Laboratory Robot Workflow Optimization for Automated Experiment Setup
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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 in minimizing pipetting errors and optimizing experimental workflows across different robotic platforms.
Innovation Solution
A method and system that use directed acyclic graphs (DAGs) to optimize experimental procedures, translating user inputs into robot instructions with minimal manual effort, and automatically optimizing experiment operations to reduce errors and increase efficiency across various laboratory robots.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual programming is used for liquid-handling robots, then flexibility and customization are improved, but time consumption and complexity increase
Solution Approach 1:
The patent uses computational representations (digital models) of experimental procedures to automatically generate robot instructions. Instead of manually programming each robot operation, the system creates a computational copy of the experimental workflow and translates it automatically, significantly reducing programming time while maintaining full experimental customization capability.
Solution Approach 2:
The system enables automated optimization of experimental workflows through computational representations that self-adjust and optimize parameters. The computational model automatically determines optimal experimental conditions and robot instructions without requiring manual intervention, allowing the system to serve itself in optimizing experiments while reducing user programming burden.
2Measurement precision
If complex programming is used to minimize pipetting errors, then measurement precision is improved, but ease of operation deteriorates
Solution Approach 1:
The patent replaces manual programming mechanics with automated computational optimization. Instead of requiring users to manually program complex pipetting sequences and error-minimization algorithms, the system uses computational representations to automatically generate optimized instructions, substituting the mechanical act of programming with automated computational processing that achieves the same precision goals.
Solution Approach 2:
The computational representation serves as an intermediary between the user's experimental intent and the robot's execution. This intermediate computational model automatically handles the complex optimization of pipetting parameters and error minimization, translating high-level experimental goals into precise robot instructions without requiring users to directly program the complex control logic.
3Adaptability or versatility
If platform-specific programming is used for different robotic platforms, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent creates a universal computational representation framework that can translate experimental workflows across multiple robotic platforms. Instead of requiring separate programming for each platform, the system uses a platform-agnostic computational model that automatically adapts to different robot types, enabling one programming approach to serve multiple robotic platforms and significantly reducing overall system complexity.
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 (e.g., a liquid handling robot) based on the optimized computational representation, and/or any other suitable steps.