Robotics, Control and Systems
2nd Cycle Studies - Mestrado
Data Structures and Algorithms; Probability and Statistics; Autonomous Robotic Systems
Lecture classes with detailed presentation of the topics of the syllabus using audiovisual means.
Laboratory classes in which students execute small projects on cognitive robotics algorithms, either in a ROS-based simulator or in real mobile robots.
Cognitive robots are autonomous robotic systems endowed with intelligent behaviors. These systems have to process sensory information in order to perceive the environment in which they operate and build representations of that environment and a knowledge base that allows them to reason, plan actions, make decisions, and eventually learn from the outcome of those actions in order to achieve goals that enable them to complete the missions for which they were designed. These perception and reasoning skills are achieved through the use of artificial intelligence techniques.
The main objective of this course is to provide the student with the fundamental knowledge to design, implement and test algorithms to deploy systems based on cognitive robots in real world applications.
1. Introduction to cognitive robotics
Robot Operating System (ROS), mobile robot simulators
2. Sensing and perception
Abstracting raw sensor data: feature extraction for semantic perception
Probabilistic estimation and tracking of static and dynamic features
3. Environment representation, localization and SLAM
Probabilistic maps: grid, topological and semantic mapping
Monte Carlo-based localization
Grid and graph-based SLAM algorithms
4. Planning, reasoning, and decision making under uncertainty
Sampling-based motion planning
Information-theoretic path planning and exploration
Introduction to reasoning using formal logic
Partially Observable Markov Decision Processes
5. Cooperative robots
Main features and challenges
6. Application case studies
Service robots: patrolling robots, assistive social robots
Field robots: search & rescue, demining
Rui Paulo Pinto da Rocha
Laboratory work or Field work: 50.0%
Alonzo Kelly. "Mobile robotics: mathematics, models, and methods", Cambridge University Press, 2014. ISBN: 978-1107031159
Sebastian Thrun, Wolfram Burgard, Dieter Fox, "Probabilistic Robotics", The MIT Press, 2005. ISBN: 978-0-262-20162-9
Howie Choset, Kevin M. Lynch, Seth Hutchinson, George A. Kantor, Wolfram Burgard, Lydia E. Kavraki, Sebastian Thrun, "Principles of Robot Motion Theory, Algorithms, and Implementations", The MIT Press, 2005. ISBN 978-0-262-03327-5
Steven M. LaValle. "Planning Algorithms", Cambridge University Press, 2006. ISBN: 978-0-521-86205-9
Stuart J. Russell, and Peter Norvig. “Artificial intelligence: a modern approach”, Fourth edition, Pearson, 2020. ISBN: 978-0134610993