Mixed Reality Lab Automation Guidance
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Solution Overview
Problem
Automated laboratory systems experience downtime and errors due to human errors, primarily caused by improper setup and placement of labware, leading to increased costs and reduced efficiency.
Innovation Solution
The implementation of a mixed reality system that provides real-time guidance and feedback to users through augmented reality, using recognizable markers and machine learning algorithms to assist in the setup and configuration of automated laboratory equipment, ensuring accurate placement and operation of labware and instruments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated laboratory systems are used to increase productivity, then output per unit time is improved, but human error from improper setup and placement of labware increases system failures and downtime
Solution Approach 1:
The system uses computer vision to continuously monitor labware placement and provides real-time feedback to users through a graphical user interface, alerting them to improper placement before it causes system failures. This closed-loop feedback mechanism prevents errors that would otherwise lead to downtime while maintaining high automation productivity.
Solution Approach 2:
The patent replaces manual visual inspection and physical verification of labware placement with an automated computer vision system using cameras and machine learning algorithms. This substitution eliminates human error in setup verification while maintaining the productivity benefits of automation.
2Adaptability or versatility
If complex automated laboratory equipment is deployed to enhance experimental capabilities, then research effectiveness is improved, but setup complexity and configuration difficulty increase
Solution Approach 1:
The system automatically detects and verifies labware placement using computer vision, eliminating the need for users to manually configure complex settings. The automated verification process makes the system self-sufficient in checking its own setup, reducing the operational burden on users while maintaining versatile experimental capabilities.
Solution Approach 2:
The graphical user interface uses color-coded visual feedback to indicate the status of labware placement (e.g., green for correct, red for incorrect). This visual signaling system simplifies the complex verification process into intuitive color cues, making operation easier without reducing experimental versatility.
3Reliability
If real-time monitoring and guidance systems are implemented to reduce errors, then reliability is improved, but device complexity and computational requirements increase
Solution Approach 1:
The system introduces a computer vision system as an intermediary layer between the physical labware and the automated laboratory equipment. This intermediary automatically verifies placement and communicates status to the control system, improving reliability without requiring direct complex integration between all system components.
Solution Approach 2:
The computer vision system creates digital copies (images) of the physical labware placement, which are then processed by machine learning algorithms to verify correctness. This copying approach simplifies the monitoring architecture by separating the visual detection function from the control logic, reducing overall system complexity.
Data Source
AI summary
Systems and methods for providing instructing mixed reality overlays for configuring automation protocols in laboratory processes using computing systems include receiving an image from an image feed of an environment. Component markers in the image of the environment are detected and matched with an associated environment component, an environment component action or both. The environment component or environment component action, or both are selected and an animation of an instruction is rendered. An overlay of the animation is caused to display in an augmented reality device associated with a user to appear in a location in the environment of the environment component or environment component action, or both.