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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: Aug 16, 2025

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Insect-controlled Robot: A Mobile Robot Platform to Evaluate the Odor-tracking Capability of an Insect

Published on: December 19, 2016

Odor source localization of multi-robots with swarm intelligence algorithms: A review.

Junhan Wang¹, Yuezhang Lin¹, Ruirui Liu¹

¹Artificial Intelligence of Things and Robotics Laboratory, School of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou, China.

Frontiers in Neurorobotics

|December 19, 2022

Summary

Robot swarms utilizing swarm intelligence (SI) algorithms offer robust solutions for odor source localization (OSL). These nature-inspired methods enhance speed and adaptability in complex environments, showing significant potential for hazard detection.

Keywords:

mobile robot multi-robot system nature-inspired computation odor source localization particle swarm optimization swarm intelligence algorithm

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Last Updated: Aug 16, 2025

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Published on: August 27, 2021

Area of Science:

Robotics
Artificial Intelligence
Environmental Science

Background:

Robot swarms are increasingly used for odor source localization (OSL) due to their adaptability to turbulent conditions.
Swarm intelligence (SI) algorithms, inspired by natural collective behavior, provide parallelism, scalability, and robustness for multi-robot systems.
The application of SI-based multi-robot systems for OSL has garnered significant research interest over the past two decades.

Purpose of the Study:

To review trending issues and basic concepts in general robot OSL.
To provide a comprehensive survey of representative SI algorithms applied to multi-robot OSL.
To highlight the evolution and diversity of SI algorithms in this field.

Main Methods:

Summarizing general robot OSL field issues and basic concepts.
Detailing various representative SI algorithms, including Particle Swarm Optimization (PSO) variants and other nature-inspired algorithms.
Analyzing computer simulations and real-world applications reported in the literature.

Main Results:

SI algorithms, particularly PSO variants, have demonstrated effectiveness in solving OSL problems.
The field has evolved from standard PSO to numerous modified and hybrid versions, alongside other SI approaches.
Current SI algorithms show promise but still have room for further development and optimization.

Conclusions:

SI-based robot swarms are effective for OSL, offering faster detection of chemical hazards.
The diversity of SI algorithms, including PSO and other nature-inspired methods, enriches the field.
Future research should focus on further developing and refining these algorithms for enhanced OSL performance.