课程介绍： 麻省理工学院的深度学习方法的导论课程，应用到计算机视觉，自然语言处理，生物学，和更多! 学生将获得深度学习算法的基础知识和在TensorFlow中构建神经网络的实践经验。先修习微积分(即求导数)和线性代数(即矩阵乘法)，我们将在学习过程中尝试解释其它内容! Python方面的经验是有帮助的，但不是必需的。欢迎听众!
第一讲： 深度学习导论 Introduction to Deep Learning，101页ppt
In the project, the vehicle was first controlled with ROS. For this purpose, the necessary nodes were prepared to be controlled with a joystick. Afterwards, DWA(Dynamic Window Approach), TEB(Timed-Elastic Band) and APF(Artificial Potential Field) path planning algorithms were applied to MIT RACECAR, respectively. These algorithms have advantages and disadvantages against each other on different issues. For this reason, a scenario was created to compare algorithms. On a curved double lane road created according to this scenario, MIT RACECAR has to follow the lanes and when it encounters an obstacle, it has to change lanes without leaving the road and pass without hitting the obstacle. In addition, an image processing algorithm was developed to obtain the position information of the lanes needed to implement this scenario. This algorithm detects the target point by processing the image taken from the ZED camera and gives the target point information to the path planning algorithm. After the necessary tools were created, the algorithms were tested against the scenario. In these tests, measurements such as how many obstacles the algorithm successfully passed, how simple routes it chose, and computational costs they have. According to these results, although it was not the algorithm that successfully passed the most obstacles, APF was chosen due to its low processing load and simple working logic. It was believed that with its uncomplicated structure, APF would also provide advantages in the future stages of the project.