 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | The design of robotic mechanisms is a core topic in robotics, and nurturing creativity is a key issue in producing human resources. Through training in machine creation, students’ creativity could be expanded. This course provides students with an explanation of the creative design of robotic mechanisms to educate them on how to create a robotic machine. The content includes an overview of the mechanisms of robot manipulators and mobile robots and an explanation of some robotic mechanisms as examples of creative design. The goal is to understand basic robot mechanisms and learn the method of how to create machines to realize the given functions. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6182) | 02-SEP-2024 - 06-DEC-2024 Mo 01:30PM - 04:20PM | Rm 222, W1 | MA, Shugen | 30 | 11 | 19 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course introduces microscopic robots and covers the fundamentals of their design, fabrication, and control for healthcare applications. Students will explore the interdisciplinary fields of engineering, materials science, medicine, and nanotechnology, and understand how these robots are set to revolutionize diagnostics, targeted therapy, and minimally invasive surgery. This course aims to equip students with the theoretical knowledge and practical insights necessary to contribute to this rapidly advancing field. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6184) | 02-SEP-2024 - 06-DEC-2024 We 09:00AM - 11:50AM | Rm 103, E1 | YASA, Immihan Ceren | 20 | 14 | 6 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Multiple view geometry techniques can provide robots with the ability to better perceive and reconstruct their surroundings, leading to improved navigation capabilities. This course aims to help students gain a deep understanding of the mathematical and computational methods used in multiple view geometry, including camera calibration, stereo vision, structure from motion, and 3D reconstruction. This knowledge is essential for developing algorithms and systems that can accurately interpret and manipulate visual data in mobile robot navigation and related applications. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6183) | 02-SEP-2024 - 06-DEC-2024 Mo 01:30PM - 04:20PM | Rm 102, E4 | LI, Haoang | 30 | 23 | 7 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| PREVIOUS CODE | ROAS 6000D |
| DESCRIPTION | This course introduces the essential fundamentals, including modeling, sensing, signal transmission and conversion, actuation, control, simulation, and implementation technologies used within the mechatronics design for robots and autonomous systems. It will give a holistic view of advanced automation technologies in industrial applications and provide the essential skills to design intelligent mechatronics systems. Through this course, students can enhance their understanding of the cross-disciplinary integration and systematic optimization of mechatronics systems involving the knowledge of mechanics, electronics, control engineering, and computer science. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6179) | 02-SEP-2024 - 06-DEC-2024 Tu 09:00AM - 11:50AM | Rm 202, W1 | ZHAO, Hang | 30 | 11 | 19 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | This course aims to provide an introduction to the fundamental knowledge of physical systems modeled with discrete state space and event driven transitions. Discrete Event Systems (DES) arise in the modeling of many engineering domains, such as automated manufacturing systems, communication networks, software systems, process control systems, and transportation systems. This course will introduce a unified modeling framework and emphasize the analysis and control of DES. Basics of automata and language theory are presented first as mathematical preliminaries. Then comes a detailed treatment of state estimation, diagnosis, security and supervisory control theory of DES based on automata model. Topics of other DES models like Petri nets, timed and hybrid automata are also covered towards the end of the course. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6177) | 02-SEP-2024 - 06-DEC-2024 Mo 09:00AM - 11:50AM | Rm 202, W1 | JI, Yiding | 20 | 7 | 13 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| PREVIOUS CODE | ROAS 6000B |
| DESCRIPTION | Light traveling in the 3D world interacts with the scene through intricate processes before being captured by a camera. These processes result in the dazzling effects like color and shading, complex surface and material appearance, different weathering, just to name a few. Physics based vision aims to invert the processes to recover the scene properties, such as shape, reflectance, light distribution, medium properties, etc., from the images by modelling and analyzing the imaging process to extract desired features or information. This course introduces the advanced methodologies in the context of physical-based vision for robotics and autonomous systems. We will introduce diverse techniques, covering from traditional methods based on hand-crafted features to recent deep learning methods. Apart from the fundamental knowledge in physical-based vision, the students will also have opportunities to discover and learn cutting-edge methodologies in popular physical-based vision topics (i.e., bad-weather restoration, shadow detection and removal, specular highlight detection and removal, intrinsic image decomposition, reflection detection and removal, and so on) of the physical-based vision, aligning with the substantial developments in robotics, autonomous driving, UAVs, etc. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6181) | 02-SEP-2024 - 06-DEC-2024 Mo 03:00PM - 05:50PM | Rm 201, W2 | ZHU, Lei | 30 | 20 | 10 | 0 |
| ATTRIBUTES | [BLD] Blended learning |
|---|---|
| VECTOR | [3-0-0:3] |
| EXCLUSION | INTR 5330 |
| CO-LIST WITH | INTR 5330 |
| DESCRIPTION | The course will cover a wide range of analytical methods used in human factors research domain. The students will gain an understanding of the procedures, objectives and limitations of different research methods. The course will also include four case studies so that students would gain first-hand experience in applying the methods in real projects. These contents are required for research investigating users’ behaviors. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6479) | 02-SEP-2024 - 06-DEC-2024 We 09:00AM - 11:50AM | TBA | HE, Dengbo | 6 | 4 | 2 | 0 | The classroom is E1202. |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Optimal control is an effective approach that has been widely used in robotics and autonomous systems. This course aims to provide students with a firm foundation in optimality principles in modern control systems design. Fundamental key concepts in optimal control are introduced, including Hamiltonian, Pontryagin's minimum principle, Bellman equation, dynamic programming, etc., which bring the prospect of formal linkage to reinforcement learning techniques. Different optimal control methods are also covered in this course, such as LQR, Kalman filter, LQG, MPC, etc. Additionally, the students will have the opportunity to discover and learn cutting-edge methodologies in the related field and develop the expertise in optimal control system design. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6178) | 02-SEP-2024 - 06-DEC-2024 Fr 09:00AM - 11:50AM | Rm 202, W4 | MA, Jun | 40 | 33 | 7 | 0 |
| VECTOR | [3-0-0:3] |
|---|---|
| DESCRIPTION | Any autonomous agent we develop must perceive and act in a 3D world. The ability to infer, model, and utilize 3D representations is therefore of central importance in AI, with applications ranging from robotic manipulation and self-driving to virtual reality and image manipulation. While 3D understanding has been a longstanding goal in computer vision, it has witnessed several impressive advances due to the rapid recent progress in (deep) learning techniques. The goal of this course is to explore this confluence of 3D Vision and Learning-based methods. In particular, this course will cover topics including - • Explicit, Implicit, and Neural 3D Representations • Differentiable Rendering • Single-view 3D Prediction: Objects, Scenes, and Humans • Neural Rendering • Multi-view 3D Inference: Radiance Fields, Multi-plane Images, Implicit Surfaces, etc. • Generative 3D Models • Shape Abstraction • Mesh and Point cloud processing |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| L01 (6180) | 02-SEP-2024 - 06-DEC-2024 Tu 03:00PM - 05:50PM | Rm 202, E1 | SONG, Jie | 20 | 17 | 3 | 0 |
| VECTOR | [0-1-0:0] |
|---|---|
| DESCRIPTION | Seminar topics presented by students, faculty and guest speakers. Students are expected to attend regularly and demonstrate proficiency in presentation in accordance with the program requirements. Graded P or F. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| T01 (6185) | 02-SEP-2024 - 06-DEC-2024 Mo 04:30PM - 05:20PM | Rm 147, E1 | MA, Shugen | 60 | 60 | 0 | 0 |
| VECTOR | [1-3 credit(s)] |
|---|---|
| DESCRIPTION | An independent study on selected topics carried out under the supervision of a faculty member. |
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6187) | TBA | TBA | TBA | 20 | 0 | 20 | 0 |
| DESCRIPTION | Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned. |
|---|
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6188) | TBA | TBA | TBA | 999 | 32 | 967 | 0 |
| DESCRIPTION | Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned. |
|---|
| Section | Date & Time | Room | Instructor | Quota | Enrol | Avail | Wait | Remarks |
|---|---|---|---|---|---|---|---|---|
| R01 (6189) | TBA | TBA | TBA | 999 | 24 | 975 | 0 |