Mobile Robot Localization Using Projected Pixel Coordinates
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Solution Overview
Problem
Conventional mobile robot localization and navigation systems face challenges in quasi-unstructured environments due to accuracy, resolution, and computational complexity issues, and are often tailored to specific settings, limiting their effectiveness in real-world scenarios.
Innovation Solution
An indoor localization and navigation system for mobile robots that uses a projector to encode pixel coordinates in a temporal light signal, detected by stationary sensor nodes and processed by an onboard computer to determine location and orientation, combined with a global navigation module for path planning, utilizing a combination of local and global positioning strategies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional localization systems are used, then system simplicity is maintained, but measurement precision and location accuracy deteriorate
Solution Approach 1:
The patent introduces sensor nodes as intermediary elements deployed in the environment to detect robot position. These nodes receive light signals from the projector, determine which projector pixels are active, and communicate this information to the robot. This intermediary system enables accurate localization without requiring complex onboard sensors on the robot itself, thus improving measurement precision while keeping the robot relatively simple.
Solution Approach 2:
The patent replaces traditional mechanical or complex electronic localization systems with an optical-based approach. A projector emits light signals that encode position information, and sensor nodes detect these optical signals to determine robot location. This substitution of mechanical/electronic systems with optical fields enables simpler hardware while achieving high measurement precision through the encoded light patterns.
2Measurement precision
If high-resolution localization is implemented, then location accuracy improves, but computational complexity increases
Solution Approach 1:
The patent pre-encodes position information into the light signal pattern before transmission. Each projector pixel corresponds to a specific location, and the activation pattern of pixels directly encodes spatial information. This preliminary encoding of position data in the optical domain eliminates the need for complex computational algorithms during runtime, as the robot can determine its location by simply detecting which pixels are active, thus achieving high resolution without proportional increases in computational complexity.
Solution Approach 2:
The patent creates a simplified copy of the environment's spatial structure through the projector's pixel activation pattern. Rather than computing complex 3D spatial relationships, the system projects a 2D pattern of active pixels that directly corresponds to the robot's position. This copying approach transforms complex spatial computation into a simple pattern-matching task, maintaining high location resolution while minimizing computational requirements.
3Measurement precision
If environment-specific customization is applied, then localization accuracy in that environment improves, but system adaptability deteriorates
Solution Approach 1:
The patent creates a universal localization system where sensor nodes can be deployed in various environments without customization. The same projector-sensor node-robot architecture works across different settings, whether indoor corridors, open spaces, or structured environments. The system adapts to different environments through the deployment density and arrangement of sensor nodes rather than requiring environment-specific configuration, thus maintaining both accuracy and adaptability.
Solution Approach 2:
The patent divides the environment into discrete zones covered by individual sensor nodes, each independently detecting projector pixel patterns. This segmentation allows the system to scale to different environment sizes and configurations without requiring overall system redesign. Each sensor node operates independently and contributes to the global localization accuracy, enabling the system to adapt to various environments by simply adding or removing nodes rather than customizing the entire system.
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 efficient navigation of mobile robots in quasi-unstructured environments by providing both coarse and fine-grained location information, compensating for the limitations of conventional systems and allowing for various application requirements in terms of accuracy and resolution.
Implementation Method 1
a projector mounted on the mobile robot and configured to project a temporal projector light signal
Implementation Method 2
a stationary sensor node comprising a light sensor configured to detect the temporal projector light signal and generate a sensor signal
Data Source
AI summary
An indoor localization and navigation system for a mobile robot, the system comprising: a projector mounted on the mobile robot and configured to project a temporal projector light signal, wherein the temporal projector light signal is encoded, for each pixel of the projector, with an information segment comprising the pixel coordinates of the each pixel of the projector; a stationary sensor node comprising a light sensor configured to detect the temporal projector light signal and generate a sensor signal and a transmitter configured to transmit a sensor node identifier and a position code generated based on the sensor signal; a receiver mounted on the mobile robot and configured to receive the sensor node identifier and the position code from the transmitter; and an onboard computer mounted on the mobile robot and operatively coupled to the projector and the receiver, wherein the onboard computer is configured to receive the sensor node identifier and the position code from the receiver and to determine a location information of the mobile robot based on the received sensor node identifier and the position code.


