Mobile Sensor Calibration for Temperature-Driven Heading Drift
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Solution Overview
Problem
Sensors in autonomous devices, such as robotic vehicles, require periodic calibration to correct bias and sensitivity errors caused by temperature, humidity, and other environmental changes, which can disrupt device operation and lead to navigation errors if not performed opportunistically.
Innovation Solution
A method for a mobile robotic device to estimate accumulated heading error based on temperature changes, triggering calibration procedures when the error exceeds a threshold, and using predictive models to iteratively compensate for bias drift, allowing for calibration during suitable operational states without disrupting device operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor calibration is performed periodically to correct bias and sensitivity errors, then measurement precision is improved, but device operation is disrupted and loss of time increases
Solution Approach 1:
The system performs preliminary actions by continuously monitoring temperature and estimating accumulated heading error in advance, so that calibration can be triggered proactively when error thresholds are approached, rather than waiting for periodic schedules or manual intervention. This allows calibration to be performed at optimal moments with minimal operational disruption.
Solution Approach 2:
The system implements feedback by continuously estimating accumulated heading error based on temperature changes and sensor data, then using this feedback to dynamically trigger calibration when needed. The calibration process itself provides feedback to update bias estimates, creating a closed-loop system that maintains precision without fixed periodic interruptions.
2Reliability
If calibration is performed frequently to maintain navigation accuracy, then reliability is improved, but productivity decreases due to operational interruptions
Solution Approach 1:
The system transitions from static periodic calibration to dynamic on-demand calibration. The calibration frequency and timing are dynamically adjusted based on real-time temperature changes, accumulated error estimates, and operational context. This allows the system to maintain high reliability by calibrating when errors accumulate while maximizing productivity by avoiding unnecessary calibration interruptions.
Solution Approach 2:
The system changes the calibration trigger parameter from fixed time intervals to dynamic error thresholds based on temperature-induced drift. By monitoring temperature changes and estimating accumulated heading error, the system adapts calibration timing to actual sensor degradation patterns, maintaining navigation accuracy while reducing redundant calibrations that would reduce productivity.
3Measurement precision
If calibration requires specific conditions such as lack of motion, then measurement precision is improved, but ease of operation worsens due to operational constraints
Solution Approach 1:
The system performs preliminary actions by continuously monitoring operational context, motion states, and temperature before calibration is needed. It identifies and queues calibration opportunities in advance, preparing to execute calibration when conditions are favorable, thus maintaining precision without requiring manual intervention or complex operational coordination.
Solution Approach 2:
The system implements self-service by automatically monitoring its own operational state, temperature, and error accumulation, then autonomously triggering and managing calibration when appropriate conditions are met. This eliminates the need for manual calibration initiation and reduces operational constraints, as the system serves itself without external intervention.
Data Source
AI summary
A mobile robotic device has a motion sensor assembly configured to provide data for deriving a navigation solution for the mobile robotic device. The mobile robotic device temperature is determined for at least two different epochs so that an accumulated heading error of the navigation solution can be estimated based on the determined temperature at the at least two different epochs. A calibration procedure is then performed for at least one sensor of the motion sensor assembly when the estimated accumulated heading error is outside a desired range.