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

Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
To solve a three-dimensional force system, first resolve each force into its respective scalar components. Do this using...
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...
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...
Lagrange Multipliers: Two Constraints01:28

Lagrange Multipliers: Two Constraints

The method of Lagrange multipliers with two constraints is used to optimize a function subject to two independent constraints. In many applications, the objective function represents a quantity to be maximized or minimized, such as cost, area, distance, or energy. The two constraints represent requirements that the solution must satisfy, such as fixed volume, limited resources, or prescribed dimensions.For a function of three variables, each constraint forms a surface in three-dimensional space.
Lagrange Multipliers: Problem Solving01:30

Lagrange Multipliers: Problem Solving

A silo with a cylindrical base, flat bottom, and hemispherical roof is a common design in agricultural and industrial storage due to its structural efficiency and ease of construction. Optimizing its dimensions to maximize storage capacity for a given amount of material—i.e., a fixed surface area—is a classic problem in applied calculus and engineering design. The key parameters are the radius r of the base and the height h of the cylindrical section.The total volume of the silo is obtained by...

You might also read

Related Articles

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

Sort by
Same author

Metabolomic comparative study in patients with liver cirrhosis and hepatocellular carcinoma related to hepatitis B virus infection.

European journal of gastroenterology & hepatology·2025
Same author

Metabolomic comparative study in patients with liver cirrhosis and hepatocellular carcinoma related to hepatitis B virus infection.

European journal of gastroenterology & hepatology·2025
Same author

Efficacy and safety of local glucocorticoids for the treatment of acute radiation-induced intestinal injury: protocol of a multicenter randomized controlled trial.

Frontiers in pharmacology·2025
Same author

<i>Bacillus amyloliquefaciens</i> Regulates the <i>Keap1/Nrf2</i> Signaling Pathway to Improve the Intestinal (Caco-2 Cells and Chicken Jejunum) Oxidative Stress Response Induced by Lipopolysaccharide (LPS).

Antioxidants (Basel, Switzerland)·2025
Same author

An Improved Spider Wasp Optimizer for UAV Three-Dimensional Path Planning.

Biomimetics (Basel, Switzerland)·2024
Same author

Enhancing Air Traffic Control Communication Systems with Integrated Automatic Speech Recognition: Models, Applications and Performance Evaluation.

Sensors (Basel, Switzerland)·2024

Related Experiment Video

Updated: Jun 22, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

12.3K

Solving UAV 3D Path Planning Based on the Improved Lemur Optimizer Algorithm.

Haijun Liang1, Wenhai Hu1, Ke Gong1

  • 1Air Traffic Management Institute, Civil Aviation Flight University of China, Deyang 618307, China.

Biomimetics (Basel, Switzerland)
|November 26, 2024
PubMed
Summary
This summary is machine-generated.

The Improved Lemur Optimization algorithm (ILO) enhances UAV path planning by improving global exploration and local exploitation. It generates efficient, smooth flight paths in complex terrains with fewer iterations.

Keywords:
Improved Lemurs Optimizer algorithm ILOLemurs Optimizer algorithm LOmathematical modeloptimal pathpath planningterrain mappingunmanned aircraft

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

6.8K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.3K

Related Experiment Videos

Last Updated: Jun 22, 2026

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery
09:41

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Published on: May 20, 2016

12.3K
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

6.8K
Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation
08:47

Author Spotlight: UAV Remote Sensing for Efficient Invasive Plant Biomass Estimation

Published on: February 9, 2024

1.3K

Area of Science:

  • Computational Intelligence
  • Robotics and Control Systems
  • Artificial Intelligence

Background:

  • Traditional optimization algorithms often suffer from premature convergence and limited local search capabilities.
  • Efficient UAV path planning in complex environments requires robust algorithms that balance exploration and exploitation.
  • Existing methods may struggle with dynamic obstacle avoidance and smooth trajectory generation.

Purpose of the Study:

  • To introduce an Improved Lemur Optimization algorithm (ILO) for enhanced UAV path planning.
  • To improve global exploration and local exploitation capabilities in optimization algorithms.
  • To generate high-quality, smooth, and cost-efficient flight paths for Unmanned Aerial Vehicles (UAVs) in complex terrains.

Main Methods:

  • The Improved Lemur Optimization algorithm (ILO) integrates Spider Monkey Optimization, Simulated Annealing, and Lemur Optimization.
  • Adaptive nonlinear decrement models, learning factors, and jump rates are employed to enhance search capabilities.
  • A Gaussian function models a mountain environment, and a mathematical model for UAV flight, including constraints and objective functions, is established.
  • Cubic spline interpolation is utilized for flight path smoothing, with a fitness function guiding obstacle avoidance.

Main Results:

  • The ILO algorithm demonstrated superior search capability, convergence speed, and accuracy on the CEC2017 benchmark set.
  • Simulation results indicated that ILO generates high-quality, smooth UAV paths with significantly fewer iterations compared to traditional genetic algorithms.
  • The algorithm effectively overcomes premature convergence and insufficient local search ability, adapting well to complex terrain.

Conclusions:

  • The Improved Lemur Optimization algorithm offers an efficient and reliable solution for UAV path planning in complex environments.
  • ILO enhances the performance of optimization algorithms by improving exploration and exploitation balance.
  • The proposed method provides a robust approach for generating smooth and cost-effective flight trajectories while avoiding obstacles.