3D Workspace Monitoring for Occlusion-Aware Machine Safety
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Solution Overview
Problem
Existing 3D sensor systems for monitoring industrial environments are difficult to configure and require significant design and testing efforts to ensure safety, as they need to account for specific hazards, machinery motion, human actions, and workspace layout, leading to potential safety hazards due to occlusions and undetectable areas.
Innovation Solution
A system that uses registered sensors to classify workspace regions as occupied, unoccupied, or unknown, with dynamic updates and semantic analysis, treating potentially occupied space as occupied for safety purposes, and employing machine learning for optimal sensor placement and occlusion management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If 3D sensor systems are used to monitor workspace for safety, then measurement precision and detection capability are improved, but device complexity and difficulty of configuration increase
Solution Approach 1:
The system performs self-calibration by automatically detecting occlusions and adjusting sensor parameters without manual intervention. The sensors autonomously adapt to workspace changes and object positions, eliminating the need for complex manual configuration while maintaining high detection precision
Solution Approach 2:
The system dynamically adjusts sensor parameters such as detection thresholds, sensitivity levels, and monitoring zones based on real-time workspace conditions. This automatic parameter adaptation allows the system to maintain optimal detection capability without requiring manual reconfiguration when workspace conditions change
2Measurement precision
If sensors are placed to cover all workspace areas, then detection capability is improved, but occlusions and undetectable areas increase
Solution Approach 1:
The system combines data from multiple sensors to create a fused workspace model that compensates for individual sensor occlusions. By merging detection results from different sensor perspectives, the system achieves complete workspace coverage without requiring every area to be directly visible to every sensor
Solution Approach 2:
The system transitions from 2D sensor planes to 3D volumetric workspace modeling, allowing detection of objects in three-dimensional space. This dimensional enhancement enables the system to detect objects that may be occluded in 2D projections by utilizing depth information and spatial relationships across multiple dimensions
3Reliability
If guarding systems separate humans and machines, then safety is improved, but productivity and workspace utilization decrease
Solution Approach 1:
The system dynamically adjusts safety zones and machine operating states based on real-time detection of human presence and behavior. Instead of static separation, the system adapts guarding levels to actual risk conditions, allowing machines to operate at full capacity when safe while providing enhanced protection when humans are nearby
Solution Approach 2:
The system applies different levels of guarding and safety monitoring to different regions of the workspace based on local risk assessment. High-risk areas receive enhanced monitoring and stricter access control, while low-risk areas allow greater freedom of movement and machine operation, optimizing both safety and productivity locally
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 granularity and safety of 3D workspace monitoring, reducing the risk of safety hazards by accurately identifying safe and unsafe conditions in real-time, while optimizing floor space and system throughput.
Implementation Method 1
3D time-of-flight cameras
Implementation Method 2
3D LIDAR
Implementation Method 3
stereo vision cameras
Data Source
AI summary
Systems and methods monitor a workspace for safety purposes using sensors distributed about the workspace. The sensors are registered with respect to each other, and this registration is monitored over time. Occluded space as well as occupied space is identified, and this mapping is frequently updated.