Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Manifold learning for robot navigation.

Narongdech Keeratipranon1, Frederic Maire, Henry Huang

  • 1Faculty of Information Technology, Queensland University of Technology, Box 2434, Brisbane Q 4001, Australia. n.keeratipranon@student.qut.edu.au

International Journal of Neural Systems
|November 23, 2006
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Sex-specific impact of body mass index on recurrence of atrial fibrillation after catheter ablation.

Heart rhythm O2·2026
Same author

Cardioneuroablation for Ictal Asystole: A Multicenter Case Series.

JACC. Clinical electrophysiology·2026
Same author

Correction: LKB1 promotes cell survival by modulating TIF-IA-mediated pre-ribosomal RNA synthesis under uridine downregulated conditions.

Oncotarget·2026
Same author

Molecular Imaging of Collagen Turnover in Myocardial Infarction.

Journal of nuclear medicine : official publication, Society of Nuclear Medicine·2026
Same author

Multiple sources of β2*-nicotinic acetylcholine receptor binding are differentially affected during tobacco smoking abstinence as revealed by Independent Component Analysis of [<sup>18</sup>F]Flubatine PET images.

Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology·2026
Same author

Tap&Say: Touch Location-Informed Large Language Model for Multimodal Text Correction on Smartphones.

Proceedings of the SIGCHI conference on human factors in computing systems. CHI Conference·2025

This study presents methods for creating isometric maps for mobile robots using Self-Organizing Maps (SOMs). These maps ensure uniform distribution of robot sensor data, improving path-planning and multi-robot coordination.

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Mobile robots require internal representations of their environment for navigation and coordination.
  • Manifold learning techniques are used for dimensionality reduction in sensor data for navigation systems.
  • Existing methods often lack an isometric property crucial for reliable path-planning.

Purpose of the Study:

  • To develop methods for building Self-Organizing Maps (SOMs) that serve as isometric maps for mobile robots.
  • To ensure that SOM neurons correspond to uniformly distributed points on the ground from sensor readings.
  • To investigate the isometric property of SOMs for enhanced robot navigation and information sharing.

Main Methods:

  • Utilizing sensor readings collected at uniformly distributed ground points to build SOMs.

Related Experiment Videos

  • Applying manifold learning techniques to improve neuron distribution in SOMs.
  • Developing a method to create isometric maps from sensor data collected via a polygonal line random walk.

    • Main Results:

    • Standard Non-Linear Dimensionality Reduction (NLDR) algorithms do not yield isometric maps for range and bearing data.
    • Auxiliary low-dimensional manifolds can enhance the uniform distribution of SOM neurons with respect to the ground.
    • Experimental validation of the proposed methods for generating isometric maps.

    Conclusions:

    • The developed SOM methods enable the creation of isometric maps for mobile robots.
    • Improved neuron distribution through auxiliary manifolds enhances the utility of SOMs for robot navigation.
    • The research contributes to more reliable path-planning and information sharing in multi-robot systems.