3D TOF Safety Monitoring With Digital Twin Point Cloud Subtraction
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Solution Overview
Problem
Industrial safety systems face challenges in accurately detecting hazardous conditions in dynamic environments due to the complexity of analyzing comprehensive 3D point cloud data from TOF sensors, often resulting in false safety trips.
Innovation Solution
A functional safety system that uses a digital twin of the industrial automation system to emulate real-time operations, generating shadow point cloud data which is subtracted from measured data to yield reduced point cloud data, focusing analysis only on anomalous entities, thereby improving hazard detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive 3D point cloud data from TOF sensors is analyzed to detect hazardous conditions, then hazard detection capability is improved, but false safety trips increase due to data complexity
Solution Approach 1:
The patent extracts and removes shadow point cloud data representing known stationary objects from the comprehensive measured point cloud data. This extraction process isolates only the anomalous entities (moving objects or hazards) from the full dataset, enabling precise hazard detection without false alarms caused by stationary structures. The shadow removal component specifically extracts and eliminates data corresponding to known entities, leaving only relevant hazard information.
2Reliability
If comprehensive 3D point cloud data is processed to ensure complete hazard coverage, then safety monitoring coverage is improved, but computational complexity and processing time increase
Solution Approach 1:
The patent extracts shadow point cloud data representing known entities and removes it from the comprehensive dataset, reducing computational complexity while maintaining safety coverage. By extracting only the necessary hazard-related information and eliminating redundant stationary object data, the system achieves efficient processing without compromising monitoring completeness.
Solution Approach 2:
The patent segments the comprehensive point cloud data into distinct components: shadow point cloud data (known stationary objects) and reduced point cloud data (anomalous entities). This segmentation allows the system to process different data types separately, applying appropriate analysis methods to each, thereby reducing overall computational complexity while maintaining thorough safety monitoring.
3Productivity
If shadow point cloud data is subtracted from measured data to reduce data set size, then processing efficiency is improved, but data accuracy may be compromised
Solution Approach 1:
The patent extracts shadow point cloud data with precise spatial coordinates and geometric information representing known stationary objects. By carefully extracting and removing only this specific subset of data while preserving the complete reduced point cloud data containing anomalous entities, the system maintains full hazard detection accuracy while achieving significant processing efficiency improvements.
Solution Approach 2:
The patent creates a digital twin (shadow point cloud) that is a precise copy or representation of the known stationary objects in the industrial automation system. This shadow copy allows the system to subtract expected stationary object data from the measured point cloud without losing information about actual hazards, thereby maintaining detection accuracy while improving processing efficiency.
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
This approach enhances the accuracy of hazard detection by filtering out known entities from the data set, reducing false safety triggers and improving the responsiveness of safety actions in dynamic industrial environments.
Implementation Method 1
measured point cloud data generated by a TOF sensor that monitors an area comprising the industrial automation system
Data Source
AI summary
A functional safety system performs safety analysis on three-dimensional point cloud data measured by a time-of-flight (TOF) sensor that monitors a hazardous industrial area that includes an automation system. To reduce the amount of point cloud data to be analyzed for hazardous conditions, the safety system executes a real-time emulation of the automation system using a digital twin and live controller data read from an industrial controller that monitors and controls the automation system. The safety system generates simulated, or shadow, point cloud data based on the emulation and subtracts this simulate point cloud data from the measured point cloud data received from the TOF sensor. This removes portions of the point cloud data corresponding to known or expected elements within the monitored area. Any remaining entities detected in the reduced point cloud data can be further analyzed for safety concerns.