Improved mobile robot based gas distribution mapping through ambient temperature information for indoor environment
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Author |
Zaffry Hadi Mohd Juffry, Kamarulzaman Kamarudin, Abdul Hamid Adom, Retnam Visvanathan, Abdulnasser Nabil Abdullah, Wan Abdul Syaqur Norzam, Ahmad Hakimi Ahmad Sa'ahiry, Zairi Ismael Rizman and Nasr Abdalmanan
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e-ISSN |
1819-6608 |
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On Pages
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776-789
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Volume No. |
21
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Issue No. |
12
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Issue Date |
August 20, 2026
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DOI |
https://doi.org/10.59018/062683
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Keywords |
mobile olfaction, gas source localization, gas distribution mapping, ambient temperature, and indoor gas dispersion.
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Abstract
Gas Distribution Mapping (GDM) is a method used to represent the dispersion of harmful gas and serves the purpose of localizing the gas source within an environment. Basically, a mobile robot carrying a gas sensor, known as Mobile Robot Olfaction (MRO), helps to collect important readings in the contaminated area and is used to generate the GDM. There are many environmental factors that could affect gas dispersion that were reported in previous work, which could also affect the quality of the GDM. However, one frequently neglected important environmental factor is ambient temperature, which could influence the dispersion of gas within the indoor environment. This paper aims to improve the original algorithm of GDM by incorporating ambient temperature information into the algorithm, which is known as the Temperature-compensated Kernel DM+V algorithm. A solution was proposed to improve the original Kernel DM+V algorithm by considering the ambient temperature reading within the environment. The original algorithm of GDM was modified by altering the kernel width according to the ambient temperature reading. The proposed solution was observed under both cooler and warmer environments, showing that the gas distribution in the warmer environment extended over a larger area compared with its dispersion in the cooler environment. Compared to the conventional method, it seems to represent a similar gas distribution even though there is a difference in ambient temperature level. This study demonstrates that incorporating ambient temperature information into the original GDM algorithm results in a more realistic gas distribution map that accurately reflects gas dispersion under varying environmental conditions.
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