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Mobile Robot Positioning with Wireless Fidelity Fingerprinting and Explainable Artificial Intelligence.

Hüseyin Abacı¹, Ahmet Çağdaş Seçkin¹

¹Computer Engineering Department, Engineering Faculty, Adnan Menderes University, 09100 Aydın, Türkiye.

Sensors (Basel, Switzerland)

|January 8, 2025

Summary

This study presents a machine learning method for accurate indoor robot positioning using Wi-Fi signals. The AdaBoost algorithm achieved high precision, even with fewer access points, demonstrating a cost-effective solution for indoor navigation.

Keywords:

explainable artificial intelligence internet of things machine learning mobile robot positioning wireless fidelity fingerprinting

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Area of Science:

Robotics
Indoor Navigation
Machine Learning

Background:

Wireless Fidelity (Wi-Fi) positioning is a cost-effective solution for indoor robot navigation.
Wi-Fi offers advantages over Global Navigation Satellite System (GNSS) for indoor environments due to signal characteristics.
Existing Wi-Fi infrastructure enables precise indoor positioning without additional hardware.

Purpose of the Study:

To propose a machine learning-based method for Wi-Fi-enabled robot positioning in indoor environments.
To achieve comprehensive 3D position estimation using existing Wi-Fi infrastructure.
To evaluate the accuracy and efficiency of the proposed method.

Main Methods:

Utilized machine learning, specifically the AdaBoost algorithm, for Wi-Fi signal strength-based positioning.
Collected a dataset of Wi-Fi access point signal strengths (dBm) across a four-floor building.
Employed explainable artificial intelligence to analyze access point importance and reduce data requirements.

Main Results:

The AdaBoost algorithm achieved high accuracy with mean average errors (MAE) of 0.044m (x-axis), 0.063m (y-axis), and 0.003m (z-axis).
Positioning remained accurate even when using data from only seven selected Wi-Fi access points, with MAE values of 0.811m (x-axis), 0.492m (y-axis), and 0.134m (z-axis).
Demonstrated the feasibility of accurate indoor positioning with reduced Wi-Fi access point usage.

Conclusions:

The proposed machine learning approach effectively utilizes Wi-Fi infrastructure for accurate indoor robot positioning.
AdaBoost algorithm shows strong performance in 3D position estimation, offering a practical solution for indoor navigation.
The study highlights the potential for optimized Wi-Fi usage in indoor positioning systems.