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Olfaction

Olfaction

The sense of smell is achieved through the activities of the olfactory system. It starts when an airborne odorant enters the nasal cavity and reaches olfactory epithelium (OE). The OE is protected by a thin layer of mucus, which also serves the purpose of dissolving more complex compounds into simpler chemical odorants. The size of the OE and the density of sensory neurons varies among species; in humans, the OE is only about 9-10 cm2.
The olfactory receptors are embedded in the cilia of the...

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Related Experiment Video

Updated: May 24, 2026

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Using Insect Electroantennogram Sensors on Autonomous Robots for Olfactory Searches

Published on: August 4, 2014

Collective odor source estimation and search in time-variant airflow environments using mobile robots.

Qing-Hao Meng¹, Wei-Xing Yang, Yang Wang

¹Institute of Robotics and Autonomous Systems, School of Electrical Engineering and Automation, Tianjin University, No. 92, Weijin Rd., Tianjin 300072, China. qh_meng@tju.edu.cn

Sensors (Basel, Switzerland)

|February 21, 2012

Summary

This study introduces a new method for robots to find the source of smells in changing air currents. It combines probability mapping with coordinated searching for effective odor source localization.

Keywords:

Bayesian rules estimation fuzzy inference multi-robot odor source localization particle swarm optimization search

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Area of Science:

Robotics
Artificial Intelligence
Environmental Science

Background:

Odor source localization (OSL) is challenging in dynamic airflow environments.
Coordinated multi-robot systems offer potential for improved OSL.
Existing methods struggle with time-varying atmospheric conditions.

Purpose of the Study:

To develop a novel OSL methodology for mobile robots in time-varying airflow.
To enhance the accuracy and robustness of odor source detection using collective intelligence.
To integrate probability estimation with coordinated multi-robot search strategies.

Main Methods:

A two-step probability-distribution map estimation using Bayesian rules and fuzzy inference.
Fusion of individual robot maps via distance-based superposition for a combined map.
Coordination of multi-robot search using particle swarm optimization with probability distribution fitness functions.
Iterative implementation of estimation and searching phases for continuous refinement.

Main Results:

Validated feasibility and robustness through large-scale simulations in advection-diffusion plume environments.
Demonstrated effectiveness in real-world experiments with mobile robots in an indoor airflow setting.
The proposed method successfully integrated prior estimation knowledge with search verification.

Conclusions:

The novel OSL methodology effectively addresses the challenges of time-varying airflow.
Iterative estimation and search phases enhance the reliability of odor source localization.
The combined approach of probability mapping and coordinated search is robust and feasible.