Autonomous mobile systems

Higher education teachers: Klančar Gregor

Subject description

Prerequisits:

Finished 1. level of the Study programme, recommended from natural scientist field. This should cover the basics knowledge of:

Mathematics: geometric translations, vector operations, basics of probability (Bayes rule, probability density, functions, normal distribution), matrix operations, numerical methods for ordinary differential equations.
Dynamic linear systems: model presentations (state space, transfer function, differential equation), basics of closed loop control.
Basics of rigid body motion description
Programming experiences in Matlab and C/C++

Content (Syllabus outline):

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. Agents 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.

Objectives and competences:

• introduction to autonomous mobile systems,
• understanding agent concept in multi agent systems,
• methods for modeling, analysis and control of mobile systems,
• review of sensors and methods for information processing,
• presentation of navigation, localization and mapping (SLAM) problems,
• introduction to some programming environments to support the subject.

Intended learning outcomes:

• Knowledge and understanding: basic knowledge from autonomous mobile systems and multiagent systems
• Usage: obtained knowledge will be demonstrated on practical examples
• Reflexion: practical examples will illustrate theoretical knowledge’s
• Applicability: obtained knowledge is applicable to many fields, demonstration on didactic apparatus and programming environments

Learning and teaching methods:

• Lectures
• Laboratory practice

Study materials

• 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.