Autonomous disinfection routine and sensor fusion for UV-C disinfection robots

Thumbnail Image

Files

master_Srinivasan_Shreyas_2026.pdf (3.81 MB)

URL

Journal Title

Journal ISSN

Volume Title

School of Electrical Engineering | Master's thesis
Unless otherwise stated, all rights belong to the author. You may download, display and print this publication for Your own personal use. Commercial use is prohibited.

Authors

Date

2025-12-19

Department

Major/Subject

Autonomous Systems

Mcode

Degree programme

Master's Programme in ICT Innovation
Master's Programme in ICT Innovation
Master's Programme in ICT Innovation

Language

en

Pages

116

Series

Abstract

This thesis presents the design and implementation of an autonomous UV-C disinfection robot capable of reliable indoor navigation and structured surface coverage in hospital-like environments. The system integrates LiDAR, wheel odometry, and a high-frequency IMU using an Extended Kalman Filter to achieve drift-resistant localization, complemented by Adaptive Monte Carlo Localization for map-based pose refinement. Multiple global and local planners were evaluated within the ROS navigation stack, including NavFn, A*, RRT, DWA, TEB, and MPC. NavFn and DWA were selected as the most stable and computationally efficient combination for real-time autonomous disinfection, with extensive tuning of costmaps, velocity constraints, and noise models to ensure safe motion and consistent trajectory tracking. To enable systematic UV-C exposure, the robot incorporates two coverage behaviors: a wall-following routine to disinfect room boundaries at a fixed 40 cm offset, and a Bug1-based strategy for identifying and navigating around isolated interior objects. These routines were validated through simulation in Gazebo and physical testing partially on a Magni robot equipped with a UV-C module and NVIDIA Jetson AGX Xavier for onboard computation. Results show that the integrated system achieves localization, safe obstacle avoidance, and reliable coverage of both boundary and interior surfaces. The work demonstrates a practical approach for autonomous UV-C disinfection and provides recommendations for improving exposure precision, multi-room navigation, and future scaling of coverage strategies in healthcare environments.

Description

Supervisor

Zhou, Quan

Thesis advisor

Fontanelli, Daniele

Keywords

UV-C disinfection, autonomous navigation, sensor fusion, extended kalman filter, path planning, wall-followingalgorithm

Other note

Citation

Permanent link to this item

https://urn.fi/URN:NBN:fi:aalto-202601201674

Collections

[dipl] Sähkötekniikan korkeakoulu / ELEC