Autonomous Lighting Fixture Grouping Using Range and Bearing
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Solution Overview
Problem
Existing lighting systems in buildings lack an efficient method for autonomously organizing light fixtures into logical groups based on factors such as co-location, layout, and environmental conditions, leading to suboptimal control and operation.
Innovation Solution
Light fixtures equipped with sensors use optical modules and/or infrared LEDs to detect neighboring fixtures, determining range and bearing, and employ an iterative least squares method to self-organize into groups, optimizing control based on co-location, layout, and environmental data, with distributed algorithms facilitating this process without a central aggregator.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If light fixtures are controlled individually by wall-mounted switches, then each fixture can be operated independently, but the system lacks autonomous organization and requires manual control configuration
Solution Approach 1:
Light fixtures autonomously determine their own grouping by detecting neighboring fixtures through optical modules and infrared LEDs, measuring range and bearing without central coordination. Each fixture independently participates in distributed algorithms to self-organize into logical groups based on spatial relationships and environmental conditions.
Solution Approach 2:
The patent replaces manual mechanical configuration (physical switch wiring and manual group assignment) with optical and electromagnetic fields. Optical modules and infrared LEDs create invisible spatial maps, while radio frequency communications transmit positioning data, substituting physical installation complexity with wireless autonomous organization.
2Measurement precision
If light fixtures are grouped by physical location, then control efficiency is improved, but installation orientation uncertainties cause bearing measurement errors
Solution Approach 1:
The system employs iterative feedback where fixtures repeatedly exchange bearing and range measurements with neighbors, refining spatial position estimates through distributed least squares algorithms. Each fixture continuously adjusts its understood position based on feedback from multiple neighbors, compensating for initial installation orientation errors.
Solution Approach 2:
During installation, fixtures perform preliminary autonomous discovery by detecting neighboring fixtures and establishing initial spatial relationships before formal grouping occurs. This preliminary action creates a rough spatial map that is subsequently refined through iterative algorithms, eliminating the need for precise manual orientation alignment.
3Extent of automation
If distributed algorithms are used without a central aggregator, then system autonomy is enhanced, but communication overhead increases
Solution Approach 1:
The patent segments the control system into autonomous fixture-level decision units, where each fixture independently processes local neighbor information and participates in distributed consensus algorithms. This segmentation eliminates the need for centralized data aggregation, reducing communication overhead by processing information locally rather than transmitting all data to a central controller.
Solution Approach 2:
Each light fixture performs partial computation by calculating its position and grouping based only on measurements from immediately neighboring fixtures rather than processing data from all fixtures in the building. This partial action reduces communication energy requirements while achieving sufficient grouping accuracy through iterative refinement.
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 system enables efficient, autonomous grouping of light fixtures into logical configurations that respond to control signals in unison, optimizing illumination based on occupancy and environmental conditions, enhancing lighting control and energy efficiency.
Implementation Method 1
a sensor can be used to detect neighboring light fixtures to determine a range and bearing for the neighboring light fixture using optical modules and/or infrared LEDs
Implementation Method 2
a neighboring light fixture may vary the output to be detected and a range and bearing measurement may be determined based on a detected pattern and/or light intensity
Data Source
AI summary
An example of an apparatus is provided. The apparatus includes a communications interface to receive data from a plurality of lighting devices. The data from a lighting device includes a distance and a bearing for neighboring lighting devices of the lighting device. The neighboring lighting devices are selected from the plurality of lighting devices. In addition, the apparatus includes a memory storage unit to store the data received via the communications interface, wherein the data is to be stored in a database. Furthermore, the apparatus includes an aggregator to generate aggregated data for each lighting device from the data in the database. The aggregated data includes data from the plurality of lighting devices. The apparatus also includes a processor to classify each lighting device of the plurality of lighting devices in a group selected from a plurality of groups based on the aggregated data.