3D Workcell Safety Monitoring With Self-Calibration Feedback
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Solution Overview
Problem
Industrial machinery safety systems, particularly those using 3D sensors, face challenges in configuration, data processing, and continuous monitoring, which can lead to safety hazards and inefficiencies in human-robot collaboration due to complex data streams and environmental variations.
Innovation Solution
A system that continuously monitors a workcell using 3D image sensors, performing initial calibration and registration, tracking static and moving elements, and employing environmental sensors to ensure accurate tracking and performance, with modules for self-error detection and a safety-rated protocol to issue alerts for potential hazards.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 3D sensors are used for safety monitoring to enable tighter human-robot coupling, then productivity and space utilization are improved, but system complexity and configuration difficulty increase
Solution Approach 1:
The system performs preliminary calibration and registration of 3D sensors during setup, storing reference data about the workcell environment. This preliminary action establishes a baseline that simplifies ongoing safety monitoring operations by pre-configuring the sensor system to recognize normal workcell geometry and object positions.
Solution Approach 2:
The system continuously compares real-time 3D sensor data against the stored reference data, providing feedback about deviations from the expected workcell state. This feedback mechanism automatically detects safety hazards without requiring complex manual configuration during operation, as the system self-monitors against its own baseline.
2Measurement precision
If 3D sensor data is processed to improve safety monitoring accuracy, then measurement precision is improved, but data processing complexity and computational requirements increase
Solution Approach 1:
The system extracts and stores only the essential geometric features and spatial relationships from 3D sensor data during calibration, rather than processing and storing complete point clouds. This extraction of critical information reduces computational complexity while maintaining the precision needed for safety monitoring.
Solution Approach 2:
The system performs preliminary processing of 3D sensor data during calibration to establish reference models of the workcell environment. By pre-processing and storing simplified representations of normal conditions, the system reduces the computational burden during real-time safety monitoring, comparing incoming data against pre-computed references rather than performing full analysis continuously.
3Reliability
If continuous monitoring is implemented to detect safety hazards, then reliability is improved, but system resource consumption and operational complexity increase
Solution Approach 1:
The system implements periodic comparison of 3D sensor data against reference models at predetermined intervals rather than continuous frame-by-frame analysis. This periodic action maintains safety monitoring reliability by regularly checking for deviations while reducing computational resource consumption and energy usage compared to continuous processing of every sensor frame.
Solution Approach 2:
The safety system monitors itself by comparing its own operational data against stored reference data, enabling self-diagnosis and self-monitoring of safety conditions. This self-service approach improves reliability through continuous self-validation while minimizing the need for external monitoring resources and reducing overall system complexity.
4Measurement precision
If calibration and registration processes are performed to ensure accurate monitoring, then measurement precision is improved, but setup time and operational downtime increase
Solution Approach 1:
The system performs calibration and registration as preliminary actions during initial setup or scheduled maintenance periods, storing reference data that enables accurate ongoing monitoring. By completing these time-consuming precision tasks in advance, the system achieves high measurement precision during operation without requiring repeated calibration interruptions.
Solution Approach 2:
The system creates digital copies or models of the workcell environment during calibration, storing reference representations that can be repeatedly compared against real-time sensor data. This copying approach allows the physical workcell to remain operational while digital references are created and stored, minimizing downtime by separating the calibration process from ongoing production operations.
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
Ensures reliable and continuous monitoring of the workspace and safety system performance, preventing safety hazards by correcting misalignments and detecting errors, thus enhancing human-robot collaboration and operational efficiency.
Implementation Method 1
3D image sensors...acquiring image data associated with the workcell
Implementation Method 2
employing environmental sensors to ensure accurate tracking and performance
Data Source
Figure 1
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AI summary
Systems and methods for continuously monitoring a workcell (100) during operation of industrial machinery (106) are disclosed. The system may comprise a safety system that includes at least one sensor (102) and supporting software and/or hardware for acquiring image data associated with the workcell; a monitoring system for detecting a parameter value associated with the safety system; and a controller (112) configured to determine a status of the safety system based at least in part on the detected parameter value and cause an alert to be issued if the status of the safety system does not satisfy a target objective.