Abstract
This paper addresses the problem of mapping odor distribution derived from a chemical source using multi-sensor integration and reasoning system design. Odor localization is the problem of finding the source of an odor or other volatile chemical. Most localization methods require a mobile vehicle to follow an odor plume along its entire path, which is time consuming and may be especially difficult in a cluttered environment. To solve both of the above challenges, this paper proposes a novel algorithm that combines data from odor and anemometer sensors, and combine sensors’ data at different positions. Initially, a multi-sensor integration method, together with the path of airflow was used to map the pattern of odor particle movement. Then, more sensors are introduced at specific regions to determine the probable location of the odor source. Finally, the results of odor source location simulation and a real experiment are presented.
Highlights
Detection of Odor SourceThe detection of airborne chemicals presents a different type of challenge than more traditional detection efforts, such as visual-based detection [1,2] or propagating signal detection [3,4,5]
In [10], the odor localization used a bi-modal search with the complementary sensing of olfaction and vision
We proposed a novel algorithm of mapping continuous particle paths using discrete sensors for odor source localization, an application of a radial basis function neural network for chemical source detection, and odor source localization using spline interpolation with the complementary
Summary
The detection of airborne chemicals presents a different type of challenge than more traditional detection efforts, such as visual-based detection [1,2] or propagating signal detection [3,4,5]. In [11], the author set up a mobile sensing system for localization of an odor source using gas and anemometric sensors These types of sensing robots are assumed to move about freely following the trail of a chemical signature, while continuously searching for the particles. Both of these assumptions may be invalid in inaccessible and hostile environments with sensors that can either function one time or need long rejuvenation time cycles. Through a reasoning system, we localize the area where the chemical source is located
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