Automation of tasks has been rapidly increasing in the last years. Autonomous systems have the ability to sense, learn and act independently, i.e. without any interference of human operators, even in unforeseen situations. They cover a wide range of areas such as robotics, process technology, smart factories and the omnipresent self-driving cars. The capabilities of autonomous systems build upon a variety of methods stemming from different engineering and scientific disciplines with computer and control sciences as the key ones.
This course gives an overview of technologies, methods and algorithms of autonomous systems. The students learn different concepts including perception, object tracking, SLAM, planning and control, which represent the core components of autonomous systems. Additionally, the students have the opportunity to apply the concepts in practical examples as part of code demos.
- Introduction to autonomous systems, perspectives and challenges.
- Sensing and Actuators: Image based sensors, range based sensors, working principles; actuator technologies
- Perception: Camera models; image processing; computer vision.
- Deep Neural Networks: CNN/GNN, RNN; supervised learning; object detection, image segmentation; scene understanding.
- Estimation: Bayesian inference; Kalman filtering; sensor fusion; multi-object tracking; localization; SLAM.
- Planning: Feasible planning, optimal planning, search algorithms, A*, Dijkstra's algorithm, forward / backward search, value iteration.
- Decision making and Control: MPC; imitation learning; reinforcement learning.
- Case studies: Sample codes and algorithms for self-driving vehicles and mobile robots in simulation and practice.
- Thrun, Burgard, Fox, ”Probabilistic Robotics”, MIT Press, 2005.
- Siegwart, Nourbakhsh, Scaramuzza, ”Introduction to Autonomous Mobile Robots”, MIT Press, 2011.
- Sutton and Barto, ”Reinforcement learning: An Introduction”, Second Edition, MIT Press, Cambridge, MA, 2018.
Written Exam: 120-150 Min.
Time: 11:30 – 14:00
Location: Building. 46-215/46-210/46-110
Credit Points: 4ECTS
Probability and Statistics
Fundamentals of robotics