Introduction to Intelligent Robotics
Keywords |
Classification |
Keyword |
OFICIAL |
Computer Science |
Instance: 2022/2023 - 2S
Cycles of Study/Courses
Acronym |
No. of Students |
Study Plan |
Curricular Years |
Credits UCN |
Credits ECTS |
Contact hours |
Total Time |
L:IACD |
2 |
study plan from 2021/22 |
3 |
- |
6 |
56 |
162 |
Teaching language
Suitable for English-speaking students
Objectives
1. To understand the basic concepts of robotics, the context of artificial intelligence in robotics and robotics middleware with emphasis for ROS.
2. To study methods of perception and sensorial interpretation, which allow creating precise world estates and mobile robots’ localization and SLAM 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 study the fundamentals of human-robot interaction and cooperative robotics.
6. To analyze the main national and international robotics competitions, the more realistic robot simulators and the more advanced robotic platforms available in the market.
7. 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 and ROS.
- Knowledge of sensory perception and interpretation and SLAM.
- Knowledge of navigation.
- Knowledge and ability to apply computational learning algorithms for robots.
- Knowledge of human-robot interaction and cooperative robotics.
- 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; Robotics Middleware and ROS – Robot Operating System;
2. Perception and Action: Sensors; Robot Vision; Sensor interpretation; Locomotion and Action; Actuator Types.
3. Localization, Mapping and Navigation: Localization and Mapping Methods; SLAM – Simultaneous Localization and Mapping; Path Planning; Obstacle Avoidance; Navigation.
4. Robot Learning: Supervised, Evolutionary, Reinforcement and Deep Learning for Robotics.
5. Cooperative Robotics: Human-Robot Interaction; Multi-Robot Communication and Cooperation.
6. 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.
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.
Calculation formula of final grade
Evaluation:
- 10% HomeWorks/Class Participation
- 40% Mini-Test/Exam
- 10% Assignment/Project: Half Way Project Evaluation
- 40% Assignment/Project: Final Project Evaluation
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