Human Detection Control for Movable Machine Collision Protection
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Solution Overview
Problem
In human-robot collaboration environments, existing safety measures struggle to efficiently and reliably monitor the presence and movements of people and non-human objects to prevent collisions and ensure safety, particularly in dynamic workspaces where various objects can interfere with the detection of kinematic parameters.
Innovation Solution
A protective device that uses a non-contact system to detect kinematic parameters such as location, speed, and acceleration of objects in three-dimensional space, and classifies objects based on polarization properties and motion modulation to trigger adaptive movement reductions in the movable machine, ensuring safety by slowing or stopping the machine when a person is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a non-contact protective device monitors the environment to detect kinematic parameters of objects, then the safety monitoring capability is improved, but the device complexity increases due to the need for multiple sensors and classification algorithms
Solution Approach 1:
The patent combines multiple sensor types (e.g., radar, camera, ultrasonic sensors) into a single protective device that integrates both detection and classification functions. This merging approach improves safety monitoring capability by utilizing complementary sensor data while managing complexity through unified processing architecture.
Solution Approach 2:
The protective device is designed with multi-functional capabilities to detect various object types (humans, animals, inanimate objects) using a single integrated system. The device performs both kinematic parameter detection and object classification functions, eliminating the need for separate specialized devices for each function.
2Measurement precision
If the protective device classifies objects based on polarization properties and motion modulation to differentiate humans from non-human objects, then the measurement precision is improved, but the difficulty of detecting and measuring increases
Solution Approach 1:
The patent utilizes changes in polarization properties and motion modulation parameters to differentiate object types. By monitoring these specific physical parameters and their temporal variations, the system achieves high classification accuracy for distinguishing humans from non-human objects without requiring direct contact or complex intervention.
Solution Approach 2:
The patent replaces direct mechanical detection methods with optical and electromagnetic field-based detection (polarization analysis, motion modulation detection). This substitution enables non-contact, high-precision measurement of object properties while reducing the complexity of physical measurement systems.
3Reliability
If the movable machine implements adaptive protective measures by slowing or stopping based on detected objects, then the safety is improved, but the productivity decreases due to movement reductions
Solution Approach 1:
The patent implements dynamic adaptive protective measures where the movable machine's speed and stopping behavior are automatically adjusted based on real-time object detection and classification. When humans are detected, the machine slows or stops; when only inanimate objects are present, normal operation continues. This dynamic response optimizes both safety and productivity by avoiding unnecessary interruptions.
Solution Approach 2:
The system establishes a closed-loop feedback mechanism where detection results from the protective device continuously inform the movable machine's control system. The machine receives real-time feedback about detected objects and automatically adjusts its operation accordingly, enabling safety-critical decisions to be made without human intervention and minimizing productivity impact.
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
The solution enables efficient and reliable monitoring of the environment, allowing for precise control of the movable machine's movements to prevent collisions, thereby enhancing safety in human-robot collaboration by accurately differentiating between human and non-human objects and adapting the machine's operation accordingly.
Implementation Method 1
detects polarization properties and motion modulation of the respective object
Implementation Method 2
detects polarization properties and motion modulation of the respective object
Data Source
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AI summary
A method for protecting people in the vicinity of a moving machine, particularly in the context of human-robot collaboration, is proposed. This method involves monitoring the environment using a protective device designed to detect one or more kinematic parameters of an object in the environment and to control the moving machine accordingly, based on these detected kinematic parameters. The protective device detects polarization properties and motion modulation of the object, which are then used to classify it as a person.Then, and only if the object in question has been classified as a human being, the protective device controls the moving machine depending on the detected kinematic parameters of this object, in particular depending on the location and speed of the object relative to the moving machine, to implement the protective measure.