Self-Localization Using Distributed UWB Signals for Low-Latency Robots
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Solution Overview
Problem
Current UWB localization systems for mobile robots suffer from high communication latency, signal interference, and limited scalability, making them unsuitable for safety-critical applications and environments where GPS is unreliable or inoperative.
Innovation Solution
A self-localizing apparatus that receives timestampable UWB signals to determine its own location without emitting signals, using a distributed localization system with timestampable signals and UWB technology to achieve accurate, real-time localization in 2D or 3D space, even in environments without direct line of sight.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a centralized UWB localization system with tags emitting signals is used, then asset tracking capability is provided, but communication latency and system robustness deteriorate for mobile robot applications
Solution Approach 1:
Instead of having mobile robots emit UWB signals and have their locations computed by a central server, the patent inverts the approach by equipping the environment with emitting transceivers and having mobile robots receive signals to compute their own locations. This eliminates the need for robots to transmit signals and reduces dependency on central server communication, thereby reducing latency and improving robustness.
Solution Approach 2:
The mobile robot is enabled to determine its own location autonomously by receiving UWB signals from transceivers and computing its position using its own processor. This self-localization capability eliminates the need for continuous communication with the central server for location updates, reducing communication latency and improving system robustness.
2Measurement precision
If multiple UWB tags emit signals simultaneously in a centralized system, then localization coverage is improved, but signal interference and network traffic load increase
Solution Approach 1:
The patent inverts the traditional UWB localization architecture by having fixed transceivers in the environment emit signals instead of mobile tags transmitting signals. This allows multiple transceivers to emit signals simultaneously without causing interference to each other, as the receiving robots process signals independently to determine their own locations.
Solution Approach 2:
Each mobile robot independently receives UWB signals from multiple transceivers and computes its own location using its onboard processor. This independent signal processing eliminates the network traffic conflicts that would occur if multiple robots tried to transmit signals simultaneously, allowing high localization accuracy without signal interference.
3Loss of information
If a centralized server computes all location data, then centralized database updates are achieved, but communication overhead and system scalability worsen
Solution Approach 1:
Each mobile robot is equipped with a processor that independently computes its location by receiving UWB signals from transceivers and processing the signal data locally. This distributed computation approach eliminates the need for a centralized server to compute all location data, reducing communication overhead and simplifying the system architecture while maintaining location data availability.
Solution Approach 2:
The centralized localization computation function is segmented and distributed to individual mobile robots. Each robot performs its own location computation independently rather than relying on a central server, which reduces the communication burden on the server and simplifies the overall system architecture.
4Measurement precision
If mobile robots transmit UWB signals for localization, then location determination is enabled, but energy consumption and device complexity increase
Solution Approach 1:
Mobile robots determine their locations by receiving UWB signals from fixed transceivers and processing these signals with their onboard processors. This approach eliminates the need for robots to transmit UWB signals, significantly reducing their energy consumption while maintaining accurate location determination capability.
Solution Approach 2:
Instead of mobile robots transmitting UWB signals to determine their locations, the patent inverts the approach by having fixed transceivers transmit signals that robots receive and process. This inversion eliminates the energy-intensive transmission function from mobile robots while preserving accurate location determination.
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 provides high update rates, increased accuracy, and improved robustness by allowing localization without direct line of sight, reducing susceptibility to interference, and enabling scalable, secure, and efficient operation in environments where GPS is unreliable.
Implementation Method 1
A self-localizing apparatus that receives timestampable UWB signals to determine its own location without emitting signals
Implementation Method 2
using a distributed localization system with timestampable signals and UWB technology to achieve accurate, real-time localization
Data Source
AI summary
A self-localizing apparatus uses timestampable signals transmitted by transceivers that are a part of a distributed localization system to compute its position relative to the transceivers. Transceivers and self-localizing apparatuses are arranged for highly accurate timestamping using digital and analog reception and transmission electronics as well as one or more highly accurate clocks, compensation units, localization units, position calibration units, scheduling units, or synchronization units. Transceivers and self-localizing apparatuses are further arranged to allow full scalability in the number of self-localizing apparatuses and to allow robust self-localization with latencies and update rates useful for high performance applications such as autonomous mobile robot control.