1
课程详述
COURSE SPECIFICATION
以下课程信息可能根据实际授课需要或在课程检讨之后产生变动。如对课程有任何疑问，请
联系授课教师。
The course information as follows may be subject to change, either during the session because of unforeseen
circumstances, or following review of the course at the end of the session. Queries about the course should be
directed to the course instructor.
1.
课程名称 Course Title
移动机器人导航与控制 Mobile Robot Navigation and Control
2.
授课院系
Originating Department
电子与电气工程系 Electronic and Electrical Engineering
3.
课程编号
Course Code
EE346
4.
课程学分 Credit Value
3
5.
课程类别
Course Type
专业选修课 Major Elective Courses
6.
授课学期
Semester
春季 Spring
7.
授课语言
Teaching Language
英文 English
8.
授课教师、所属学系、联系方
式（如属团队授课，请列明其
他授课教师）
Instructor(s), Affiliation&
Contact
（For team teaching, please list
allinstructors）
张宏、电子系、hzhang@sustech.edu.cn
9.
实验员/助教、所属学系、联系
方式
Tutor/TA(s), Contact
待公布 To be announced / 已确定的实验员/助教联系方式 Please list all Tutor/TA(s).
TBA
需要 TA
10.
选课人数限额(可不填)
Maximum Enrolment
（Optional）
20
2
11.
授课方式
Delivery Method
讲授
Lectures
习题/辅导/讨论
Tutorials
实验/实习
Lab/Practical
其它(请具体注明)
Other（Please specify）
总学时
Total
学时数
Credit Hours
32
32
64
12.
先修课程、其它学习要求
Pre-requisites or Other
Academic Requirements
MA212 概率论与数理统计；EE205 信号和系统
MA212 Probability and Statistics；EE205 Signals and Systems
13.
后续课程、其它学习规划
Courses for which this course
is a pre-requisite
无 None
14.
其它要求修读本课程的学系
Cross-listing Dept.
教学大纲及教学日历 SYLLABUS
15.
教学目标 Course Objectives
通过本课程，学生将从一套零部件开始，并以自动驾驶的小型模型自动驾驶机器人车辆完成。在此过程中，学生将使用传
统或最先进的方法，最新的软件工具和真实的硬件来建立和实验，从而获得动手学习的经验。该课程是车辆自动驾驶的实
用介绍。它探索了针对自动化理论挑战的现实解决方案，包括它们在算法中的实现，在仿真以及硬件中的部署。使用
Python，机器人操作系统（ROS）和 Docker 构建的现代软件架构，学生将尝试到经典架构和基于现代机器学习的方法的
互补优势。本入门课程将一部移动机器人从零到无人驾驶在汽车在模型版道路上安全行驶。
With this course, the students will start from a package of robot parts and finish with a scaled self-driving robot vehicle
that drives autonomously. In the process, the student will use established as well as experiment with state-of-the art
approaches, the latest software tools and real hardware in an engaging hands-on learning experience. The course is a
practical introduction to vehicle autonomy. It explores real-world solutions to the theoretical challenges of automation,
including their implementation in algorithms and their deployment in simulation as well as on hardware, using modern
robotics software development tools such as Python, ROS, and Docker.
16.
预达学习成果 Learning Outcomes
了解移动机器人基本零部件
掌握基本机器人编程工具，如 Python, ROS, OpenCV
学习相关计算机视觉算法，如滤波，线特征检测，物体检测，物体姿态估计
尝试 PID 类基本系统控制方法
培养团队工作能力
阅读机器人导航相关文章
- Learn the basic components of a mobile robot
- Acquire knowledge about the programming environment of a robot (Python, ROS, OpenCV)
- Establish an understanding of the basic algorithms in computer vision such as filtering, line detection, object
detection, perspective-n-point
3
- Practice with the PID control algorithm of a dynamic system (mobile robot)
- Experience working within a group and preparing lab reports and presentations
- Read scientific papers on the subject of autonomous robotics and self-driving vehicles
17.
课程内容及教学日历 （如授课语言以英文为主，则课程内容介绍可以用英文；如团队教学或模块教学，教学日历须注明
主讲人）
Course Contents (in Parts/Chapters/Sections/Weeks. Please notify name of instructor for course section(s), if
this is a team teaching or module course.)
Part 1: （8 学时）
Autonomous vehicles and Robotic systems System and autonomy architectures
Representations
Robot operating system (ROS)
Part 2: （8 学时）
Kinematics: modeling, calibration and system processing
PID control
Part 3: （22 学时）
Computer vision basics
Feature extraction
Line Detection
Place recognition
RANSAC
Camera calibration
Part 4:（10 学时）
Bayes filter
Particle filter
Kalman filter
Simultaneous localization and mapping (SLAM)
Part 5: （16 学时）
Robot path planning
AIDO: AI Driving Olympics
Learn-based autonomous navigation
18.
教材及其它参考资料 Textbook and Supplementary Readings
4
1. ROS Robot Programming: From the Basic Concept to Practical Programming and Robot Application, by YoonSeok
Pyo, HanCheol Cho, RyuWoon Jung and TaeHoon Lim.
免费下载，中文版/英文版，https://community.robotsource.org/t/download-the-ros-robot-programming-book-for-
free/51
2. Computer Vision: Algorithms and Applications, by Richard Szeliski,
免费下载, https://szeliski.org/Book/drafts/SzeliskiBook_20100903_draft.pdf
课程评估 ASSESSMENT
19.
评估形式
Type of
Assessment
评估时间
Time
占考试总成绩百分比
% of final
score
违纪处罚
Penalty
备注
Notes
出勤 Attendance
10%
课堂表现
Class
Performance
小测验
Quiz
课程项目 Projects
60%
项目部分包括单位任务的完成和期末
机器人比赛成绩，各占 30%左右
平时作业
Assignments
期中考试
Mid-Term Test
20%
期末考试
Final Exam
期末报告
Final
Presentation
10%
其它（可根据需要
改写以上评估方
式）
Others (The
above may be
modified as
necessary)
20.
记分方式 GRADING SYSTEM
A. 十三级等级制 Letter Grading
课程审批 REVIEW AND APPROVAL
21.
本课程设置已经过以下责任人/委员会审议通过
This Course has been approved by the following person or committee of authority
电子与电气工程系课程设置与培养方案委员会
