Dynamic Safety Signaling for Human-Robot Workspaces
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Solution Overview
Problem
Industrial machinery poses hazards to humans, and existing safeguarding systems either severely restrict interaction or are difficult for workers to understand, leading to productivity issues and unexpected machinery stops due to complex decision-making processes based on proximity and future trajectory projections.
Innovation Solution
A system that uses sensors and computational modeling to dynamically identify safe and unsafe zones in a 3D workspace by generating a 3D spatial representation, including safety envelopes around machinery and humans, and provides real-time visual or audible feedback on safety levels through colored illumination or other signaling methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional safeguarding systems are used to ensure safety, then safety is improved, but interaction between human and machine is severely restricted and productivity decreases
Solution Approach 1:
The patent implements dynamic safety monitoring that continuously adjusts safety parameters based on real-time detection of human presence, machinery state, and environmental conditions. This allows the system to transition from static safety zones to dynamic safety envelopes that adapt to current operational context, enabling safer human-machine collaboration while maintaining productivity
Solution Approach 2:
The system changes safety parameters dynamically based on detected conditions. When no human is detected, the system operates in full safety mode with strict safety zones. When human presence is detected and verified as safe, the system adjusts parameters to allow closer interaction, thereby improving productivity while maintaining safety
2Ease of operation
If complex 3D sensing and analysis approaches are used to enable closer human-machine collaboration, then interaction is improved, but the system becomes difficult for human operators to understand and evaluate
Solution Approach 1:
The patent implements visual feedback mechanisms that display safety zones, detection status, and system decisions to human operators in real-time. This feedback loop makes the complex 3D sensing and analysis transparent to operators, allowing them to understand and evaluate system behavior without needing to comprehend the underlying complexity
Solution Approach 2:
The system uses color-coded visual indicators to represent different safety states and zones. Complex spatial information is simplified into intuitive color displays that show operators the current safety status and system decisions at a glance, making the system easier to understand and monitor
3Reliability
If dynamic 3D speed and separation monitoring is implemented to make safety decisions based on proximity, then safety is improved, but unexpected machinery stops occur due to arbitrary proximity thresholds
Solution Approach 1:
The patent replaces arbitrary fixed proximity thresholds with dynamic safety calculations that consider multiple factors including human position, velocity, trajectory, machinery state, and environmental conditions. This dynamic approach allows the system to make more nuanced safety decisions that reduce unnecessary stops while maintaining safety
Solution Approach 2:
The system dynamically changes safety parameters such as safe separation distances and speed limits based on real-time conditions. When conditions are favorable, the system allows closer proximity and higher speeds; when risks increase, parameters are adjusted accordingly. This prevents arbitrary stops while maintaining safety
Data Source
AI summary
Systems and methods for determining safe and unsafe zones in a workspace—where safe actions are calculated in real time based on all relevant objects (e.g., some observed by sensors and others computationally generated based on analysis of the sensed workspace) and on the current state of the machinery (e.g., a robot) in the workspace—may utilize a variety of workspace-monitoring approaches as well as dynamic modeling of the robot geometry. The future trajectory of the robot(s) and/or the human(s) may be forecast using, e.g., a model of human movement and other forms of control. Modeling and forecasting of the robot may, in some embodiments, make use of data provided by the robot controller that may or may not include safety guarantees.