Object Sensor Switching Point Learning for Reliable Detection
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Solution Overview
Problem
Existing sensor systems face challenges in reliably detecting objects due to variations in foreground and background objects, requiring manual adjustment of switching points which is labor-intensive and ineffective in complex situations.
Innovation Solution
A system and method for automatically setting a switching point through a learning phase where the sensor is trained with labeled data from various detection situations, allowing for robust and flexible object detection without expert intervention, using additional sensors for temporary support during the learning phase.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual adjustment of switching points is used, then the sensor can be configured for specific applications, but the process is labor-intensive and requires expert knowledge
Solution Approach 1:
The sensor system automatically determines optimal switching points through self-learning processes. The evaluation unit analyzes sensor signals from multiple sensor elements and autonomously configures switching points without requiring manual expert intervention, thereby improving ease of operation while maintaining detection reliability
Solution Approach 2:
The system uses feedback from sensor signals to continuously optimize switching points. The evaluation unit processes signals from multiple sensor elements, analyzes detection results, and adjusts switching points based on the detected object characteristics and background conditions, enabling automatic adaptation to different application scenarios
2Adaptability or versatility
If switching point is set to detect all possible object extremes, then detection coverage is maximized, but the minimum distance between real object and background becomes too large for normal applications
Solution Approach 1:
Instead of using a single universal switching point for all objects, the system determines local switching points tailored to specific object types and detection scenarios. The evaluation unit analyzes sensor signals and object characteristics to configure switching points that are optimized for each particular application, achieving both high adaptability and precise object-background separation
Solution Approach 2:
The switching points are made dynamic rather than static. The system can adapt switching point values based on detected object properties, background conditions, and detection requirements. This dynamic adjustment allows the sensor to maintain optimal performance across different application scenarios without requiring excessive distance margins
3Reliability
If multiple sensor elements are used to improve detection robustness, then detection reliability increases, but the complexity of determining switching points increases
Solution Approach 1:
The system merges the signals from multiple sensor elements and processes them together in a unified evaluation unit. By combining the sensor signals and determining switching points for the combined signal rather than individually for each element, the system maintains detection robustness while reducing the overall complexity of switching point determination
Solution Approach 2:
The evaluation unit is designed with multi-functionality to handle signals from multiple sensor elements and automatically determine appropriate switching points for various detection scenarios. This universal approach allows the same evaluation unit to process different sensor element configurations and object types without requiring separate complex determination processes for each case
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 automated setting of switching points enhances sensor reliability and adaptability, allowing for accurate object detection in complex scenarios without the need for manual expertise, and can be transferred to other sensors or applications.
Implementation Method 1
The optoelectronic sensors include through-beam and reflection light barriers that detect interruptions or weakening of a light beam directed at a light receiver
Implementation Method 2
reflection light barriers that detect interruptions or weakening of a light beam directed at a light receiver directly or via a reflector
Implementation Method 3
With a triangulation sensor, the optical axes of the light transmitter and light receiver are at an angle to one another, and with the aid of a spatially resolving light receiver, the angle at which an object is detected can be measured and a distance can thus be determined
Data Source
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AI summary
A sensor (10) for detecting an object (20) in a monitoring area (18) is specified, comprising at least one sensor element (36) for acquiring a sensor signal, a switching output (30) for outputting a binary object detection signal, and an evaluation unit (28) configured to generate the object detection signal from the sensor signal depending on the detected object (20) and to determine a switching point during a learning phase that establishes the mapping between the sensor signal and the object detection signal. The evaluation unit (28) is further configured to acquire a respective sensor signal for a multitude of detection situations during the learning phase, specifying the corresponding object detection signal for each detection situation, and to derive the switching point from this.