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

Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Decision Making: P-value Method01:09

Decision Making: P-value Method

The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can have a...
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

A drone flying through complex terrain often relies on more than one sensing method to estimate small changes in altitude. Along with direct measurements, air pressure provides a useful indirect indicator of vertical movement. Atmospheric pressure decreases as altitude increases, and this relationship is commonly described using an exponential model. Although accurate, converting pressure measurements into altitude values requires calculations that are too complex to perform repeatedly during...

You might also read

Related Articles

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

Sort by
Same author

Genome-Wide Analysis of AGPase Identifies <i>CsAGP4</i> as a Regulator of Watermelon Mosaic Virus Resistance in Cucumber.

International journal of molecular sciences·2026
Same author

MLP-residual surrogate model for aerodynamic prediction in projectile external flows.

Science progress·2026
Same author

The effect of family continuity management on the anxiety level of parents of children with febrile seizures and recurrence rate.

Frontiers in medicine·2026
Same author

Integrated Optimized HPLC-MS/MS Profiling and GWAS Uncover Candidate Genes for Folate Content in Cucumber Fruits.

Journal of agricultural and food chemistry·2026
Same author

A Metaheuristic Optimization Algorithm for Task Clustering in Collaborative Multi-Cluster Systems.

Sensors (Basel, Switzerland)·2026
Same author

Label-free tissue NIR-II autofluorescence imaging for visualization of human liver malignancy.

Nature biomedical engineering·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Videos

A Multilayer Decision-Making Method for UAV Formation Cooperative Flight in Complex Urban Environments.

Junjie Wang1, Dongyu Yan1, Yongping Hao2

  • 1School of Science, Shenyang Ligong University, Shenyang 110159, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new hierarchical control framework for multi-unmanned aerial vehicle (UAV) coordination in complex urban environments. The framework enhances path planning efficiency and ensures adaptive obstacle avoidance for safer autonomous flight.

Keywords:
complex urban environmentformation cooperative flightobstacle avoidancepath planningtrajectory trackingvirtual leader–follower

Related Experiment Videos

Area of Science:

  • Robotics and Control Systems
  • Artificial Intelligence and Machine Learning
  • Aerospace Engineering

Background:

  • Complex urban environments pose significant challenges for Unmanned Aerial Vehicle (UAV) operations, including dynamic obstacles, limited perception, and coordination constraints.
  • Existing path planning and obstacle avoidance algorithms struggle with efficiency and adaptability in dense, dynamic settings.
  • Coordinated multi-UAV systems require robust frameworks for simultaneous formation maintenance and autonomous navigation.

Purpose of the Study:

  • To propose a novel hierarchical control framework for multi-UAVs in complex urban environments.
  • To enhance global path planning efficiency and local obstacle avoidance capabilities.
  • To enable adaptive coordination between formation maintenance and obstacle avoidance tasks.

Main Methods:

  • Development of a Dynamic Adaptive Strategy Rapidly Exploring Random Tree Star (DASRRT*) algorithm for global path planning, incorporating hybrid guided sampling and adaptive step size strategies.
  • Construction of a distributed controller using an improved artificial potential field method for local obstacle avoidance, integrating spring-damper models and consensus theory for formation control.
  • Implementation of a real-time local target selection and a dual-mode switching mechanism for adaptive control mode adjustment based on environmental complexity.

Main Results:

  • The DASRRT* algorithm demonstrated a 34.78% reduction in path planning time and a 1.15% decrease in path length compared to traditional methods.
  • Simulations confirmed the hierarchical control framework's ability to adapt control modes based on environmental complexity.
  • The proposed framework exhibited strong adaptability in complex environments and good generalization across various scenarios.

Conclusions:

  • The developed hierarchical control framework effectively addresses challenges in multi-UAV coordination within complex urban environments.
  • The DASRRT* algorithm significantly improves path planning efficiency and trajectory quality.
  • The adaptive control mechanism enhances the robustness and generalizability of multi-UAV systems in dynamic and complex scenarios.