Course Overview

Course Title:Sustainable Ocean Intelligent Autonomous Monitoring

Relevant SDGs:Goal 14: Conserve and sustainably use the oceans, seas and marine resources

Credit(s): 2credits

Course Description:

This course focuses on the theme of "protection and sustainable utilization of oceans and marine resources to promote sustainable development". The course adopts a combination of theory and practice to introduce related technologies and typical applications of ocean intelligent autonomous monitoring. Typically, the course includes unmanned surface vehicle(USV),unmanned aerial vehicle(UAV), autonomous underwater vehicle(AUV), and related algorithms for data processing. After successfully completing this course, students are able to:

• have a comprehensive and preliminary understanding of the field of sustainable ocean intelligence autonomous monitoring.
• understand and master the overall architecture and key technologies of the three important autonomous systems of USV, UAV, and AUV.
• implement basic ocean intelligent autonomous monitoring system with programming software.

What skills will students get?

1. Understand the meaning of autonomous monitoring of ocean intelligence, explain the key technologies of autonomous systems such as unmanned surface vehicle(USV),unmanned aerial vehicle(UAV), autonomous underwater vehicle(AUV).
2. Exploit unmanned system technology to analyze and solve practical problems of sustainable ocean intelligent autonomous monitoring.
3. Understand the basic algorithms of intelligent autonomous system.

Mode of Teaching

Lectures & Discussion & Exercises & Project demos

Grading

Lectures 24h, exercise sessions 8 h, independent work 30 h. Students are awarded 2cr for completing the course.

1. Attendance:30%
2. Group presentation: 70%

Course-specific Restrictions

Students from all study programs are welcome, and thus no formal requirements are set. Students with no background in engineering are encouraged to glance through, e.g., knowledge of signals and systems, estimation theory, and the excellent material of Elements of AI.

Class Schedule

Instructors

Rui Gao

Rui Gao received the Doctor of Science (Tech.) degree in automation, systems and control engineering from Aalto University, Finland, in 2020. She was a Postdoctoral Researcher with the Department of Electrical Engineering and Automation, Aalto University in 2021. Since 2022, she is Assistant Professor at the School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University. Her research interests include autonomous systems, state estimation, and convex optimization.

Jian Wang

Jian Wang is Assistant Research Fellow at the School of Naval Architecture Ocean and Civil Engineering, Shanghai Jiao Tong University. He has published 15 related papers, applied for 4 invention patents and obtained 8 software Copyrights, and presided over a number of topics related to navigation control of USVs and cooperative control of USV formation. His research results "Test and verification technology and application of ship intelligent control System" won the first prize of Scientific and Technological Progress of the Chinese Society of Naval Architecture Engineering in 2021. His research interests include the design of Unmanned Surface Vehicle (USV) platform and the cooperative control of marine unmanned multi-agents.

Alex Jung

Alex Jung received Ph. D degree (with “sub auspiciis”) from TU Vienna in 2012. Since 2015, he is Assistant Professor for Machine Learning at the Department of Computer Science of Aalto University. He received an Amazon Web Services Machine Learning Award and has been chosen as the CS Teacher of the Year in 2018. His textbook “Machine Learning: The Basics” has been published by Springer in 2022. Alex serves as an Associate Editor for IEEE Signal Processing Letters, Editorial Board Member for “Machine Learning” (Springer) and Chair for the IEEE Finland Jt. Chapter SP-CAS.

Filip Tronarp

Filip Tronarp received his Master of Science in Engineering Mathematics from Lund University in 2016, and his Doctor of Science (Tech.) from Aalto University in 2020. Since 2020 he is a postdoctoral researcher with the Department of Computer Science at University of Tübingen. His reseach interest are modelling and inference in stochastic systems.

Roland Siegwart

Roland Siegwart is full Professor of Autonomous Systems at ETH Zurich since July 2006 and Founding Co-Director of the Wyss Zurich. From January 2010 to December 2014, he took office as Vice President Research and Corporate Relations in the ETH Executive Board. He is member of the board of directors of various companies, including Komax and NZZ. Roland Siegwart's research interests are in the design and control of robot systems operating in complex and highly dynamical environments. His major goal is to find new ways to deal with uncertainties and enable the design of highly interactive and adaptive autonomous robots.

Course Contact

GAO Rui:rgao@sjtu.edu.cn