# Indoor Navigation with Ultra‑Wideband Iiwari Technology, 5G and AI - Part 1 of 2

> Mobile Robot Indoor Navigation with Ultra Wideband (UWB) Technology. Motivation and UWB rationale.

## Overview
This article describes the development of a system demonstrating the potential of ultra-wideband (UWB) technology for the indoor navigation of a mobile robot. The solution combines Iiwari UWB positioning with a custom navigation controller and a 5G-connected AI obstacle detection model. This work was implemented as part of the AuToTIE project.

## The Navigation Challenge
The project aimed to achieve reliable localization for a robot operating within a large, multi-floor, multi-building environment. While outdoor robots rely on GPS, indoor environments require alternative solutions. Wi-Fi and Bluetooth often lack the necessary precision and stability, and local LiDAR-based SLAM can struggle to maintain absolute global consistency across large, complex spaces. The goal was to create a system that remains stable in dynamic campus environments, allowing for navigation near workstations while safely responding to obstacles.

## Ultra-Wideband (UWB) Implementation
UWB technology was selected for its ability to provide decimetre-level indoor positioning by measuring the time-of-flight of short radio pulses. 

*   **Configuration:** Two Iiwari UWB tags were mounted on a Clearpath Dingo-D robot (front and back).
*   **Data Processing:** The system delivers coordinates at approximately 10 Hz with noise levels around ±20 cm.
*   **Integration:** These coordinates are fed into a navigation node running on ROS Noetic, with fused odometry used to assist with smooth rotation.

## Comparison with LiDAR-based Navigation
While LiDAR-based navigation is a mature technology, it presented several practical drawbacks for this project:
*   Requires extensive mapping and tuning of costmaps and planners for each specific environment.
*   Susceptibility to noise from glass walls, reflective surfaces, and furniture.
*   Lack of portability; moving the robot to a new area often requires starting the mapping process from scratch.

By using a UWB-centric stack, the architecture remains simpler, easier to teach, and more transferable between different rooms sharing the same UWB infrastructure.

## AI Obstacle Detection and 5G Connectivity
To ensure safety in dynamic situations, an AI model was integrated to detect obstacles. The model communicates with the robot over a Nokia 5G connection. The use of 5G ensures that the control loop remains smooth and responsive, providing a significant performance improvement over standard Wi-Fi.

## Project Credits and Information
*   **Authors:** Md Al-Naim (md.al-naim@savonia.fi), Asmo Jakorinne (asmo.jakorinne@savonia.fi)
*   **Supervision:** Premton Canamusa (Savonia UAS), Asmo Jakorinne (Savonia UAS), Teemu Lätti (Iiwari Tracking Solutions)
*   **Development:** Md Al-Naim (Savonia DigiCenter North Savo)
*   **Project:** Automaatio- ja Tekoäly Tiedoksi -hanke (AuToTIE), Pohjois-Savon Liitto, EAKR
*   **Robot Platform:** Clearpath Dingo-D
*   **Location:** Savonia UAS DigiCenter North Savo
*   **Funding/Support:** 5G:llä vauhtia digitalisaatioon (Pohjois-Savon liitto, REACT-EU EAKR: A77467)

## References
*   Che, F., Ahmed, Q.Z., Lazaridis, P.I., Sureephong, P. and Alade, T. (2023) ‘Indoor Positioning System (IPS) using Ultra‑Wide Bandwidth (UWB)—For Industrial Internet of Things (IIoT)’, Sensors, 23(12), 5710.
*   Hayward, S.J., van Lopik, K., Hinde, C. and West, A.A. (2022) ‘A survey of indoor location technologies, techniques and applications in industry’, Internet of Things, 20, 100608.
*   Zafari, F., Gkelias, A. and Leung, K.K. (2019) ‘A Survey of Indoor Localization Systems and Technologies’, IEEE Communications Surveys & Tutorials, 21(3), pp. 2568–2599.
*   Saha, A. and Dhara, B.C. (2024) ‘3D LiDAR‑based obstacle detection and tracking for autonomous navigation in dynamic environments’, International Journal of Intelligent Robotics and Applications, 8, pp. 39–60.
*   5G:llä vauhtia digitalisaatioon’, Pohjois-Savon liitto, REACT-EU EAKR: A77467.