Robotic Sample Position Learning Using Fiducial Beacon Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current robotic systems for biological sample preparation and diagnostic assays require manual calibration and complex collision detection methods, leading to inefficiencies and potential de-calibration issues.
Innovation Solution
An apparatus with fiducial beacons and a robotic sample handler using magnetic sensors for automatic calibration, allowing the robotic handler to learn and navigate its workspace through a search pattern, determining the coordinates of the beacons to accurately position samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration is used to teach robot coordinates, then the robot can be precisely positioned in the workspace, but the system requires frequent human intervention and calibration
Solution Approach 1:
The system uses fiducial beacons with magnetic fields that enable the robot to automatically learn and calibrate workspace positions without human intervention. The robot's sensor detects the magnetic field of fiducial beacons during autonomous movement, automatically determining coordinates and updating its position map.
Solution Approach 2:
The patent replaces manual mechanical calibration with an automated magnetic field-based detection system. The fiducial beacons generate magnetic fields that are detected by the robot's sensor, substituting the mechanical process of manual coordinate entry with an automated electromagnetic detection and calculation process.
2Extent of automation
If bump-sense devices with force sensors are used for automatic calibration, then the robot can automatically detect locations, but the system becomes more complex and risks de-calibration through collisions
Solution Approach 1:
The patent replaces mechanical bump-sensing with magnetic field detection. Instead of using force sensors to detect physical collisions with fixed structures, the system uses a sensor to detect the magnetic field of fiducial beacons, eliminating the need for complex mechanical sensing systems and force measurement capabilities.
Solution Approach 2:
The fiducial beacons act as intermediaries between the robot and the workspace structures. Rather than the robot directly sensing physical structures through collision, the magnetic field of the fiducial beacon mediates the detection process, providing position information without requiring physical contact or complex force sensing.
3Ease of operation
If bump-sense devices are used for automatic calibration, then automated location detection is achieved, but the system requires fixed structures and force sensors that increase complexity
Solution Approach 1:
The patent replaces mechanical collision-based sensing with magnetic field-based detection. The sensor detects the magnetic field of fiducial beacons during robot movement, automatically calculating positions without requiring fixed physical structures for bumping or complex force sensor systems.
Solution Approach 2:
The fiducial beacons serve multiple functions: they provide position reference points for calibration, enable automated detection during robot movement, and work with any robot configuration without requiring specific fixed structures in the workspace. The magnetic field detection method is universally applicable across different workspace layouts.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables efficient and precise robotic sample handling without manual intervention, reducing calibration frequency and avoiding collisions, thereby enhancing system reliability and throughput.
Implementation Method 1
a sensor configured to generate a field detection signal when in a near vicinity of the fiducial beacon
Data Source
AI summary
An automated apparatus can provide pre-analytical processing of samples, racking and forwarding to an adjacent analyzer for analysis. The apparatus may have a controller that implements an auto-learn process to teach robotic handlers the locations within the workspace(s) of the apparatus. A robotic sample handler may include a sensor configured to generate a detection signal when in a near vicinity of a fiducial beacon in the workspace of the apparatus for biological sample preparation, preprocessing and/or diagnostic assay performed by one or more analyzers of the automated apparatus. The controller may control the robotic sample handler to conduct a search pattern so that a location of the fiducial beacon may be detected and thereafter calculated to obtain a more accurate location of the beacon. The calculated positions may then serve as a basis for the controlled movement of samples by the robot to and from locations of the workspace.