Course description
Overview of autonomous mobile systems and definition of the agent concept. Categorization of such systems regarding their properties such as: autonomy, mobility, different agent performance, systems structures, driving mechanism, goals, sensing and interactions with environment and areas of applicability. Agent’s architecture and some examples of construction.
Multi-Agent Systems (MAS) as a subfield of artificial intelligence, introduction of principles for complex systems construction using agents as basic entities. Possible areas of applications, classification based on different properties and capabilities and properties and disadvantages of such system usage.
Modeling of kinematic, motion constraints and dynamic properties of mobile systems. Demonstration on practical examples of mobile systems.
Different approaches for control of mobile systems, motion planning and obstacle avoidance. Control to desired position, orientation, pose, following desired path or trajectory. Motion planning methods, optimal path search in known environment.
Sensors used in mobile robotics systems, their principles of operation and usage. Sensors fusion methods such as Kalman filter, particle filter and the like.
- Navigation, mapping of unknown environment, localization using sensor information and environment map, simultaneous localization and mapping (SLAM). Different approaches demonstration using clear examples.
Course is carried out on study programme
2nd Cycle Postgraduate Study Programme in Electrical Engineering
Objectives and competences
The objective of the course is to introduce autonomous mobile systems and to give students basic knowledge required to develop autonomous mobile robots. This course includes areas of mobile robotics with illustrative examples and algorithms for their design.
This course include basic theoretic knowledge of methods for modeling, analysis and control of mobile systems, path planning, use of sensors, methods for information processing in localization and environment mapping and multi-agent systems. For a more comprehensive understanding of the field, a number of challenges and problems that we encounter in practice are described.
Within laboratory work while designing mobile platforms student can examine and consolidates the obtained theoretical knowledge. Student also gets familiar with some of the programming environments and their applicability to the subject.
Learning and teaching methods
The lectures provide a theoretical background on particular subjects together with presentation of simple practical examples. A complete study material is available to the students.
Practical work is being performed in the laboratory environment, and is accomplished in steps acquainting students with the problem and instrumentation. Project group is consisted of two students who program their own algorithms for control of mobile robot platform, environment detection, localization in environment, motion path planning, obstacle avoidance, and reasoning system that enables robot autonomy at performing given tasks (e.g. autonomous delivery, driving in traffic, environment mapping, etc.)
At the end of semester, students report on their results together with comparison to the results from the literature.
Intended learning outcomes
After successful completion of the course, students should be able to:
- understand and use basic knowledge from the autonomous mobile system area,
- derive kinematic model of a mobile robot platform and use it for development of control, path planning and localisation,
- use sensors and optimally fuse information obtained from more sources,
- develop filters for localisation in environment,
- write a control algorithm for desired path following, which can safely find the way to the goal at present disturbances,
- develop path planning algorithms which can automatically find safe path in a given map,
- implementation of the reasoning system for mobile platform,
- explain the mechanisms of multiagent systems operation,
- have the overview of possible applications of autonomous systems.
Reference nosilca
- KLANČAR, Gregor, TESLIĆ, Luka, ŠKRJANC, Igor. Mobile-robot pose estimation and environment mapping using an extended Kalman filter. International Journal of Systems Science, vol. 45, no. 12, str. 2603-2618, 2014.
- KLANČAR, Gregor, BLAŽIČ, Sašo, MATKO, Drago, MUŠIČ, Gašper. Image-based attitude control of a remote sensing satellite. Journal of intelligent & robotic systems, ISSN 0921-0296, vol. 66, no. 3, str. 343-357, 201
- KLANČAR, Gregor, MATKO, Drago, BLAŽIČ, Sašo: A Control Strategy for Platoons of Differential-Drive Wheeled Mobile Robot. Robotics and Autonomous Systems, vol. 59, no. 2, str. 57-64, 2011.
- KLANČAR, Gregor, ZDEŠAR, Andrej, BLAŽIČ, Sašo, ŠKRJANC, Igor: Wheeled mobile robotics : from fundamentals towards autonomous systems, Elsevier: Butterworth-Heinemann, Cambridge, 2017.
- KLANČAR, Gregor, ŠKRJANC, Igor. Tracking-error model-based predictive control for mobile robots in real time. Robotics and Autonomous Systems, vol. 55, no. 6, str. 460-469, 2007.
Study materials
- Gregor Klančar, Andrej Zdešar, Sašo Blažič, Igor Škrjanc: Wheeled mobile robotics : from fundamentals towards autonomous systems, Elsevier: Butterworth-Heinemann, Cambridge, 2017.
- Gregory Dudek, Michael Jenkin: Computational Principles of Mobile Robotics, Cambridge University Press, New York, 2010.
- 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 (Intelligent Robotics and Autonomous Agents series), MIT Press, Cambridge, 2005.
- Sebastian Thrun, Wolfram Burgard, Dieter Fox: Probabilistic Robotics (Intelligent Robotics and Autonomous Agents series), MIT Press, Cambridge, 2006.
- Michael Wooldridge: An Introduction to MultiAgent Systems, Second Edition, John Wiley & Sons, Chichester, England, 2009.