic robotics. ISBN: 0-262-20162-3

Teaching methods and learning activities

- Exposition with interaction in classes.
- Use of simulators for mobile robots and exploration of robotic platforms.
- Assignments on robot learning and cooperative robotics.
- Challenge students to higher-level learning and higher order thinking.
- Complete Project and several simple homeworks with immediate and detailed feedback.

Software

Webots - https://cyberbotics.com/
FCPortugal CodeBase RoboCup - https://github.com/m-abr

keywords

Technological sciences > Engineering > Control engineering > Robótica Robotics
Physical sciences > Computer science > Cybernetics > Artificial intelligence

Evaluation Type

Distributed evaluation without final exam

Assessment Components

designation	Weight (%)
Participação presencial	10,00
Teste	40,00
Trabalho prático ou de projeto	50,00
Total:	100,00

Amount of time allocated to each course unit

designation	Time (hours)
Estudo autónomo	56,00
Frequência das aulas	56,00
Trabalho laboratorial	50,00
Total:	162,00

Eligibility for exams

Terms of frequency:
- Attendance and delivery of the assignment with more than 7.5 out of 20 grade and achieve 7.5 (in 20) in the exam.

Calculation formula of final grade

Evaluation:
- 10% HomeWorks/Class Participation
- 30% Mini-Test/Exam
- 10% Assignment/Project: Half Way Project Evaluation
- 50% Assignment/Project: Final Project Evaluation (presentation, code, demo and paper).

Examinations or Special Assignments

N/A

Internship work/project

N/A

Special assessment (TE, DA, ...)

Students with special circumstances should discuss and negotiate their situation with their teachers.

Classification improvement

The exam is improvable at the time of appeal. The practical part is not improvable.

Observations

Jury:
Luis Paulo Reis
Alípio Jorge
Álvaro Figueira