Finite State Machine Profile Detection via Distance Segmentation
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Solution Overview
Problem
Existing methods for detecting the profile of an arrangement in automated manufacturing or transport processes are either computationally intensive or require complex data evaluation, making them unreliable and resource-heavy.
Innovation Solution
A method using a finite state machine to reduce the complexity of profile recognition by mapping distances to states and state transitions, allowing for a simplified and efficient comparison of measured profiles with reference profiles, without the need to store individual distance values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If comprehensive profile evaluation is performed, then reliability of profile recognition is improved, but device complexity and computational requirements increase
Solution Approach 1:
The patent segments the continuous profile data into discrete distance levels (e.g., 5 levels from closest to farthest). This segmentation transforms complex continuous measurements into simplified categorical states, reducing computational complexity while maintaining reliable profile recognition through the finite state machine's sequential evaluation of these discrete levels.
2Reliability
If comprehensive profile evaluation is performed, then reliability of profile recognition is improved, but computing power requirements increase
Solution Approach 1:
The patent segments the continuous profile data into discrete distance levels (e.g., 5 levels from closest to farthest). This segmentation transforms complex continuous measurements into simplified categorical states, reducing computational complexity while maintaining reliable profile recognition through the finite state machine's sequential evaluation of these discrete levels.
3Measurement precision
If comprehensive profile data is stored, then measurement precision is improved, but data storage requirements increase
Solution Approach 1:
The patent segments continuous distance measurements into discrete distance levels (closest, second closest, etc.), reducing storage requirements from storing every individual distance value to storing only the sequence of discrete levels observed during profile evaluation.
Solution Approach 2:
The patent extracts only the essential profile characteristics (sequence of distance levels) from the complete set of distance measurements. Instead of storing all raw distance data, the system extracts and stores only the relevant sequential pattern of distance levels, significantly reducing storage requirements while preserving measurement precision for profile recognition.
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
This approach provides a reliable and computationally lightweight method for profile recognition, reducing data storage and processing requirements while maintaining accuracy in identifying profile matches or deviations.
Implementation Method 1
The distance sensor (19) is a light sensor, which is designed to emit a light signal, to receive a reflection of the light signal generated by the respective surface, and to determine a distance of the respective surface from the light sensor from the received reflection
Implementation Method 2
The light sensor is designed to determine the distance between the respective surface and the light sensor from the transit time of a pulsed light signal to the respective surface and back
Implementation Method 3
or from the phase shift of a modulated light signal emitted by the light sensor to the light signal reflected on the respective surface
Data Source
Figure 1~2
Figure 3
AI summary
A method is provided for detecting the profile of an arrangement traversing the detection range of a distance sensor. The distance sensor is configured to continuously determine the distance between a surface of the arrangement located within the sensor's detection range and the sensor itself. The method is designed as a finite automaton, which has at least one rest state, one evaluation state, and several measurement states, each measurement state being assigned a range of distance values. In response to an activation event, the finite automaton transitions from the rest state to a measurement state. The finite automaton transitions from one measurement state to another when the distance sensor detects a distance that lies within the range of values assigned to the other measurement state. A sequence of measurement states is thus recorded.In response to a deactivation event, the finite automaton transitions from a measurement state to the evaluation state, in which, depending on a comparison of the recorded sequence with a reference sequence corresponding to the profile to be recognized, it is determined whether there is a profile match or a profile deviation.