WueCampus

Course summary

In dieser Vorlesung werden die technischen Grundlagen sowohl für Manipulatoren als auch für mobile Roboter behandelt. This lecture discusses the technical basics for manipulators as well as for mobile robots.

Course summary

The course aims to provide an introduction to Artificial Intelligence (AI) by covering the main topics ranging from search and logic, to decision-making and learning. The course is highly recommended to whomever is interested in the exciting topic of AI for the current studies, and possibly for a future job in research or industry. The course is organized in 15 lectures with the following topics: Lecture 01: Introduction to Artificial Intelligence; Lecture 02: Classical search; Lecture 03: Advanced search; Lecture 04: Propositional logic; Lecture 05: First-order logic and inference; Lecture 06: Planning; Lecture 07: Knowledge representation; Lecture 08: Uncertainty quantification; Lecture 09: Probabilistic reasoning; Lecture 10: Decision theory; Lecture 11: Sequential decision theory; Lecture 12: Multi-agent and game theory; Lecture 13: Learning - Basics; Lecture 14: Learning - Advanced; Lecture 15: Reinforcement learning; At the end of the course, the students will have acquired the required theoretical and empirical knowledge about classic and modern Artificial Intelligence (AI) approaches. They will have a good knowledge of the core problems and goals of AI, its current state-of-the-art, and its future challenges. They will be able to conduct their M.Sc. theses on topics related to AI, and they will have good starting knowledge of AI for a future career in research or industry. The evaluation consists of a written exam. The exam is in English and lasts 2 hours. There will be an exam at the end of the Winter Semester and one at the beginning of the Summer Semester. It will consist of open questions and/or quick application of algorithms by hand. The course is organized into 15 lectures and 15 exercise sessions. The lectures will provide both theoretical and practical perspectives on the respective topics. The exercise sessions will aim at strengthening the knowledge of the topics presented in the lectures through a quick refresh and guided exercises. Some of the exercise sessions will be used to host invited lectures given by experts about cutting-edge topics of AI, e.g., Robotics and Virtual Reality, explaining them more in detail compared to the lecture. Through the expert knowledge of the invited speakers, the invited lectures aim to provide a rich knowledge of the topics that will shape the future of AI, with a particular focus on research aspects and their impact on society. The main reference book for the course is: - Stuart, Russell, and Norvig Peter. "Artificial Intelligence A Modern Approach Third Edition." (2010). (https://people.engr.tamu.edu/guni/csce421/files/AI_Russell_Norvig.pdf). Additional references are: - Bishop, Christopher M., and Nasser M. Nasrabadi. Pattern recognition and machine learning. Vol. 4. No. 4. New York: springer, 2006. (https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf); - Koller, Daphne, and Nir Friedman. Probabilistic graphical models: principles and techniques. MIT press, 2009; - Peterson, Martin. An introduction to decision theory. Cambridge University Press, 2017; - Sutton, Richard S., and Andrew G. Barto. Reinforcement learning: An introduction. MIT press, 2018. (http://incompleteideas.net/book/the-book.html).

Course summary

Due to the technological advances in hardware and compute power nowadays the visual data is collected in huge amounts practically by everyone. The digital cameras are pervasive. Most of the smart devices include camera sensors and are capable of capturing stills and videos. With these large amounts of visual data there is a pressing need to automatically process, organize, interpret the data and refine the information in ways that humans are capable. Computer Vision is asked to provide such solutions. How to achieve such goals? What are the challenges? What are the solutions? What is state-of-the-art? What are the benefits? What is the societal impact? How can we further improve the state-of-the-art? In this seminar, we review, explore and debate such questions based on the recent research at the intersection of Machine Learning, Computer Vision, Robotics and Artificial Intelligence. Each participant will be assigned one topic from a range of computer vision topics, including but not limited to those listed below. Each participant is expected to prepare a written review report covering the state-of-the-art on the particular topic (compiled from at least two research papers) and a corresponding oral presentation. At the same time, each participant is expected to interact, read and comment on the reports provided and presented by the other participants. Each participant will get skills on critical analysis, scientific discourse, and preparation, writing, and presentation on a research topic. Moreover, the participants will get acquainted with state-of-the-art computer vision.

Course summary

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Course summary

Due to the technological advances in hardware and compute power nowadays the visual data is collected in huge amounts practically by everyone. The digital cameras are pervasive. Most of the smart devices include camera sensors and are capable of capturing stills and videos. With these large amounts of visual data there is a pressing need to automatically process, organize, interpret the data and refine the information in ways that humans are capable. Computer Vision is asked to provide such solutions. How to achieve such goals? What are the challenges? What are the solutions? What is state-of-the-art? What are the benefits? What is the societal impact? How can we further improve the state-of-the-art? In this seminar, we review, explore and debate such questions based on the recent research at the intersection of Machine Learning, Computer Vision, Robotics and Artificial Intelligence. Each participant will be assigned one topic from a range of computer vision topics, including but not limited to those listed below. Each participant is expected to prepare a written review report covering the state-of-the-art on the particular topic (compiled from at least two research papers) and a corresponding oral presentation. At the same time, each participant is expected to interact, read and comment on the reports provided and presented by the other participants. Each participant will get skills on critical analysis, scientific discourse, and preparation, writing, and presentation on a research topic. Moreover, the participants will get acquainted with state-of-the-art computer vision.

Course summary

In dieser Vorlesung werden die technischen Grundlagen sowohl für Manipulatoren als auch für mobile Roboter behandelt. This lecture discusses the technical basics for manipulators as well as for mobile robots.

Course summary

In dieser Vorlesung werden die technischen Grundlagen sowohl für Manipulatoren als auch für mobile Roboter behandelt. This lecture discusses the technical basics for manipulators as well as for mobile robots.

Course summary

In dieser Vorlesung werden die technischen Grundlagen sowohl für Manipulatoren als auch für mobile Roboter behandelt. This lecture discusses the technical basics for manipulators as well as for mobile robots.

Course summary

Beschreiben Sie kurz und prägnant, worum es in diesem Kurs geht.

Course summary

Abstract:

The idea of this course is to use modern teleoperation and make robotics more approchable. Experiments part of this course can be performed via internet and these include experiments in robot kinematics, navigation of remote rovers, path planning and sensor data acquisition and processing. The real robot used in the experiments is a four wheeled ackermann steered real wheel driven indoor mobile robot designed and built at our department specifically for remote experiments.

Gliederung:

1) Kinematics of a car-like mobile robot 

2) Navigation control of a car-like mobile robot 

3) Path planning of a car-like mobile robot 

4) Modelling of the forward and inverse kinematics of differential drive robot 

5) Sensor data acquisition and processing

Detaillierter Inhalt:

"Tele-Experiments with mobile robots" is an attempt to put basic robot theory and its implementation together to bring to students an interesting and practical course. Given that this tele-course is simultaneously used as part of regular on-site lectures, the course contents are kept up-to-date and always accessible. The experiements available here include a carefully selected mixture of real-world and simulation of robotic principles. Various topics in field robotics including kinematics, navigation principles, path planning, theoretical analysis and inverse kinematics, sensor data acquisition and processing are discussed and students are presented with challenging quizzes before beginning the experiments. Sensors are also chosen so that students get confusing results and are supposed to spend time thinking about the acquired sensor values and how to interpret those. Time delay concepts in robot teleoperation on variable bandwidth networks are also transparently presented to users as part of involuntary learning.