🦾 Robotics: Course Overview

Note

Registration is live! Course quizzes and grades are available at https://q.utoronto.ca/.

Embark on a journey into the world of robotics and automation for self-driving laboratories. This asynchronous, remote course equips you with the skills to control peristaltic pumps, linear actuators, automated liquid handlers, and solid dispensers using a microcontroller, a motor driver, and a workflow orchestration package. You’ll also learn to control mobile cobots and perform spatial referencing and ID recognition via computer vision. The course will conclude with a solid sample transfer workflow using a multi-axis robot. Remotely accessible resources will be provided as necessary.

Self-driving lab robotic platforms. 1. ADA at the University of British Columbia (C. Berlinguette, J. Hein, A. Aspuru-Guzik); 2. Artificial Chemist (M. Abolhasani, NC State University); 3. Robotically reconfigurable flow chemistry platform (C. Coley, MIT); 4. Chemputer (L. Cronin, University of Glasgow); 5. Mobile robot chemist (A. Cooper, University of Liverpool). Source: https://acceleration.utoronto.ca/maps

🔑 Prerequisites

The recommended prerequisite for this course is Introduction to AI for Discovery using Self-driving Labs (Course 1: Hello World)

🎯 Learning Outcomes

• Design and execute software to manage a peristaltic pump’s operations using a microcontroller and motor driver, demonstrating application and integration skills

• Construct the “Digital Pipette” and develop software to manipulate the linear actuator, showcasing capabilities in hardware assembly and software programming

• Operate an automated liquid handler, such as Science Jubilee or Opentrons, to accurately transfer liquid between vials, demonstrating proficiency in laboratory automation techniques

• Exhibit the ability to control a mobile collaborative robot (cobot) using the ROS framework, reflecting advanced understanding and operational skills in robotics

• Showcase the use of OpenCV and AprilTags for spatial referencing and ID lookup, illustrating advanced skills in computer vision and object identification

• Configure and utilize ROS, AprilTags, and a multi-axis robot to execute solid sample transfers, demonstrating integrated skills in robotics programming and operation

🛠️ Competencies/Skills

• Motor drivers

• Serial communication

• Automated liquid handlers

• Robotic control

• Robotic simulation

• Computer vision

• Automated solid handlers

🧩 Modules

Each module is intended to take approximately 3-4 hours, assuming that the recommended prerequisites have been met.

Module Name	Topics	Learning Outcomes
3.0 Orientation	Git GitHub Version control GitHub Classroom Codespaces	Describe the purpose of Git and GitHub Create a GitHub account and a repository Commit, push, and pull changes Run a unit test and fix a simple Python function Define continuous integration
3.1 Pumps and Pipettes	Workflow orchestration Microcontrollers Peristaltic pumps Linear actuators Motor drivers	Implement software to control a peristaltic pump via a microcontroller and a motor driver Build the “Digital Pipette” and implement software to control the linear actuator
3.2 Serial communication	Mass balance Serial communication Reading data	Design and execute software to read data from a mass balance using serial communication
3.3 Liquid Handlers	Workflow orchestration Jubilee Opentrons	Perform liquid transfer between vials with an automated liquid handler (Science Jubilee and OT-2)
3.4 Mobile robotics	ROS Isaac Sim Asynchronous task execution Prefect	Demonstrate control of a mobile cobot using frameworks such as the Robot Operating System (ROS) and Isaac Sim Use a workflow orchestration package to manage asynchronous tasks Define asynchrony in the context of hardware control for automonous laboratories
3.5 Computer vision	OpenCV AprilTags	Demonstrate spatial referencing and ID lookup by using OpenCV and AprilTags Use a motorized microscope and OpenCV to search for regions of interest in a sample
3.6 Solid sample transfer	Workflow orchestration ROS AprilTags Multi-axis robotics	Use ROS, AprilTags, a multi-axis robot, and a workflow orchestration platform to perform solid sample transfer

⚖️ Course Assessments and Grading Schema

Each student is required to complete various quizzes and GitHub Classroom assignments. The course is structured into an orientation module followed by several modules. The course is graded on a pass/fail basis with 70% as the threshold for passing. Here is the breakdown of the points for each part of the course:

• 🧭 Orientation Module: Worth 15 points.
• 📚 Modules 1-6: Each includes:
  • 🧭 A guided notebook tutorial (ungraded)
  • 📓 A knowledge check (graded, 5 points)
  • 🛠️ A GitHub Classroom assignment (graded, 10 points*)

*The final module's GitHub Classroom assignment is worth 30 points.

Note that partial points are available on certain assignments.

👤 Course developer(s)

• Sterling Baird, PhD Materials Science and Engineering (Acceleration Consortium)