Autonomous Mobile Systems

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.

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.

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.