Lab Robot Experiment DAG Planning for Faster Accurate Pipetting
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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 constructs a computational representation of experiments using directed acyclic graphs (DAGs), optimizes these representations to minimize errors and operations, and translates them into robot instructions, providing a no- or low-code programming interface that works across multiple laboratory robot models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional programming methods are used for liquid-handling robots, then precise control of experimental operations can be achieved, but user time and cognitive overhead increase significantly
Solution Approach 1:
The patent introduces an intermediary computational representation layer (DAG) that sits between the user's high-level experimental intent and the robot's low-level execution commands. This mediator automatically handles the complex translation and optimization, preserving pipetting precision while eliminating manual programming time.
Solution Approach 2:
The system performs self-service by automatically generating, optimizing, and translating robot instructions without requiring user expertise in robot programming. The computational representation automatically optimizes experimental workflows and translates them into platform-specific commands, making the system self-sufficient.
2Reliability
If platform-specific programming is used for different robotic platforms, then precise control for each platform can be achieved, but system complexity and adaptability worsen
Solution Approach 1:
The patent creates a universal computational representation (DAG) that can represent experimental workflows independently of any specific robot platform. This single representation format can be translated to multiple different robotic platforms, providing universality while maintaining platform-specific reliability through accurate translation layers.
Solution Approach 2:
The computational representation acts as an intermediary that decouples the experimental logic from platform-specific commands. This mediator layer handles the complexity of platform differences, allowing reliable control across multiple platforms without increasing user-facing programming complexity.
3Manufacturing precision
If manual programming is used for experimental workflows, then detailed control over each operation can be achieved, but experimental throughput decreases
Solution Approach 1:
The system automatically optimizes experimental workflows by performing self-service tasks including generating computational representations, optimizing operation sequences, and translating to robot commands. This automation maintains procedural accuracy while dramatically increasing throughput by eliminating manual programming bottlenecks.
Solution Approach 2:
The computational representation performs preliminary action by pre-planning and optimizing the entire experimental workflow before execution. This advance planning ensures procedural accuracy is maintained while enabling faster execution through pre-optimized operation sequences.
4Measurement precision
If extensive programming knowledge is required for robot operation, then precise control and error minimization can be achieved, but ease of operation deteriorates
Solution Approach 1:
The system performs self-service by automatically handling all aspects of precise pipetting control and error minimization through its computational representation and optimization algorithms. Users don't need programming knowledge because the system autonomously manages precision control, making operation accessible to non-programmers.
Solution Approach 2:
The computational representation serves as an intermediary that translates user-friendly experimental specifications into precise robot commands. This mediator handles the complexity of precision control internally, allowing users to operate the system without needing to understand the underlying programming or precision control mechanisms.
Data Source
AI summary
The 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. The system including: a laboratory robot system (e.g., a liquid handling robot system), a deck, a user interface, and/or any other suitable components.