1. SylabUZ
2. Wydział Nauk Inżynieryjno-Technicznych
3. semestr zimowy 2023/2024
4. Automatyka i robotyka - Program Erasmus drugiego stopnia
5. Robot localization and navigation

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Robot localization and navigation - opis przedmiotu

Cel przedmiotu

• To provide fundamental skills within the framework of formulation and implementation of localization and path planning for mobile robots.
• To provide knowledge on methods of mobile platforms navigation.
• To learn skills of robotic systems integration.

Wymagania wstępne

Zapisz zmiany

Fundamentals of robotics, Robot control.

Zakres tematyczny

Introduction. Typical mobile robot platforms. Legs and wheels as the movement mechanisms.  Essential problems. Examples and applications. 

Robot perception. Sensor classification. Characterization of sensor performance and uncertainty of measurements. Feature extraction. Perception algorithms. Vision algorithms. Models of workspace (raster, geometric, topological).

Kinematics of mobile robots. Kinematic models and constraints. Controllability of robot. Workspace and motion control. Kinematics of actuators (camera, laser rangefinders, manipulators, etc.).

Localization of mobile robot.  Classification of methods. Challenges in localization. Odometry. Localization based on maps. Probabilistic methods. Kalman filtering In localization. Systems based on environmental marks and global positioning systems. Autonomous map building.

Navigation. Trajectory planning. Classification of motion planning methods. Fundamental techniques of motion planning (visibility graphs, workspace decomposition, Bayesian methods, potential methods etc.).  Obstacles avoidance. Movement optimization.

Mobile robot networks. Models of robotnic networks. Centralized and multiagent systems. Methods of motion planning for swarms of robots. Coordination of tasks. Problems of connectivity, randez-vous and optimal robot deployment.

Metody kształcenia

Lecture, Laboratory exercises.

Efekty uczenia się i metody weryfikacji osiągania efektów uczenia się

Opis efektu	Symbole efektów	Metody weryfikacji	Forma zajęć

Warunki zaliczenia

Lecture – the main condition to get a pass is a positive assessment of written or/and oral examination test

Laboratory – the main condition to get a pass is a sufficient number of positive evaluations of written or oral tests conducted at least three times per semester and positive evaluations of the laboratory tasks assigned by the lecturer.

 

Calculation of the final grade: lecture 50% + laboratory 50%

Literatura podstawowa

1. Siegwart R., Nourbakhsh I.R.: Introduction to Autonomous Mobile Robots. MIT Press, 2010
2. 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, A bradford book, 2005
3. Corke P., Robot Vision Control, Springer Business Media, 2011
4. Steven LaValle, Planning algorithms, Cambridge University Press, 2006
5. V.J. Lumelsky.: Sensing, Intelligence, Motion., Wiley, 2006 
6. Murphy R.: Introduction to AI Robotics, MIT Press, 2000.

Literatura uzupełniająca

Uwagi

Zmodyfikowane przez dr hab. inż. Wojciech Paszke, prof. UZ (ostatnia modyfikacja: 13-06-2023 12:08)