CSC477/CSC2630: Introduction to Mobile Robotics, Fall 2022

Overview

This cross-listed course (undergraduate version CSC477H, graduate version CSC2630H) provides an introduction to robotic systems from a computational perspective. A robot is regarded as an intelligent computer that can use sensors and act on the world. We will consider the definitional problems in robotics and look at how they are being solved in practice and by the research community. The emphasis is on algorithms, probabilistic reasoning, optimization, inference mechanisms, and behavior strategies, as opposed to electromechanical systems design. This course aims to help students improve their probabilistic modeling skills and instill the idea that a robot that explicitly accounts for its uncertainty works better than a robot that does not.

Cross-Campus Course Delivery

Lectures for this course will be delivered in person by the instructor at the UTM campus on Wednesdays 3-5pm ET, in MN3190. Lectures will also be livestreamed on Zoom for those joining remotely, and projected in the Myhal Building in the St George campus, room MY580, for students who want to join in person from the St George campus. There will be a videconferencing setup in MY580 and MN3190, allowing all students to ask questions live. Lectures will be recorded and posted online a few hours after the recording.

Tutorials for this course will be delivered in person by one of the TAs at the UTM campus on Wednesdays 5-6pm ET, in DH2020. Tutorials will also be livestreamed on Zoom for those joining remotely, and projected in the Myhal Building in the St George campus, room MY580, for students who want to join in person from the St George campus. There will be a videconferencing setup in MY580 and DH2020, allowing all students to ask questions live. Tutorials will be recorded and posted online a few hours after the recording.

Office hours for this course will be delivered on Zoom. In person office hours can be arranged by appointment.

Course Description

This course will broadly cover the following areas:

• Kinematics and Dynamics: how can we model robotic systems using approximate physical models that enable us to make predictions about how robots move in response to given commands?
• Feedback Control and Planning: how can we compute the commands that can bring a robotic system from its current state to a desired state?
• Mapping: how can we combine noisy measurements from sensors with the robot’s pose to build a map of the environment?
• State Estimation: the state of the robot is not always directly measurable/observable. How can we determine the relative weighs of multiple sensor measurements in order to form an accurate estimate of the (hidden) state?
• Geometry of Computer Vision: how can modeling pixel projections on an RGB camera help us infer the 3D structure of the world? How can we triangulate points seen from two cameras? How can we estimate the camera’s pose (and therefore the robot’s) while it is moving in the environment?