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

Orthogonal Trajectories01:26

Orthogonal Trajectories

24
Orthogonal trajectories describe the geometric relationship between two families of curves that intersect each other at right angles. One illustrative case involves a family of parabolas that open sideways along the x-axis. These curves share a common shape but differ by a scaling parameter, resulting in a set of curves that all pass through the origin and widen at different rates.Determining Orthogonal TrajectoriesTo identify the orthogonal trajectories for these parabolas, the first step...
24
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

540
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...
540
Application of Linearization and Approximation01:29

Application of Linearization and Approximation

67
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...
67
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

706
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...
706
Impact: Problem Solving01:26

Impact: Problem Solving

441
In an experiment conducted during a Mars mission, a rover propels a projectile with an initial velocity, and the projectile rebounds after colliding with the Martian surface. To ascertain the maximum height attained by the projectile after this collision, the known restitution coefficient and acceleration due to gravity are employed.
By designating the launch point as the origin and utilizing kinematic equations, the vertical component of the projectile's velocity at the point of impact is...
441
Application of Nonlinear Inequalities01:29

Application of Nonlinear Inequalities

214
A nonlinear inequality describes a comparison involving an expression that curves or behaves more complexly than a straight line. These inequalities often appear in forms that include squares, products, or variables in the denominator.To solve such an inequality, one starts by rewriting it so that zero appears on one side. For example, the inequality:  can be factored as: This form makes it easier to identify the values that cause the expression to equal zero. In this case, the...
214

You might also read

Related Articles

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

Sort by
Same author

Novel variants of the KLF1 gene associated with the In(Lu) phenotype lead to alterations in protein structure.

Transfusion medicine (Oxford, England)·2026
Same author

Ventilation-Associated Differences in Lower Airway Microbial Signatures and Peripheral Blood Transcriptome Among Critically Ill COVID-19 Patients.

Infection and drug resistance·2026
Same author

Evaluating convolutional neural network models for automated abdominal aortic calcification scoring in chronic kidney disease patients across multiple centers.

Quantitative imaging in medicine and surgery·2026
Same author

Association between triglyceride glucose index and risk of acute kidney injury in critically ill patients: a systematic review and meta-analysis.

Frontiers in endocrinology·2026
Same author

Energy-Efficient 3D Trajectory Optimization and Resource Allocation for UAV-Enabled ISAC Systems.

Entropy (Basel, Switzerland)·2026
Same author

Association between glycated hemoglobin variability and risk of diabetic kidney disease and diabetic retinopathy in diabetic patients: a systematic review and meta-analysis.

Frontiers in endocrinology·2026

Related Experiment Video

Updated: Jan 19, 2026

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.1K

Completion Time Minimization for Multi-UAV Information Collection via Trajectory Planning.

Zhen Qin1, Aijing Li2, Chao Dong3

  • 1College of Communications Engineering, Army Engineering University of PLA, Nanjing 210042, China. qzqzla912@163.com.

Sensors (Basel, Switzerland)
|September 22, 2019
PubMed
Summary

This study minimizes mission completion time for multi-UAV systems in Wireless Sensor Networks (WSNs). The novel approach optimizes Unmanned Aerial Vehicle (UAV) trajectories for efficient data collection, enhancing network performance.

Keywords:
mission completion timetrajectory planningunmanned aerial vehiclewireless sensor networks

More Related Videos

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

1.2K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.1K

Related Experiment Videos

Last Updated: Jan 19, 2026

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.1K
Eye Tracking During A Complex Aviation Task For Insights Into Information Processing
07:48

Eye Tracking During A Complex Aviation Task For Insights Into Information Processing

Published on: April 4, 2025

1.2K
Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.1K

Area of Science:

  • Robotics and Automation
  • Wireless Sensor Networks
  • Optimization Algorithms

Background:

  • Unmanned Aerial Vehicles (UAVs) are crucial for data collection in Wireless Sensor Networks (WSNs) due to their mobility and deployment flexibility.
  • Optimizing UAV operations is key to maximizing network lifespan and efficiency.
  • Cooperative missions involving multiple UAVs present complex trajectory planning challenges.

Purpose of the Study:

  • To minimize mission completion time for cooperative rotary-wing UAVs in a multi-UAV WSN monitoring scenario.
  • To address the NP-hard problem of optimizing UAV trajectories while considering information collection quality.
  • To develop a novel algorithm for efficient data collection in WSNs using multiple UAVs.

Main Methods:

  • Formulation of the problem as a mixed-integer non-convex optimization problem.
  • Development of a hovering point selection algorithm considering Base Station (BS) coverage and information quality.
  • Application of a min-max cycle cover algorithm for trajectory assignment.
  • Optimization of time allocation for UAVs collecting data while flying.

Main Results:

  • A novel algorithm that successfully minimizes mission completion time for multi-UAV systems.
  • Demonstrated superior performance compared to existing state-of-the-art algorithms in simulations.
  • Effective integration of flying and hovering data collection strategies.

Conclusions:

  • The proposed method provides an effective solution for minimizing mission completion time in cooperative UAV-based WSNs.
  • Optimized UAV trajectories significantly enhance data collection efficiency and network performance.
  • This research offers a valuable contribution to the field of autonomous systems in WSNs.