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

Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the drone...

You might also read

Related Articles

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

Sort by
Same authorSame journal

A Modified Multi-Strategy Dhole Optimization Algorithm and Its Engineering Applications.

Biomimetics (Basel, Switzerland)·2026
Same author

Enriched Environment Suppresses Neuronal Ferroptosis Through SIRT1/AKT/GSK3β-Dependent Glycogen Metabolic Reprogramming After Cerebral Ischemia-Reperfusion.

Antioxidants (Basel, Switzerland)·2026
Same author

EKEO: An Enhanced Kangaroo Escape Optimizer with Balanced Search for Global Optimization and Engineering Design.

Biomimetics (Basel, Switzerland)·2026
Same author

Integrated transcriptomic analysis reveals miRNA-hub mRNA-TF interactions and key regulatory targets in <i>STEC</i> infected intestinal epithelial cells.

Frontiers in cellular and infection microbiology·2026
Same author

EBBO: A Biomimetically Enhanced Optimization Algorithm with Multi-Stage Cooperation for Complex Engineering Applications.

Biomimetics (Basel, Switzerland)·2026
Same author

ROS-responsive, brain- and M1 microglia-targeting modified ginkgetin-loaded smart liposomes ameliorate cerebral ischemia by HIF-1α-mediated negative regulation of microglia pyroptosis.

Materials today. Bio·2026
Same journal

Multiphysics Investigation on Thermal Characteristics of Internal Bio-Inspired V-Ribbed Cooling Channels for Outer Rotor PMSM.

Biomimetics (Basel, Switzerland)·2026
Same journal

Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks.

Biomimetics (Basel, Switzerland)·2026
Same journal

A Systematic Taxonomy of the Sunflower Optimization Algorithm: Variants, Hybridization Strategies, Applications, and Research Directions.

Biomimetics (Basel, Switzerland)·2026
Same journal

Toward a Compositional Theory of Trust in Embodied Intelligence: A QNLP Framework for Modeling Context, Interaction, and Trustworthiness.

Biomimetics (Basel, Switzerland)·2026
Same journal

Empirical Logic for Bio-Inspired Soft Computing: Illustrative Applications in Control Engineering and Cluster Analysis.

Biomimetics (Basel, Switzerland)·2026
See all related articles
  1. Home
  3. Research On Uav Path Planning Based On Enhanced Artificial Lemming Algorithm.
  1. Home
  3. Research On Uav Path Planning Based On Enhanced Artificial Lemming Algorithm.

Related Experiment Video

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Research on UAV Path Planning Based on Enhanced Artificial Lemming Algorithm.

Yu Liu1,2, Maosheng Fu1,2, Chaochuan Jia1,2

  • 1School of Electronic Information and Artificial Intelligence, West Anhui University, Lu'an 237012, China.

Biomimetics (Basel, Switzerland)
|May 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

A new algorithm, ALAEN, enhances unmanned aerial vehicle (UAV) path planning by improving route optimization and safety. This method offers a significant improvement over traditional techniques for effective UAV trajectory planning.

Keywords:
BetaCOBLUAVartificial lemming algorithmpath planning

More Related Videos

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

Published on: April 23, 2020

Related Experiment Videos

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)
08:04

Fully Automated Leg Tracking in Freely Moving Insects using Feature Learning Leg Segmentation and Tracking (FLLIT)

Published on: April 23, 2020

Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Optimization Algorithms

Background:

  • Unmanned aerial vehicle (UAV) path planning presents significant challenges in achieving both effectiveness and safety.
  • Traditional optimization methods often struggle to identify optimal flight routes efficiently.
  • Ensuring reliable navigation is critical for various UAV applications.

Purpose of the Study:

  • To propose an enhanced artificial lemming optimization algorithm (ALAEN) for improved UAV path planning.
  • To address the limitations of existing optimization methods in finding optimal UAV trajectories.
  • To enhance the safety and efficiency of UAV navigation.

Main Methods:

  • Introduction of stochastic differential mutation and Beta opposition-based learning into the artificial lemming algorithm (ALA).
  • Comparative analysis of ALAEN against other algorithms using the CEC2017 benchmark test set.
  • Empirical testing of ALAEN on two distinct map scenarios for real-world UAV trajectory planning simulation.

    • Main Results:

    • ALAEN demonstrated superior optimization ability and convergence speed compared to the standard ALA and other algorithms.
    • ALAEN achieved a significantly improved ranking (1.34) compared to ALA (5.45) on the CEC2017 test set.
    • In simulations, ALAEN resulted in a lower average cost function (91.598) than ALA (92.999) on a small map.
    • ALAEN produced the shortest trajectory routes and lowest trajectory cost functions among tested algorithms.

    Conclusions:

    • The enhanced artificial lemming optimization algorithm (ALAEN) effectively improves UAV path planning capabilities.
    • ALAEN offers enhanced safety and efficiency in trajectory planning compared to the basic ALA.
    • The proposed ALAEN algorithm represents a significant advancement for complex UAV navigation tasks.