Semantic Robot Workspace Safe Zones for Dynamic Workpiece Handling
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Solution Overview
Problem
Industrial robotic systems face challenges in dynamically configuring safe zones due to the need to consider various movements and interactions between robots, workpieces, and humans, leading to larger-than-needed exclusion zones and complex safety implementations.
Innovation Solution
A system utilizing semantic understanding and sensor networks to distinguish between workpieces and other objects, dynamically tracking the robot-workpiece combination's envelope and marking safe zones, allowing for real-time adjustment of safety protocols based on the presence and configuration of workpieces and humans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional exclusion zones are defined based on robot movement envelope, then safety is ensured, but floor space is wasted and robot operating time is reduced
Solution Approach 1:
The patent implements dynamic safe zone determination that continuously updates exclusion zones based on real-time robot position, velocity, acceleration, and payload characteristics. Instead of static exclusion zones, the system calculates dynamic safe zones that expand and contract with robot motion, allowing the robot to operate more efficiently while maintaining safety margins.
Solution Approach 2:
The system changes multiple parameters simultaneously including robot kinematic parameters (position, velocity, acceleration), payload parameters (mass, center of gravity, inertia), and environmental parameters (workspace boundaries, obstacle locations) to dynamically recalculate safe zones. This multi-parameter approach allows precise determination of minimal safe zones rather than conservative fixed zones.
2Reliability
If exclusion zones are enlarged to account for workpiece movements, then safety is improved, but floor space and robot operating time are further reduced
Solution Approach 1:
The patent dynamically determines safe zones that adapt to actual workpiece movements rather than using fixed enlarged zones. The system monitors workpiece position, velocity, and acceleration in real-time and adjusts the exclusion zone boundaries accordingly, minimizing the floor space required while ensuring safety during workpiece manipulation.
Solution Approach 2:
The system performs preliminary calculations of workpiece trajectories and potential collision paths before executing robot movements. By predicting workpiece motion based on current robot state and planned trajectories, the system can establish minimal safe zones in advance rather than using conservative oversized zones.
3Adaptability or versatility
If complex custom state machines are implemented to support manual and automated process interactions, then process flexibility is improved, but device complexity increases
Solution Approach 1:
The patent implements a universal safe zone determination system that handles multiple process types (manual operation, automated operation, collaborative work) through a single integrated algorithm. The system universally applies dynamic safe zone calculations across all operation modes, eliminating the need for separate state machines for each process type while maintaining flexibility.
Solution Approach 2:
The system automatically detects and adapts to different operation modes and process requirements without requiring complex external control logic. The safe zone determination algorithm self-adjusts based on real-time sensor data and robot state, providing process flexibility through autonomous decision-making rather than pre-programmed state machines.
4Device complexity
If semantic understanding is implemented to distinguish workpieces from other objects, then safety implementation becomes simpler, but measurement and detection difficulty increases
Solution Approach 1:
The patent replaces complex semantic analysis and object recognition systems with a simplified detection approach that relies on physical characteristics (position, velocity, acceleration thresholds) to distinguish workpieces from other objects. This substitution of mechanical/physical detection for semantic understanding reduces detection complexity while maintaining adequate discrimination capability.
Solution Approach 2:
The system uses changes in physical parameters (velocity, acceleration, position continuity) to identify workpieces rather than relying on semantic analysis of object appearance or characteristics. By monitoring parameter changes during robot operation, the system can distinguish workpieces from stationary objects or humans without complex detection algorithms.
Data Source
AI summary
Embodiments of the present invention determine the configuration of a workpiece and whether it is actually being handled by a monitored piece of machinery, such as a robot. The problem solved by the invention is especially challenging in real-world factory environments because many objects, most of which are not workpieces, may be in proximity to the machinery. Accordingly, embodiments of the invention utilize semantic understanding to distinguish between workpieces that may become associated with the robot and other objects (and humans) in the workspace that will not, and detect when the robot is carrying a workpiece.