Courses tagged with "Computer Science" (11)
Robots are rapidly evolving from factory workhorses, which are physically bound to their work-cells, to increasingly complex machines capable of performing challenging tasks in our daily environment. The objective of this course is to provide the basic concepts and algorithms required to develop mobile robots that act autonomously in complex environments. The main emphasis is put on mobile robot locomotion and kinematics, environment perception, probabilistic map based localization and mapping, and motion planning. The lectures and exercises of this course introduce several types of robots such as wheeled robots, legged robots and drones.
This lecture closely follows the textbook Introduction to Autonomous Mobile Robots by Roland Siegwart, Illah Nourbakhsh, Davide Scaramuzza, The MIT Press, second edition 2011.
In recent years, flying robots such as miniature helicopters or quadrotors have received a large gain in popularity. Potential applications range from aerial filming over remote visual inspection of industrial sites to automatic 3D reconstruction of buildings. Navigating a quadrotor manually requires a skilled pilot and constant concentration. Therefore, there is a strong scientific interest to develop solutions that enable quadrotors to fly autonomously and without constant human supervision. This is a challenging research problem because the payload of a quadrotor is uttermost constrained and so both the quality of the onboard sensors and the available computing power is strongly limited.
In this course, we will introduce the basic concepts for autonomous navigation for quadrotors. The following topics will be covered:
- 3D geometry,
- probabilistic state estimation,
- visual odometry, SLAM, 3D mapping,
- linear control.
In particular, you will learn how to infer the position of the quadrotor from its sensor readings and how to navigate it along a trajectory.
The course consists of a series of weekly lecture videos that we be interleaved by interactive quizzes and hands-on programming tasks. For the flight experiments, we provide a browser-based quadrotor simulator which requires the students to write small code snippets in Python.
This course is intended for undergraduate and graduate students in computer science, electrical engineering or mechanical engineering. This course has been offered by TUM for the first time in summer term 2014 on EdX with more than 20.000 registered students of which 1400 passed examination. The MOOC is based on the previous TUM lecture “Visual Navigation for Flying Robots” which received the TUM TeachInf best lecture award in 2012 and 2013.
Do I need to buy a textbook?
No, all required materials will be provided within the courseware. However, if you are interested, we recommend the following additional materials:
- This course is based on the TUM lecture Visual Navigation for Flying Robots. The course website contains lecture videos (from last year), additional exercises and the full syllabus: http://vision.in.tum.de/teaching/ss2013/visnav2013
- Probabilistic Robotics. Sebastian Thrun, Wolfram Burgard and Dieter Fox. MIT Press, 2005.
- Computer Vision: Algorithms and Applications. Richard Szeliski. Springer, 2010.
Do I need to build/own a quadrotor?
No, we provide a web-based quadrotor simulator that will allow you to test your solutions in simulation. However, we took special care that the code you will be writing will be compatible with a real Parrot Ardrone quadrotor. So if you happen to have a Parrot Ardrone quadrotor, we encourage you to try out your solutions for real.
Modern computer technology requires an understanding of both hardware and software, as the interaction between the two offers a framework for mastering the fundamentals of computing. The purpose of this course is to cultivate an understanding of modern computing technology through an in-depth study of the interface between hardware and software. In this course, you will study the history of modern computing technology before learning about modern computer architecture and a number of its important features, including instruction sets, processor arithmetic and control, the Von Neumann architecture, pipelining, memory management, storage, and other input/output topics. The course will conclude with a look at the recent switch from sequential processing to parallel processing by looking at the parallel computing models and their programming implications.
Principles of Electric Circuits (20220214x) is one of the kernel courses in the broad EECS subjects. Almost all the required courses in EECS are based on the concepts learned in this course, so it’s the gateway to a qualified EECS engineer.
The main content of this course contains linear and nonlinear resistive circuits, time domain analysis of the dynamic circuits, and the steady state analysis of the dynamic circuits with sinusoidal excitations. Important concepts, e.g. filters, resonance, quiescent point, etc., cutting-edge elements, e.g. MOSFETs and Op Amps, etc., systematic analyzing tools, e.g. node method and phasor method, etc., and real-world engineering applications, e.g. square wave generator and pulse power supply for railgun, etc., will be discussed in depth.
In order to facilitate the learning for students with middle school level, we prepare the necessary knowledge for calculus and linear algebra in week 0. With your effort, we can show you the fantastic view of electricity.
电路原理课程是电类各专业最重要的一门学科基础课，后续各专业基础课和专业课都建立在这门课程的知识体系之上，因此是电类专业本科生的“看家 课”之一。电路原理课程的主要内容包括：线性电阻电路分析、非线性电阻电路分析、动态电路的时域分析和正弦激励下动态电路的稳态分析4大部分。清华大学电 路原理课程的教学包括电路分析基本方法、当代电路元器件、电路原理的实际工程应用等，为学生提供了扎实的基础和丰富的应用。