Autonomous Mobile Machines

11M1015

Master

German

5.0

irregular

1 semester

Autonomous mobile systems have already become an integral part of industry. Autonomous intralogistics systems are state of the art, but agricultural robotics and autonomous/highly automated driving are also topics that have arrived in the industry. In this course, students are taught the necessary software techniques to be able to work in these future fields. This includes an introduction to the Robot Operating System (ROS), as well as the environmental perception and navigation of autonomous systems.

1. introduction Robot Operating System (ROS)
2. sensors & actuators of autonomous mobile systems
3. navigation (localization, path planning, obstacle avoidance)
4. environment perception (object recognition, mapping)
5. simulation environments for the development of algorithms for navigation and environment perception

The total workload for the module is 150 hours (see also "ECTS credit points and grading").

• Portfolio exam or
• Written examination or
• Project Report, written

• Field work / Experimental work

Portfolio examination: Term paper 60 points + presentation 40 points

Graded examination:

• Term paper as part of the portfolio examination: approx. 10-15 pages, the number of pages varies depending on the topic and group size.
• Presentation as part of the portfolio examination: approx. 20 minutes

Ungraded examination:

• Experimental work: Experiment: approx. 4 experiments in total

Basic programming skills

Students who have successfully completed this course are familiar with the problems that autonomous mobile systems have to solve and are familiar with various solution strategies. They can classify these and apply them to specific problems.

Students acquire in-depth knowledge in the field of navigation of autonomous systems and semantic environment perception. They demonstrate this knowledge in the preparation of a term paper.

The course provides an introduction to the Robot Operating System and the Gazebo simulation environment. Students are able to use these tools to implement the navigation and environmental perception of robots. Students who have successfully completed this module are able to systematically plan and implement a concept for experimental work and a project in a small team, present it to a larger group of students and answer critical questions. Students are able to understand autonomous mobile machines as systems (e.g. the relationship between sensor configuration and control algorithm). Autonomous mobile machines have strong interdisciplinary links to mechatronics, computer science and electronics - systems thinking is therefore firmly anchored in the subject.

Students are able to adapt the Robot Operating System to different robot hardware and place it in the context of an application question.

The module should be carried out in close cooperation with the ongoing research projects at the Agro-Technicum, so that research questions from the projects are included in the module. Students develop their software on the basis of these questions as part of their term paper and can relate the results to the research question.

Students will be able to reflect on the use of agricultural robots and AI to support supply processes against the background of ethical, legal and economic framework conditions.

• Stiene, Stefan

• Stiene, Stefan
• Schöning, Julius