3D Workspace Safety Signaling for Human-Robot Collaboration
Solution Overview
Problem
Industrial machinery poses hazards to humans, and existing safeguarding systems, while effective, can be complex for human operators to understand, leading to productivity issues and unexpected machinery stops due to arbitrary proximity-based decision-making.
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, incorporating the geometry and movements of machinery and humans, and providing real-time safety indications through visual or audible signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional safeguarding systems are used to ensure safety, then safety is improved, but device complexity increases and ease of operation deteriorates
Solution Approach 1:
The patent replaces complex mechanical safeguarding systems with optical sensing systems (cameras, depth sensors) and computational algorithms to detect human presence and determine safety. This substitution reduces mechanical complexity while maintaining safety through software-based monitoring and real-time analysis of workspace conditions.
Solution Approach 2:
The system dynamically changes safety parameters based on real-time conditions. Instead of fixed safety zones, the system continuously adjusts safe/unsafe region boundaries by analyzing human position, machinery position, and predicted trajectories. This allows the safety system to adapt to varying operational conditions, reducing unnecessary restrictions while maintaining protection.
2Productivity
If sophisticated 3D sensing and analysis approaches are used to allow closer human-machine collaboration, then productivity is improved, but ease of operation deteriorates due to difficulty in evaluating system decisions
Solution Approach 1:
The system provides visual feedback by projecting safe/unsafe region boundaries directly into the workspace using augmented reality displays or visual indicators. This allows operators to see exactly what the system perceives and understand the rationale behind safety decisions in real-time, improving trust and ease of operation while maintaining sophisticated 3D sensing capabilities.
Solution Approach 2:
The patent introduces an intermediary visualization layer that translates complex sensor data and algorithmic decisions into intuitive visual representations. This intermediary layer (AR overlays, colored zone indicators) mediates between the sophisticated sensing system and the human operator, making system decisions transparent without simplifying the underlying complex detection algorithms.
3Reliability
If arbitrary proximity-based decision-making is used to control machinery, then safety is improved, but productivity deteriorates due to unexpected stops and slowdowns
Solution Approach 1:
The system transitions from static, arbitrary proximity thresholds to dynamic safety boundaries that adapt in real-time based on actual human and machinery positions, velocities, and predicted trajectories. This dynamic approach allows the system to maintain safety while reducing unnecessary stops by accurately assessing real-time risk rather than relying on fixed distance limits.
Solution Approach 2:
The system performs preliminary prediction of future human and machinery positions to anticipate potential hazards before they occur. By forecasting trajectories and predicting future states, the system can take preventive action only when truly necessary, avoiding unnecessary interruptions while maintaining safety through proactive risk assessment.
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.