Multi-Cell Workspace Mapping for 3D Safety Monitoring
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Solution Overview
Problem
Current systems fail to reliably detect, classify, and track humans and objects in 3D within industrial workspaces, especially in dynamic and occluded environments, limiting their ability to ensure safe interactions between humans and machinery.
Innovation Solution
A safety system utilizing a network of 3D sensors and controllers that create a 3D representation of the workspace, classifying regions as occupied, unoccupied, or unknown, and predicting object movements to adjust machinery operations and prevent collisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 3D sensors and controllers are deployed to monitor multiple workcells, then safety monitoring capability is improved, but system complexity increases
Solution Approach 1:
The system divides the factory workspace into multiple discrete workcells, each with its own local controller that independently maps and monitors safe regions. This segmentation allows each controller to manage a manageable portion of the workspace while collectively providing comprehensive safety monitoring across the entire facility.
Solution Approach 2:
Each workcell controller is designed to perform multiple functions: it maps the 3D workspace geometry, identifies safe regions, tracks objects and humans, predicts movements, and controls machinery safety. This multi-functionality reduces the need for separate specialized systems for each task.
2Measurement precision
If 3D workspace mapping and object tracking are implemented, then detection accuracy is improved, but computational requirements increase
Solution Approach 1:
The system performs preliminary mapping of the workspace geometry and identification of safe regions before actual operation begins. This pre-computed spatial information is stored and reused during runtime, avoiding the need for continuous complex computations while maintaining high detection accuracy.
Solution Approach 2:
The system focuses computational resources on tracking only the relevant objects and regions within each workcell rather than processing all possible data points in the entire factory. This partial action approach maintains detection accuracy while reducing overall computational load.
3Reliability
If real-time prediction of object movements is performed, then collision prevention capability is improved, but processing time increases
Solution Approach 1:
The system pre-computes possible movement trajectories and safe regions based on the mapped workspace geometry. During operation, it only needs to compare current object positions against these pre-determined safe paths and regions, significantly reducing real-time processing requirements while maintaining collision prevention capability.
4Area of stationary object
If multiple workcells are monitored with individual controllers, then monitoring coverage is improved, but communication overhead increases
Solution Approach 1:
The system segments the monitoring task by assigning each workcell its own controller that independently manages local safety monitoring. This segmentation reduces communication overhead by eliminating the need for centralized processing of all sensor data, as each controller handles its own workcell autonomously.
Data Source
AI summary
Safety systems in distributed factory workcells intercommunicate or communicate with a central controller so that when a person, robot or vehicle passes from one workcell or space into another on the same factory floor, the new workcell or space need not repeat the tasks of analysis and classification and can instead immediately integrate the new entrant into the existing workcell or space-monitoring schema. The workcell or space can also communicate attributes such as occlusions, unsafe areas, movement speed, and object trajectories, enabling rapid reaction by the monitoring system of the new workcell or space.