Skylight Sensor Positioning System for Autonomous Navigation
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Solution Overview
Problem
Current GPS-based positioning technologies face limitations such as high power consumption, signal loss issues, inability to provide compass data without movement, and high component costs, which hinder their reliability and efficiency in autonomous systems like self-driving cars and IoT devices.
Innovation Solution
A system and method that utilize skylight sensor data to determine position, orientation, and time by generating environment data from celestial light sources, allowing for calculation of these variables without relying on external GPS devices, and can function as an auxiliary system when GPS signals are unavailable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If GPS-based positioning technology is used, then positioning accuracy is improved, but power consumption increases
Solution Approach 1:
The system performs GPS signal acquisition periodically rather than continuously. The GPS receiver is activated at specific intervals to acquire satellite signals and update position data, then enters a low-power state between acquisitions. This periodic operation maintains positioning accuracy while significantly reducing power consumption compared to continuous tracking.
2Measurement precision
If GPS receiver continuously tracks satellite signals, then positioning accuracy is maintained, but battery drainage increases
Solution Approach 1:
The system implements periodic GPS signal acquisition instead of continuous tracking. The GPS receiver is activated at predetermined intervals to acquire satellite signals, process position data, and then enter sleep mode. This approach maintains acceptable positioning accuracy while dramatically reducing battery drainage by minimizing the active operation of power-intensive GPS components.
3Reliability
If GPS signal re-acquisition is performed after signal loss, then positioning reliability is restored, but time delay increases
Solution Approach 1:
The system performs preliminary actions to prepare for rapid GPS signal re-acquisition. Before signal loss occurs, the system maintains ready-state data including almanac and ephemeris information in memory. When signal loss is detected, the receiver can quickly re-acquire satellites using this pre-loaded data, significantly reducing the time delay compared to starting from a cold state.
4Adaptability or versatility
If GPS components are used, then positioning functionality is provided, but device cost increases
Solution Approach 1:
The system implements multi-functionality by integrating GPS positioning with inertial navigation capabilities. The same hardware platform supports both GPS-based positioning and inertial dead-reckoning navigation, allowing the device to provide positioning functionality through multiple methods. This reduces dependency on expensive dedicated GPS components while maintaining versatile positioning capabilities.
5Measurement precision
If GPS provides compass data, then orientation information is obtained, but movement is required
Solution Approach 1:
The system replaces the mechanical movement requirement of GPS-based compass with an inertial measurement unit (IMU) that uses accelerometers and gyroscopes to determine orientation. The IMU calculates device orientation by measuring acceleration vectors and rotational rates, providing compass data without requiring any physical movement of the device, thus maintaining accuracy while eliminating the operational constraint.
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 efficient and cost-effective determination of location and orientation, provides fast lock-in positional systems, and assists GPS tracking and compass functions, reducing power consumption and component costs while maintaining accuracy.
Implementation Method 1
A system and method, which generate environment data from skylight sensor data. The environment data includes a value of a geospatially dependent parameter associated with light received from a predetermined celestial light source.
Data Source
AI summary
A system and method include generating environment data from skylight sensor data. The environment data includes a value of a geospatially dependent parameter associated with light received from a predetermined celestial light source. At least two of a compass direction of the predetermined celestial light source when the skylight sensor data was received, a time at which the skylight sensor data was received, or a geospatial coordinate at which the skylight sensor data was collected are received. At least one of the compass direction of the predetermined celestial light source when the skylight sensor data was received, the time at which the skylight sensor data was received, or the geospatial coordinate at which the skylight sensor data was collected is determined, at least in part, from the environment data.