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 Concept Videos

Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

571
To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
571
Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

3.5K
In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
3.5K
Indirect Motor Pathways01:22

Indirect Motor Pathways

3.8K
The indirect motor or extrapyramidal pathways originate in the brainstem, the lower portion of the brain that connects it to the spinal cord. They consist of several distinct tracts, each with specialized functions. The four main tracts of the indirect motor pathways are the vestibulospinal tract, the reticulospinal tract, the tectospinal tract, and the rubrospinal tract.
The vestibulospinal tract originates in the vestibular nuclei of the brainstem. The vestibular system detects changes in...
3.8K
Direct Motor Pathways01:11

Direct Motor Pathways

4.9K
The direct motor pathways, also known as the pyramidal tracts, are a group of neural pathways that originate in the brain and descend through the spinal cord. They control the voluntary movement of the body. There are two major direct motor pathways: the corticospinal and the corticobulbar tracts.
The corticospinal tract is responsible for the voluntary movement of the limbs and trunk. It originates in the cerebral cortex of the brain and descends through the cerebrum's internal capsule and...
4.9K

You might also read

Related Articles

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

Sort by
Same author

Research and Implementation of Localization of Multiple Local Discharge Sources in Switchgear Based on Ultrasound.

Sensors (Basel, Switzerland)·2026
Same author

Design and Development of a Precision Spraying Control System for Orchards Based on Machine Vision Detection.

Sensors (Basel, Switzerland)·2025
Same author

Citrus Disease Detection Based on Dilated Reparam Feature Enhancement and Shared Parameter Head.

Sensors (Basel, Switzerland)·2025
Same author

Leveraging Thermal Infrared Imaging for Pig Ear Detection Research: The TIRPigEar Dataset and Performances of Deep Learning Models.

Animals : an open access journal from MDPI·2025
Same author

A Dataset of Visible Light and Thermal Infrared Images for Health Monitoring of Caged Laying Hens in Large-Scale Farming.

Sensors (Basel, Switzerland)·2024
Same author

Automatic Monitoring Methods for Greenhouse and Hazardous Gases Emitted from Ruminant Production Systems: A Review.

Sensors (Basel, Switzerland)·2024

Related Experiment Video

Updated: Mar 6, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.9K

A Dynamic Bioinspired Neural Network Based Real-Time Path Planning Method for Autonomous Underwater Vehicles.

Jianjun Ni1, Liuying Wu2, Pengfei Shi1

  • 1College of IOT Engineering, Hohai University, Changzhou 213022, China; Changzhou Key Laboratory of Special Robot and Intelligent Technology, Hohai University, Changzhou 213022, China.

Computational Intelligence and Neuroscience
|March 4, 2017
PubMed
Summary

This study introduces an improved dynamic bioinspired neural network (BINN) for autonomous underwater vehicle (AUV) real-time path planning. The enhanced method efficiently navigates complex 3D environments, overcoming limitations of previous BINN applications.

More Related Videos

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Related Experiment Videos

Last Updated: Mar 6, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
11:18

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

10.9K
Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

1.2K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Marine Engineering

Background:

  • Real-time path planning for autonomous underwater vehicles (AUVs) in 3D unknown environments is challenging.
  • Bioinspired neural networks (BINNs) offer advantages like no learning requirement but face issues with large environments and obstacle sizes.
  • Existing BINNs struggle with computational complexity and repeated paths for AUVs.

Purpose of the Study:

  • To propose an improved dynamic bioinspired neural network (BINN) for enhanced real-time path planning in 3D unknown underwater environments.
  • To address the computational complexity and repeated path problems associated with traditional BINNs for AUVs.
  • To enhance the efficiency and effectiveness of AUV navigation in complex underwater scenarios.

Main Methods:

  • An improved dynamic BINN is proposed, treating the AUV as the core and sizing the network based on sensor detection range.
  • The BINN moves with the AUV to reduce computational load.
  • A virtual target and target attractor concepts are introduced to ensure effective navigation and improve neural activity efficiency.

Main Results:

  • The proposed dynamic BINN method effectively reduces computational complexity for AUV path planning.
  • The virtual target mechanism allows automatic avoidance of large obstacles.
  • Experiments in various 3D underwater environments demonstrate the method's efficiency.

Conclusions:

  • The improved dynamic BINN provides an efficient solution for real-time path planning for AUVs in complex 3D environments.
  • The method overcomes limitations of traditional BINNs, offering better performance and reduced computational demands.
  • This approach enhances AUV autonomy and navigation capabilities in challenging underwater settings.