Six-Dimensional Force Sensor Calibration via Static and Dynamic Segmentation
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Solution Overview
Problem
Current six-dimensional force sensor calibration methods fail to accurately detect external acting forces and moments in different postures due to the influence of the sensor's weight and load weight, leading to inconsistent measurement values.
Innovation Solution
A method involving static and dynamic calibration, where the sensor's gravity and static force are calibrated in a static state, and dynamic force is calibrated using zero drift processing in a motion state, allowing for accurate detection of external forces and moments across various postures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If direct zeroing calibration is performed on the six-dimensional force sensor, then the calibration process is simple, but the sensor cannot accurately detect external acting forces and moments in different postures due to the influence of sensor weight and load weight
Solution Approach 1:
The calibration process is segmented into two distinct phases: static calibration (performed when the sensor is stationary) and dynamic calibration (performed during motion). This segmentation allows each phase to address specific error sources independently, with static calibration handling gravitational effects and dynamic calibration handling motion-induced errors, thereby achieving high measurement precision without overly complicating the overall calibration procedure
Solution Approach 2:
Static calibration is performed as a preliminary action before dynamic calibration. By first calibrating the sensor in a static state to establish baseline parameters and compensate for gravitational effects, the subsequent dynamic calibration can focus specifically on motion-related errors, improving overall detection accuracy while maintaining a structured and manageable calibration workflow
2Loss of time
If the sensor is calibrated without considering gravity and static force, then the calibration is quick and simple, but measurement values differ in different postures leading to inaccurate detection
Solution Approach 1:
The gravitational force and static force components are extracted and calibrated separately from dynamic measurement signals. By identifying and isolating these persistent error sources through static calibration, the system can compensate for them independently, ensuring measurement consistency across different postures without requiring excessive calibration time
Solution Approach 2:
The calibration process utilizes parameter changes by measuring the sensor output in multiple known static positions with different orientations. By collecting data across varying gravitational force vectors and calculating calibration parameters that account for these changes, the system achieves posture-independent measurement accuracy while maintaining efficient calibration timing
3Measurement precision
If static and dynamic calibration are performed separately, then the sensor can be accurately calibrated for different states, but the calibration process becomes more complex
Solution Approach 1:
The static calibration and dynamic calibration processes are merged into a unified calibration framework where parameters obtained from static calibration serve as initial values or constraints for dynamic calibration. This integration maintains the distinct advantages of each calibration type while reducing overall process complexity through coordinated parameter optimization
Solution Approach 2:
The calibration system performs self-service by automatically identifying whether the sensor is in a static or dynamic state and applying the appropriate calibration algorithm. The system autonomously selects calibration modes, processes corresponding data, and integrates results without requiring manual intervention to switch between calibration types, thereby reducing operational complexity while maintaining high accuracy
Data Source
AI summary
Embodiments of this application provide a sensor calibration method, apparatus, and device, a data measurement method, apparatus, and device, and a storage medium, where the sensor calibration method includes: acquiring a gravity of a sensor itself and a static force measured in a case that the sensor is in a static state and has no external acting force; performing equivalent calibration processing on the gravity and the static force to obtain a calibrated static force; acquiring a dynamic force measured in a case that the sensor is in a motion state and has no external acting force; performing zero drift processing on the dynamic force according to the calibrated static force to obtain a calibrated dynamic force; and using the calibrated dynamic force as a calibration parameter for performing calibration in a case that the sensor measures an external acting force, to obtain a calibrated sensor with the calibration parameter.