🦾 Autonomous Systems for Self-driving Labs
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
🔑 Recommended Prerequisites
The recommended prerequisite for this course is Introduction to AI for Discovery using Self-driving Labs
🎯 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 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 |
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Controlling pumps and pipettes |
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Serial communication |
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Automated liquid handlers |
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Mobile robotics |
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Computer vision |
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Solid sample transfer |
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⚖️ 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 six subsequent 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)