Movable Module Position and Orientation Estimation with Joint Constraints
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Magnetometer measurements in work machines with large metal masses are often unreliable due to magnetic field distortions, leading to inaccurate state estimates of module positions and orientations, which can result in drifting configurations.
Innovation Solution
Incorporate kinematic relationships into state estimation by determining pairs of reference and 'measured' vectors representing joint constraints, which are used to correct sensor data from inertial sensors, thereby minimizing errors in position and orientation calculations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If magnetometer measurements are used for state estimation, then position and orientation can be determined, but magnetic field distortions from large metal masses cause measurement errors and drifting configurations
Solution Approach 1:
The patent extracts and removes the harmful magnetic field distortions from the state estimation process by identifying and eliminating magnetometer measurements that are affected by large metal masses. The system selectively discards corrupted magnetic field data while retaining reliable inertial sensor measurements, thereby extracting only the useful measurement components free from distortion.
Solution Approach 2:
The patent applies local quality by treating different sensor measurements differently based on their local reliability. Inertial sensors are weighted more heavily in regions where magnetic field distortions are present, while magnetometer data is used only in regions where the magnetic field is relatively undistorted. This spatially varying quality weighting optimizes the fusion of heterogeneous sensor data.
2Measurement precision
If multiple sensor types are fused for state estimation, then measurement accuracy can be improved, but sensor data drifts and inconsistencies increase due to different error characteristics
Solution Approach 1:
The patent implements feedback through an iterative optimization process that continuously adjusts the weighting of different sensor measurements based on their consistency with the kinematic model. The state estimation algorithm processes sensor data in temporal sequence, using past estimation results to inform current measurements, and automatically corrects drift by referencing the known kinematic relationships between body segments.
Solution Approach 2:
The patent merges inertial sensor measurements with magnetometer data through a unified state estimation framework that processes both sensor types simultaneously. The kinematic model serves as a bridge that combines these heterogeneous measurements into a consistent state estimate, merging the complementary strengths of each sensor type while compensating for their individual weaknesses through the constraints of the kinematic model.
3Measurement precision
If kinematic relationships are incorporated into state estimation, then measurement accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the state estimation computation into modular processing steps that handle different aspects of the problem separately. The system divides the kinematic model into discrete body segments with defined relationships, processes sensor measurements segment by segment, and applies optimization iteratively to each segment. This modular segmentation reduces the overall computational complexity by breaking down the complex estimation problem into manageable sub-problems.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
The invention relates to a method for state estimation of position and orientation of a plurality of modules of a common system, which are movable relative to one another via joints (J1, J2), by means of inertial sensors (S1, S2) that are arranged on the modules. At least one vector pair ( si n mess,i , w n ref,i or si o mess,i , w o ref,i ) is determined (100, 101, 110, 111), which represents kinematic relationships of at least one of the joints (J1, J2) and of the two modules connected to the joint (J1, J2), and which is included in the state estimation.