Autonomous Object Location in Mobile Frames Using Accelerometer Models
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Solution Overview
Problem
Existing location systems fail to accurately locate objects within enclosed environments, such as vehicles, without external infrastructure, and inertial navigation systems require calibration and suffer from accuracy drift over time.
Innovation Solution
The use of 3-axis accelerometer sensor data to determine vehicle maneuvers and apply this data to a mathematical model trained with reference accelerometer data to estimate the object's location within a mobile reference frame, eliminating the need for external communication systems like GPS.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external infrastructure (transmitters, receivers, GPS) is used for location determination, then location accuracy is improved, but device complexity and dependency on external systems increases
Solution Approach 1:
The system uses the object's own accelerometer to determine its location within the vehicle. The accelerometer data is processed through a mathematical model that was trained with reference data from multiple locations in the vehicle, enabling the object to self-determine its position without external infrastructure.
Solution Approach 2:
The patent replaces external electromagnetic positioning systems (GPS, radio, ultrasound) with an inertial sensing approach using an accelerometer. This mechanical sensing system processes acceleration data to infer location based on the vehicle's maneuver characteristics, substituting complex external infrastructure with a simpler onboard sensor.
2Adaptability or versatility
If inertial navigation systems with accelerometers are used, then external infrastructure dependency is reduced, but accuracy drifts over time requiring periodic re-calibration
Solution Approach 1:
The system performs preliminary training by collecting accelerometer data from reference locations within the vehicle during various maneuvers. This training data is used to create a mathematical model that maps acceleration patterns to spatial locations. During operation, the pre-trained model enables accurate location determination without drift, as it compares current acceleration data against the established reference patterns rather than integrating acceleration over time.
3Extent of automation
If enclosed environment location systems are implemented, then location determination in vehicles is enabled, but power consumption and cost increase
Solution Approach 1:
The system uses a low-cost, simple accelerometer that can be easily integrated into the object. Rather than using power-intensive external communication systems or complex onboard infrastructure, the patent relies on a inexpensive sensor that consumes minimal power while providing sufficient data for location determination through the trained mathematical model.
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
Enables accurate and autonomous object location within vehicles without external infrastructure, maintaining accuracy over time and allowing for functionality adjustments based on location, such as disabling phone operations, with low power and cost requirements.
Implementation Method 1
Sensor data may therefore be received from an accelerometer associated with an object
Data Source
AI summary
An apparatus and method for estimating a location of an object within a mobile reference frame. Sensor data is received from an accelerometer associated with an object followed by determining from the sensor data that the mobile reference frame is executing one of a set of predetermined maneuvers. In response to such determination: (1) the sensor data is applied to a mathematical model associated with the executed maneuver, the model trained with previously obtained data from one or more reference accelerometers positioned at known locations within the mobile reference frame and estimating the location of the object by applying the sensor data to the mathematical model; and/or (2) incorporating reference accelerometers in the mobile reference frame and comparing the sensor data with the reference accelerometers and estimating the location of the object.