Robot Fall Prediction Using Gravity Offset and Acceleration
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Solution Overview
Problem
Current robot fall prediction technologies rely solely on gravity center offset or acceleration values, leading to high misjudgment rates and increased hardware costs, resulting in inaccurate and non-real-time predictions that can cause damage or casualties.
Innovation Solution
A computer-implemented method that uses a weighted value of the robot's center of gravity, obtained through position sensors and gyroscopes, combined with acceleration data from sensors to predict falls, reducing hardware costs and improving real-time accuracy by employing decision tree parameters and correlation factors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensor parameters are combined for fall prediction, then prediction accuracy is improved, but hardware cost increases
Solution Approach 1:
The patent extracts and utilizes only the necessary sensor parameters (acceleration values from existing acceleration sensors and gravity center offset from existing gravity center detection mechanisms) that are already present in typical robot systems, rather than combining multiple additional sensor parameters. This extraction approach maintains prediction accuracy while avoiding increased hardware costs.
Solution Approach 2:
The patent makes existing sensors serve multiple functions: acceleration sensors are used not only for motion detection but also for fall prediction, and gravity center detection mechanisms are utilized for both balance control and fall prediction. This multi-functionality approach improves prediction accuracy without requiring additional specialized hardware.
2Measurement precision
If multiple sensor parameters are combined for fall prediction, then prediction accuracy is improved, but real-time performance deteriorates
Solution Approach 1:
The patent extracts and processes only two essential parameters (acceleration values and gravity center offset) that can be obtained and processed rapidly from existing sensors, rather than combining multiple complex sensor parameters. This streamlined extraction approach ensures prediction accuracy is maintained while real-time performance is preserved.
Solution Approach 2:
The patent applies partial action by focusing on the most critical parameters for fall prediction (acceleration and gravity center offset) rather than processing all available sensor data. This selective approach achieves sufficient prediction accuracy while maintaining real-time performance through reduced computational load.
3Device complexity
If only gravity center offset or acceleration value is used for fall prediction, then hardware cost is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The patent merges the information from two existing sensor sources (acceleration values from acceleration sensors and gravity center offset from gravity center detection mechanisms) into a unified fall prediction approach. This merging of data from existing hardware maintains cost-effectiveness while improving prediction accuracy through complementary information fusion.
Solution Approach 2:
The patent enables existing acceleration sensors and gravity center detection mechanisms to serve dual purposes: their primary functions plus fall prediction. This multi-functionality approach improves prediction accuracy by utilizing data from existing hardware without requiring additional specialized sensors or increasing hardware costs.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The method effectively reduces hardware costs and enhances the accuracy and real-time performance of robot fall predictions, preventing potential damage and casualties by accurately determining the likelihood and direction of a robot's fall.
Implementation Method 1
obtain a weighted value of a center of gravity of a robot corresponding to a posture of the robot
Implementation Method 2
correcting an acceleration of the robot based on an offset direction of the center of gravity of the robot
Data Source
AI summary
The present disclosure is applicable to robot technology. A method for robot fall prediction, and a robot are provided. The method includes: searching a weighted value of a center of gravity of the robot corresponding to a posture of the robot, according to a preset first corresponding relationship; correcting an offset of the center of gravity of the robot based on the weighted value of the center of gravity of the robot; correcting an acceleration of the robot based on an offset direction of the center of gravity of the robot; and determining whether the robot will fall based on the corrected offset of the center of gravity, the offset direction of the center of gravity, and the corrected acceleration of the robot. The present disclosure improves the real-time performance and accuracy of the prediction for the fall of a robot through the fusion calculation of various data.