Robot Orientation Determination Using Outlier-Free Sensor Data
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Solution Overview
Problem
Existing methods for determining the position of autonomous robots are prone to inaccuracies due to incorrect measurements and ambiguities in position data, which can lead to increased inaccuracy in navigation and movement control.
Innovation Solution
A method that compares expected position data from one sensor device with actual position data from another sensor device using a similarity matrix and cluster analysis to detect outliers, ensuring only consistent and accurate position data is used for further determination, thereby increasing the accuracy and reliability of the robot's position.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If position data from sensor devices are used for robot navigation, then the robot can determine its position and navigate autonomously, but measurement inaccuracies and ambiguities in the position data lead to reduced navigation accuracy
Solution Approach 1:
The patent combines position data from multiple sensor devices (e.g., odometry sensors, RFID readers, cameras) to determine robot position. By merging data from different sensing modalities, the system compensates for individual sensor inaccuracies and ambiguities, thereby improving both measurement precision and reliability simultaneously
Solution Approach 2:
The system uses feedback mechanisms where position determination results are continuously refined by comparing expected position (from odometry) with actual position (from RFID or camera-based localization). This closed-loop feedback allows the system to correct measurement errors and maintain high accuracy over time
2Measurement precision
If multiple sensor devices are used to improve position accuracy, then measurement precision increases, but device complexity and data processing requirements increase
Solution Approach 1:
The system segments the sensing function into specialized sensor devices, each optimized for specific tasks (odometry for motion tracking, RFID for location identification, cameras for visual localization). This segmentation allows each sensor to operate independently at its optimal performance level while simplifying the overall system architecture through functional specialization
Solution Approach 2:
The control device is designed with multi-functionality to handle various sensor data types and processing algorithms. It can selectively activate different sensor devices and processing methods based on environmental conditions, thereby managing complexity while maintaining high measurement precision across diverse operating scenarios
Data Source
AI summary
The disclosure relates to a method for determining the orientation of a robot and comprises at least one method step in which expected orientation data (I) pertaining to an expected orientation of the robot are provided and a method step in which actual orientation data (II) pertaining to an actual orientation of the robot are provided. According to the disclosure, one method step involves the expected orientation data (I) and the actual orientation data (II) being used to ascertain outlier-free orientation data that are used for determining the orientation of the robot. The disclosure further relates to a robot and to an orientation determination apparatus.