Bucket Tooth Tip Coordinate Estimation Under Sensor Noise
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Solution Overview
Problem
Existing technologies struggle to accurately estimate the three-dimensional coordinates of a bucket tooth tip in excavators due to system noise and measurement noise, leading to reduced accuracy and quality in construction tasks such as trenching and sloping.
Innovation Solution
A method and system that utilizes a kinematic model, dynamic sensors, and a Kalman filter to determine estimated coordinates of the bucket tooth tip by accounting for system noise and measurement noise, involving the establishment of coordinate systems, determination of noise covariances, and iterative prediction and estimation using a state vector and error covariance matrix.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a kinematic model and sensor measurements are used to estimate bucket tooth tip coordinates, then the measurement precision is improved, but system noise and measurement noise reduce the reliability of the estimation
Solution Approach 1:
The patent implements a feedback mechanism through the Kalman filter algorithm, where the estimated coordinates are continuously compared with sensor measurements, and the difference (residual) is used to correct the estimation. The filter gain dynamically adjusts based on the covariance matrices to optimize the feedback correction, thereby improving reliability while maintaining precision.
Solution Approach 2:
The patent introduces an intermediary error covariance matrix that mediates between the kinematic model predictions and sensor measurements. This matrix quantifies the uncertainty in the estimation process and allows the system to weigh the reliability of different data sources, effectively handling noise while maintaining measurement precision.
2Reliability
If noise covariance matrices are introduced to handle system noise and measurement noise, then the reliability is improved, but the device complexity increases
Solution Approach 1:
The patent changes the parameters of the estimation system by introducing covariance matrices (system noise covariance Q and measurement noise covariance R) that characterize the noise properties. These parameter changes allow the Kalman filter to adaptively handle noise without requiring complex hardware modifications, thereby improving reliability with minimal increase in system complexity.
3Measurement precision
If iterative prediction and estimation using Kalman filter is performed, then the measurement precision is improved, but the computational time increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating and storing the covariance matrices and filter gain values during system initialization. The Kalman filter uses these pre-computed parameters in real-time operations, reducing the computational burden during iterative prediction and estimation, thereby minimizing time loss while maintaining precision.
Data Source
AI summary
The present disclosure relates to a method, apparatus and system for estimating the coordinates of the bucket tooth tip, an excavator and a storage medium. The method includes: establishing a kinematic model of an excavator according to dimensions of a plurality of components of the excavator, and obtaining measured values of the coordinates of the bucket tooth tip of the excavator according to angles of the plurality of components of the excavator measured by excavator sensors with the kinematic model; obtaining a system noise and a measurement noise of the coordinates of the bucket tooth tip of the excavator; and determining estimated values of the coordinates of the bucket tooth tip of the excavator according to the measured values of the coordinates of the bucket tooth tip of the excavator, the system noise and the measurement noise of the coordinates of the bucket tooth tip of the excavator.