Automatic Vehicle Parameter Calibration via Sensor Bias Correction
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Solution Overview
Problem
Automated industrial vehicles face significant errors in navigation and operation due to changes in vehicle parameters over time, such as wheel diameter, wear, and seasonal conditions, which are not accurately reflected in sensor measurements, leading to inaccuracies in localization and mapping.
Innovation Solution
A computer-implemented method for automatically calibrating vehicle parameters by analyzing errors between actual and observed sensor readings from multiple measurement models, identifying bias, and adjusting parameters to correct for changes such as tire wear and sensor drift, using a system that includes a mobile computer, central computer, and sensor array with encoders, laser scanners, and cameras to model vehicle pose and correct for errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle parameters are not calibrated over time, then the system structure remains simple and operation is easy, but navigation accuracy and measurement precision deteriorate due to parameter changes from wear and tear
Solution Approach 1:
The calibration system performs self-calibration by automatically detecting parameter changes through sensor data analysis and adjusting vehicle parameters without external intervention. The system monitors its own performance degradation and corrects it autonomously, eliminating the need for manual calibration operations.
Solution Approach 2:
The system continuously monitors sensor measurements and compares them against expected values to detect deviations caused by parameter changes. This feedback loop triggers automatic calibration when accuracy thresholds are exceeded, maintaining measurement precision through closed-loop control.
2Measurement precision
If manual calibration is performed regularly, then measurement precision can be maintained, but productivity decreases due to time loss from calibration operations
Solution Approach 1:
The calibration process occurs continuously in the background during normal vehicle operation rather than requiring scheduled stoppages. Sensor data is constantly analyzed and parameters are automatically adjusted, ensuring measurement precision is maintained without interrupting productivity.
Solution Approach 2:
The system performs self-calibration autonomously during operation, eliminating the need for manual calibration interventions that would reduce productivity. The vehicle monitors and corrects its own parameter drift without human involvement or operational disruption.
3Measurement precision
If multiple sensors are used to improve location accuracy, then measurement precision improves, but device complexity and cost increase
Solution Approach 1:
The system fuses data from multiple sensors (laser scanners, cameras, encoders) to achieve accurate location determination. By combining complementary sensor modalities, the system achieves robust measurement precision that relies on the collective strength of multiple sensors rather than any single sensor.
Solution Approach 2:
The sensor array serves dual purposes: primary navigation functions and automatic calibration functions. The same sensors used for location determination also detect parameter changes for calibration, eliminating the need for separate calibration sensors and reducing overall system complexity.
Data Source
AI summary
A method and apparatus for automatically calibrating vehicle parameters is described. In one embodiment, the method includes measuring a parameter value during vehicle operation; comparing the measured parameter value to an actual parameter value to identify at least one vehicle parameter bias as an actual measurement error; and modifying at least one vehicle parameter based on the at least one vehicle parameter bias.