Jove
Visualize
Contact Us

Related Concept Videos

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.3K
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...
4.3K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.5K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
5.5K
Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

228
Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
228
Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

14.2K
Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
14.2K
Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

166
Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
166
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

246
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...
246

You might also read

Related Articles

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

Sort by
Same author

Novel Counteraction Effect of H<sub>2</sub>O and SO<sub>2</sub> toward HCl on the Chemical Adsorption of Gaseous Hg<sup>0</sup> onto Sulfureted HPW/γ-Fe<sub>2</sub>O<sub>3</sub> at Low Temperatures: Mechanism and Its Application in Hg<sup>0</sup> Recovery from Coal-Fired Flue Gas.

Environmental science & technology·2021
Same author

An Early-Onset Advanced Rectal Cancer Patient With Increased KRAS Gene Copy Number Showed A Primary Resistance to Cetuximab in Combination With Chemotherapy: A Case Report.

Frontiers in oncology·2021
Same author

Construction of a risk assessment model of cardiovascular disease in a rural Chinese hypertensive population based on lasso-Cox analysis.

Journal of clinical hypertension (Greenwich, Conn.)·2021
Same author

Low fat mass index outperforms handgrip weakness and GLIM-defined malnutrition in predicting cancer survival: Derivation of cutoff values and joint analysis in an observational cohort.

Clinical nutrition (Edinburgh, Scotland)·2021
Same author

Development and validation of a Modified Patient-Generated Subjective Global Assessment as a nutritional assessment tool in cancer patients.

Journal of cachexia, sarcopenia and muscle·2021
Same author

Structural Basis of Pore Formation in the Mannose Phosphotransferase System by Pediocin PA-1.

Applied and environmental microbiology·2021
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 Experiment Video

Updated: Aug 3, 2025

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
06:19

Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

Published on: December 29, 2021

2.6K

Collision-Avoiding Flocking With Multiple Fixed-Wing UAVs in Obstacle-Cluttered Environments: A Task-Specific

Chao Yan, Chang Wang, Xiaojia Xiang

    IEEE Transactions on Neural Networks and Learning Systems
    |April 7, 2023
    PubMed
    Summary

    This study introduces a new method for multiple fixed-wing unmanned aerial vehicles (UAVs) to fly together safely, avoiding collisions even with obstacles. The task-specific curriculum-based multiagent deep reinforcement learning (TSCAL) approach improves learning efficiency and stability.

    More Related Videos

    Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees
    09:09

    Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

    Published on: November 15, 2014

    11.0K
    Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
    07:49

    Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

    Published on: November 26, 2019

    8.1K

    Related Experiment Videos

    Last Updated: Aug 3, 2025

    Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night
    06:19

    Low-Cost Automated Flight Intercept Trap for the Temporal Sub-Sampling of Flying Insects Attracted to Artificial Light at Night

    Published on: December 29, 2021

    2.6K
    Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees
    09:09

    Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

    Published on: November 15, 2014

    11.0K
    Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization
    07:49

    Automated Deployment of an Internet Protocol Telephony Service on Unmanned Aerial Vehicles Using Network Functions Virtualization

    Published on: November 26, 2019

    8.1K

    Area of Science:

    • Robotics
    • Artificial Intelligence
    • Aerospace Engineering

    Background:

    • Coordinated flight of multiple unmanned aerial vehicles (UAVs) is crucial for complex tasks.
    • Developing decentralized, collision-avoiding flocking policies for fixed-wing UAVs in cluttered environments remains a significant challenge.

    Purpose of the Study:

    • To propose a novel task-specific curriculum-based multiagent deep reinforcement learning (TSCAL) approach.
    • To enable decentralized flocking with obstacle avoidance for multiple fixed-wing UAVs.

    Main Methods:

    • Decomposing the flocking task into subtasks and progressively increasing complexity.
    • Utilizing a hierarchical recurrent attention multiagent actor-critic (HRAMA) algorithm for online learning.
    • Implementing model reload and buffer reuse for offline knowledge transfer between learning stages.

    Main Results:

    • TSCAL demonstrates superior policy optimality, sample efficiency, and learning stability compared to existing methods.
    • Numerical simulations validate the effectiveness of the proposed approach.
    • High-fidelity hardware-in-the-loop (HITL) simulations confirm TSCAL's adaptability.

    Conclusions:

    • The TSCAL approach offers an effective solution for decentralized flocking with obstacle avoidance in multi-UAV systems.
    • The curriculum-based learning strategy significantly enhances the learning process and policy performance.
    • The method is validated through both simulation and HITL testing, showing practical applicability.