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Related Experiment Video

Updated: May 25, 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

Sonar sensor models and their application to mobile robot localization.

Antoni Burguera¹, Yolanda González, Gabriel Oliver

¹Department de Matemàtiques i Informàtica, Universitat de les Illes Balears, Ctra. de Valldemosa Km. 7.5, E-07122 Palma de Mallorca, Spain; E-Mails: y.gonzalez@uib.es (Y.G.); goliver@uib.es (G.O.).

Sensors (Basel, Switzerland)

|February 4, 2012

Summary

This study introduces a new mobile robot localization method using sonar sensors and particle filters. The approach enhances particle accuracy with real-time environmental data, improving navigation performance.

Keywords:

mobile robot localization particle filter sonar

Related Experiment Videos

Last Updated: May 25, 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

Area of Science:

Robotics
Sensor Fusion
Probabilistic Methods

Background:

Mobile robot localization is crucial for autonomous navigation.
Existing methods often face challenges with sensor uncertainties and dynamic environments.
Sonar sensors offer a cost-effective solution but require careful modeling.

Purpose of the Study:

To present a novel particle filter-based mobile robot localization approach using sonar sensors.
To incorporate local environment information into particle filters for enhanced accuracy.
To experimentally validate the proposed method against existing localization strategies.

Main Methods:

Utilizing particle filters augmented with dynamically updated local environment information.
Conducting experimental characterization of sonar sensor performance and uncertainties.
Developing a probabilistic measurement model based on empirical sonar data.
Comparing the novel approach with sonar-laser based localization methods.

Main Results:

Quantitative evaluation demonstrating the effectiveness of the proposed localization approach.
Comparative analysis showing performance benefits over other sonar and laser-based strategies.
Qualitative results providing visual evidence of successful localization in various scenarios.

Conclusions:

The presented particle filter approach with augmented local environment information offers a robust solution for mobile robot localization using sonar sensors.
Experimental validation confirms the approach's accuracy and superiority compared to existing methods.
The study highlights the importance of accurate sensor modeling for reliable autonomous navigation.