Autonomous Mobile Lift Detection Using Multi-Sensor Thresholds
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Solution Overview
Problem
Traditional contact sensors used in autonomous mobile devices (AMDs) for lift detection are costly, complex, and prone to errors, including false detections and user interference, which increases the likelihood of injury or damage.
Innovation Solution
The use of multiple sensors such as accelerometers, gyrometers, and cliff sensors to determine lift events, eliminating the need for additional hardware and enhancing accuracy across various scenarios by setting scenario-specific thresholds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional contact sensors are used for lift detection, then lift detection capability is provided, but cost and device complexity increase
Solution Approach 1:
The patent applies multi-functionality by using existing sensors (accelerometer, gyrometer, cliff sensors) that serve multiple purposes - navigation, obstacle detection, and lift detection - thereby eliminating the need for dedicated contact sensors and reducing device complexity while maintaining lift detection capability
Solution Approach 2:
The patent combines data from multiple existing sensors to perform lift detection, merging their functions rather than using a separate dedicated sensor. This integration approach reduces overall device complexity while achieving reliable lift detection through composite sensor data analysis
2Reliability
If traditional contact sensors are used for lift detection, then lift detection is achieved, but manufacturing cost increases
Solution Approach 1:
By making existing sensors multi-functional for lift detection in addition to their primary functions, the patent eliminates the need for additional dedicated sensors, thereby reducing bill of materials cost and manufacturing expenses while maintaining accurate lift detection
Solution Approach 2:
The system uses its existing sensor suite to perform lift detection without requiring external or additional specialized components, allowing the device to serve its own detection needs using already-integrated sensors, thus reducing manufacturing cost
3Reliability
If traditional contact sensors are used for lift detection, then basic lift detection is provided, but false detections and user interference occur
Solution Approach 1:
The patent implements feedback by continuously monitoring data from multiple sensors and using scenario-specific thresholds to validate lift events. The system cross-references accelerometer, gyrometer, and cliff sensor data to confirm genuine lift events while filtering out false positives and user interference through multi-parameter validation
Solution Approach 2:
The system dynamically adjusts detection thresholds based on identified scenarios (e.g., navigation mode, charging mode, user interaction mode), allowing adaptive response to different operational contexts. This dynamic threshold adjustment enables the system to distinguish between genuine lift events and user interference by considering the current operational state
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
This approach provides more accurate and consistent lift detection, reduces user interference, and lowers the overall cost and complexity of the AMD, while preventing false detections and enhancing safety.
Implementation Method 1
The processor(s) may use sensor data from an accelerometer
Implementation Method 2
The processor(s) may use sensor data from a gyrometer
Implementation Method 3
The cliff sensors may be time-of-flight sensors that have a field of view directed downward toward a floor
Data Source
AI summary
An autonomous mobile device (AMD) may move around while performing tasks. If the AMD is lifted, the AMD responds to ensure safety of the user, modify ongoing operation, and so forth. For example, the AMD may stop the wheels, retract a mast, or suspend navigation tasks. To accurately determine whether or not the AMD has been lifted, the AMD uses one or more sensors to determine a vertical lift distance and rotation of the AMD with respect to one or more axes. For example, while the AMD is stationary, the sensors used may include an accelerometer or a gyrometer. While the AMD is moving, data from time-of-flight sensors or one or more cameras may also be used. If the AMD is stationary low-level sensor thresholds are used. If the AMD is moving steadily, medium-level sensor thresholds are used. If the AMD is suddenly decelerating, high-level sensor thresholds are used.