研究生全英文课程信息“智能机器人前沿(2017年) ”

智能机器人前沿课程围绕机器人感知、交互、规划、控制、执行等方面，每学期会选择不同的主题进行讲授，课程将面向学术前沿以及工程应用前沿，扩大学生的视野，激发学生对前沿问题的探索热情。

本学期由陈翔教授全英文讲授，主题为Optimization of Field Sensor Network. 请大家注意：除专业硕士外，所有有兴趣的硕士、博士研究生均可选课（把学号、姓名等信息发给张雪波老师zhangxuebo@nankai.edu.cn），下学期可以把1个学分在URP系统中补上。本学期课程具体信息如下：

1.  讲授语言：英文。

2.  课时：16课时。

3.  地点：计控楼327教室

4.  时间安排：

1)  06/14:  上午8:55—11:40，3课时。

2)  06/16:  上午8:55—11:40，3课时。

3)  06/19:  上午10:00—11:40,  2课时。

4)  06/20:  上午10:00—11:40,  3课时。

5)  06/21:  上午10:00—11:40,  3课时。

6)  06/22   上午10:00—11:40,  2课时。

Optimization of Field Sensor Network

Abstract: This 16-credit hours short course is a seminar-type research oriented course. It is intended for graduate students who are interested in field sensors and sensor network. The field sensors are broadly used in various autonomous operations such as robotics, autonomous vehicles, and UAVs, etc. The complexities in field sensor data capturing and structure normally require non-trivial post-sensing treatment and the operation efficiency to a great extent depends on the deployment of field sensors. Therefore, the deployment optimization of field sensors and sensor network is of significant interests to both the academic community and the relevant industries. This short-course will present research motivation, design initiatives and ideas, and preliminary results for the optimization of field sensor coverage. It is noted that this topic is attracting a lot of research interests and far from mature, hence, the course is intended to provide background knowledge to raise research interest instead of complete solution.

Main Contents:

1. Introduction to field sensor and sensor network

2. Fundamentals of Geometric images

3. Modeling visual coverage strength

4. Tensors and tensor model of sensor-target framework

5. Star-convex set and representation of field sensor coverage

6. Optimization of visual sensor network

7. Optimization of field sensor network

8. Open problems