Inertial Sensor Array Rotational Rate Estimation via Hidden Markov Model
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Solution Overview
Problem
Current robotic systems face challenges in accurately determining rotational rates due to the accumulation of errors in inertial measurement units (IMUs), particularly with high-performance gyroscopes being impractical due to high costs, and there is a lack of middle-ground options that provide both low cost and adequate performance.
Innovation Solution
A method utilizing a redundant array of inexpensive inertial sensors, specifically MEMS gyroscopes, is proposed, which employs a hidden Markov model (HMM) to fuse sensor readings, account for noise and bias, and determine rotational rates by solving for the output using algorithms like the forward algorithm, Viterbi algorithm, or pre-filtering to reduce computation time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high-performance gyroscopes are used, then measurement precision is improved, but device cost increases
Solution Approach 1:
The patent employs multiple low-cost MEMS gyroscopes instead of a single high-performance gyroscope. By using inexpensive sensors that can be easily replaced or recalibrated, the system achieves high measurement precision through redundancy and fusion algorithms while keeping individual sensor costs low
Solution Approach 2:
The patent combines readings from multiple independent gyroscope sensors through sensor fusion algorithms. By merging data from several low-cost sensors, the system achieves measurement precision comparable to expensive single sensors while maintaining lower overall system cost and improved reliability
2Measurement precision
If multiple inertial sensors are used, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent uses identical copies of the same low-cost MEMS gyroscope sensor multiple times rather than employing different types of sensors. This simplifies the system architecture while improving precision through redundancy, as all sensors share the same characteristics and can be processed uniformly
Solution Approach 2:
The patent transforms the sensor array output from individual raw measurements into a fused orientation estimate by changing the parameter representation.通过使用HMM和滤波算法，将多个传感器的原始数据转换为统一的姿态估计参数，简化了后续处理复杂度
3Measurement precision
If sensor fusion algorithms are used, then measurement precision is improved, but computation time increases
Solution Approach 1:
The patent performs preliminary filtering and preprocessing of sensor data before applying complex fusion algorithms. By pre-processing the incoming sensor streams to remove obvious outliers and normalize data, the system reduces the computational burden on subsequent fusion algorithms while maintaining precision
Solution Approach 2:
The patent uses efficient algorithms that skip unnecessary computation steps in real-time processing. By implementing optimized HMM solutions and approximation methods, the system achieves accurate rotational rate determination without full exhaustive computation, reducing latency while maintaining precision
Data Source
AI summary
A method for determining the rotational rate of a movable member using an array of inertial sensors is provided. The method includes defining a hidden Markov model (“HMM”). The HMM represents a discrete value measurement of the rotational rate of the movable member. A transition probability of the HMM accounts for a motion model (linear or non-linear) of the movable member. An observation probability of the HMM accounts for noise and bias of at least one of the inertial sensors of the array of inertial sensors. A processor receives input from the array of inertial sensors. The processor determines the rotational rate of the movable member by solving for an output of the HMM using the input received from the array of inertial sensors. The processor may use a forward algorithm, a forward-backward algorithm, or a Viterbi algorithm to solve the HMM.