1.
| [1]
Understand the motivations and principles for building autonomous robotic system based on sensory perception, control principles, and AI algorithms; and how robotics relates to the broader field of artificial intelligence |
2.
| [2]
Formulate problems associated with domain specific data (e.g., obstacle avoidance and robotic arm manipulation) in terms of abstract models of robotics and AI algorithms |
3.
| [3]
Implement solutions to robotics problems using tools such as Matlab, apply numerical optimization and machine learning algorithms |
Mapping from Course Learning Outcomes to Programme Learning Outcomes
| PLO a | PLO b | PLO c | PLO d | PLO e | PLO f | PLO g | PLO h | PLO i | PLO j |
CLO 1 | T | | | | | | | | | T |
CLO 2 | T | | T | | | | | | | |
CLO 3 | T | | T | | | | | | | |
T - Teach, P - Practice
For BEng(CompSc) Programme Learning Outcomes, please refer to
here.
|
Syllabus |
Calendar Entry:
This course provides an introduction to mathematics and algorithms underneath state-of-the-art robotic systems. The majority of these techniques are heavily based on probabilistic reasoning and optimization – two areas with wide applicability in modern AI. We will also cover some basic knowledge about robotics, namely geometry, kinematics, dynamics, control of a robot, as well as the mathematical tools required to describe the spatial motion of a robot will be presented. In addition, we will cover perception, planning, and learning for a robotic system, with the obstacle avoidance and robotic arm manipulation as typical examples.
Note: The focus of the course is on mathematics and algorithms; we will not study mechanical or electrical design of robots.
|
Detailed Description:
Geometry and Mechanics |
Mapped to CLOs
|
Principles of robotics | 1 |
Transformation, robot state representation, configuration space | 1, 2 |
Kinematics, dynamics and basic control | 1, 2 |
Motion planning |
Mapped to CLOs
|
Search for a path | 2, 3 |
Trajectory optimization | 2, 3 |
Inverse kinematics | 2, 3 |
Perception |
Mapped to CLOs
|
Point clouds | 2, 3 |
Pose estimation | 2, 3 |
Multi-view geometry | 2, 3 |
Advanced topics and applications |
Mapped to CLOs
|
Multiple-robot system | 2, 3 |
Applications | 1, 2 |
|
Assessment:
Continuous Assessment:
50% Written Examination:
50%
|
Teaching Plan |
Please refer to the corresponding Moodle course.
|
Moodle Course(s) |
|