Automated Experiment Planning for Liquid-Handling Robot Programming
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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 throughput.
Innovation Solution
A method and system that use directed acyclic graphs (DAGs) to optimize experimental procedures, reducing user input and programming time by automatically translating experiment specifications into robot control motions across multiple robot models, minimizing pipetting errors, and optimizing dilution sequences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual programming and setup of liquid-handling robots is used, then experimental flexibility and customization are achieved, but user time and cognitive overhead increase significantly
Solution Approach 1:
The system performs self-service by automatically generating optimized experimental protocols and robot control motions from user-defined parameters. The optimization engine autonomously determines dilution sequences, pipetting volumes, and timing without requiring expert programming knowledge, allowing users to simply specify experimental goals and receive fully optimized execution plans.
Solution Approach 2:
The system performs preliminary optimization computations before actual experiment execution. The optimization engine pre-calculates the optimal experimental protocol, dilution sequences, and robot motion paths based on user parameters, so that when the experiment runs, all decisions have already been made, eliminating the need for real-time programming or adjustment.
2Adaptability or versatility
If expert-level programming knowledge is required for robot operation, then complex experimental protocols can be implemented, but accessibility and adoption rates decrease
Solution Approach 1:
The optimization engine acts as an intermediary between the user's simple experimental parameters and the complex robot control system. It translates high-level user requirements into detailed, optimized protocols and robot commands, shielding users from the complexity of robot programming while still enabling sophisticated experimental workflows.
Solution Approach 2:
The system replaces the mechanical need for expert programming knowledge with an automated optimization engine that computationally generates protocols. Instead of users needing to manually program complex sequences, the engine substitutes this manual process with automated algorithmic generation, making the system accessible to non-experts.
3Productivity
If traditional manual input methods are used for experiment setup, then user control and customization are maintained, but throughput and efficiency are limited
Solution Approach 1:
The system segments the experiment setup process into distinct modular components: user parameter input, optimization computation, protocol generation, and robot execution. This segmentation allows each component to be independently optimized and managed, enabling high throughput without overwhelming complexity in any single area.
Solution Approach 2:
The optimization engine dynamically changes multiple parameters simultaneously (dilution factors, volumes, timing, sequence ordering) to maximize throughput. By computationally optimizing these parameters rather than using fixed manual settings, the system achieves high productivity while the user interface remains simple.
4Manufacturing precision
If automated optimization is implemented for experimental protocols, then pipetting errors are minimized and efficiency increases, but computational resources and processing time are required
Solution Approach 1:
The optimization computation is performed preliminarily before experiment execution, so that when the actual pipetting occurs, the optimized protocol is already ready to execute. This preliminary computational phase consumes energy and time, but enables high-precision pipetting during the actual experiment by providing pre-optimized sequences that minimize errors.
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.