Indoor Localization for Autonomous Robot Navigation

TL;DR

This paper explores using indoor positioning systems with machine learning to enable autonomous robots to navigate indoors, demonstrating promising results with a custom dataset and path-planning algorithm.

Contribution

It introduces a novel approach combining IPSs, ML models, and A* path planning for autonomous indoor robot navigation, with experimental validation.

Findings

Robot navigates corners successfully 50% of the time

Machine learning models can predict directions from RSSI data

Indoor positioning shows promise for autonomous robot navigation

Abstract

Indoor positioning systems (IPSs) have gained attention as outdoor navigation becomes prevalent in everyday life. Research is being actively conducted on how indoor smartphone navigation can be accomplished and improved using received signal strength indication (RSSI) and machine learning (ML). IPSs have more use cases that need further exploration, and we aim to explore using IPSs for the indoor navigation of an autonomous robot. We collected a dataset and trained models to test on a robot. We also developed an A* path-planning algorithm so that our robot could navigate itself using predicted directions. After testing different network structures, our robot was able to successfully navigate corners around 50 percent of the time. The findings of this paper indicate that using IPSs for autonomous robots is a promising area of future research.