Automated Experiment Planning for Cross-Platform Lab Robots
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Solution Overview
Problem
Programming liquid-handling robots for experiments is time-consuming and requires in-depth knowledge, and existing technologies face challenges with pipetting errors and manual input requirements, limiting automated experiment adoption and efficiency across different robotic platforms.
Innovation Solution
A method and system that use directed acyclic graphs (DAGs) to optimize experimental procedures, reducing user input and translating experiment specifications into robot control motions across various laboratory robot models, minimizing pipetting errors, and automating experiment setup and execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual programming methods are used for liquid-handling robots, then experiment setup can be completed, but user time and cognitive overhead increase significantly
Solution Approach 1:
The system performs automated experiment design and protocol generation without requiring manual programming. The automated experiment design server receives high-level parameters, automatically generates detailed protocols, and translates them into robot-executable code, making the system self-sufficient in the programming task.
Solution Approach 2:
An automated experiment design server acts as an intermediary between the user and the robot. The user provides simple parameters to the server, which then handles the complex protocol generation and code translation, mediating the interaction and eliminating the need for direct manual programming.
2Reliability
If existing automation technologies are used, then some experiments can be automated, but pipetting errors persist and limit accuracy
Solution Approach 1:
The system dynamically adjusts pipetting parameters such as aspiration and dispensing volumes, speeds, and pressures based on the specific experiment protocol requirements. This parameter optimization minimizes pipetting errors while maintaining automation capability.
3Adaptability or versatility
If platform-specific programming is used for different robotic platforms, then each robot can be controlled, but device complexity and knowledge requirements increase
Solution Approach 1:
The system provides a universal programming interface that works across multiple robotic platforms. The automated experiment design server generates platform-agnostic protocols that can be executed on different robot models, eliminating the need for platform-specific programming knowledge.
4Ease of operation
If manual experiment setup is performed, then flexibility is maintained, but experimental throughput decreases
Solution Approach 1:
The system performs preliminary automated setup including protocol generation, parameter optimization, and code translation before experiment execution. This preliminary automation maintains flexibility in experiment design while enabling high-speed automated execution, thereby increasing throughput.
Data Source
AI summary
In variants, a method for automated experimentation can include: determining experimental constraints, constructing a computational representation of the experiment, optimizing the computational representation subject to the experimental constraints, determining instructions for a laboratory robot based on the optimized computational representation, and/or any other suitable steps.