极其牛逼的德国慕尼黑工业大学即将在edx平台上推出一门综合了计算机AI、电子工程、航空等多学科的慕课，Autonomous Navigation for Flying Robots，即“飞行机器人的自动导航技术”，这种飞行机器人主要是指Quadrotors(四旋翼飞行器)，就是我们平常所见的四旋翼直升机，这种飞行器飞行稳定、易于操控，目前已经有很广泛成熟的商业应用了，比如Amazon前段时间研发无人机快递所用的那种飞行器，以及《舌尖上中国2》里各种无人机航拍摄影，想必在未来一定有更大的用武之地。
这门课将介绍四旋翼直升机自动导航的基本概念，包括概率状态估计，线性控制和轨迹规划，用Python语言来控制飞行器的飞行，是面向计算机、电子工程和机械专业的研究生课程。开课日期是5月6日，共8周，每周4学时，喜欢钻研这种新技术的同学速去报名啊
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课程简介
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 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 including topics such as probabilistic state estimation, linear control, and path planning. You will learn how to infer the position of the quadrotor from its sensor readings, how to navigate along a series of waypoints, and how to plan collision free trajectories. The course consists of a series of weekly lecture videos that we be interleaved by interactive quizzes and hands-on programming tasks. The programming exercises will require you to write small code snippets in Python to make a quadrotor fly in simulation.
This course is intended for graduate students in computer science, electrical engineering or mechanical engineering. The course is based on the TUM lecture “Visual Navigation for Flying Robots” which received the TUM TeachInf best lecture award in 2012 and 2013. The course website from last year (including lecture videos and course syllabus) can be found here: http://vision.in.tum.de/teaching/ss2013/visnav2013
注：本课程基于获得慕尼黑工业大学最佳课程奖的“飞行机器人的视觉导航技术”课程，该课视频和课件可点击这里
http://vision.in.tum.de/teaching/ss2013/visnav2013
