Robot Localization Using Wi-Fi Fingerprints Without LiDAR
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Solution Overview
Problem
Current robot localization methods are ineffective when robots experience unexpected movements, particularly in environments lacking distinct surrounding structures, and require expensive sensors like lidar for accurate positioning.
Innovation Solution
A method using communication environment information, such as access point identifiers and signal strengths, to generate an environmental profile, compare it with learning profiles, and determine the robot's position without relying on surrounding structures, utilizing a probability map and potentially incorporating beacon or visible light communication signals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expensive sensors like lidar are used to accurately measure distance with respect to nearby objects, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces expensive lidar sensors with inexpensive wireless communication modules that can be found in most mobile devices. Instead of using specialized measurement equipment, the system utilizes readily available Wi-Fi or Bluetooth communication components to achieve localization functionality at low cost.
Solution Approach 2:
The patent substitutes the mechanical/optical measurement system (lidar) with an electromagnetic field-based system (wireless communication). By measuring signal strength and using fingerprinting techniques, the system achieves positioning without mechanical moving parts or complex optical components.
2Measurement precision
If surrounding structures are used for robot localization, then localization accuracy is improved, but adaptability to different environments worsens
Solution Approach 1:
The patent creates a universal localization system that works across diverse environments by using wireless signal fingerprinting rather than structure-dependent methods. The system can operate in open spaces, corridors, rooms with various furnishings, and other environments where traditional visual or structural recognition fails.
Solution Approach 2:
The patent changes the localization parameter from structural features (which vary by environment) to wireless signal characteristics (which are universally available). By measuring signal strength, signal-to-noise ratio, and other communication parameters, the system achieves environment-independent localization.
3Measurement precision
If complex algorithms are used to match local map with sensor data, then localization accuracy is improved, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-collecting wireless signal characteristics at various locations and storing them as fingerprint data before localization is needed. This offline phase creates a reference database that enables rapid online positioning without complex real-time matching algorithms.
Solution Approach 2:
The patent creates simplified copies of the environment in the form of signal strength maps and fingerprint databases. Instead of processing complex 3D point clouds or images in real-time, the system compares measured signal patterns against pre-stored fingerprint copies, dramatically reducing processing time.
Data Source
AI summary
A robot and a method for localizing a robot are disclosed. The method for localizing a robot may include acquiring communication environment information including identifiers of access points and received signal strengths from the access points, generating an environmental profile for a current position of the robot based on the acquired communication environment information, comparing the generated environmental profile with a plurality of learning profiles associated with a plurality of regions, respectively, determining a learning profile corresponding to the environmental profile, based on the comparison, and determining a region associated with the determined learning profile as a current position of the robot. In a 5G environment connected for the Internet of Things, embodiments of the present disclosure may be implemented by executing an artificial intelligence algorithm and/or machine learning algorithm.