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

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.2K
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...
5.2K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

6.5K
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...
6.5K
Relative Velocity in Two Dimensions01:11

Relative Velocity in Two Dimensions

8.8K
Relative velocity is the velocity of an object as observed from a particular reference frame, or the velocity of one reference frame with respect to another reference frame. The concept of relative velocity can be used to describe motion in two dimensions. Consider a particle P and two reference frames S and S′. The position of the origin of S′ as measured in S is , the position of P as measured in S′ is , and the position of P as measured in S is , which can be evaluated by utilizing...
8.8K
Relative Velocity in One Dimension01:10

Relative Velocity in One Dimension

9.6K
The understanding of the concept of reference frames is essential to discuss relative motion in one or more dimensions. When we say that an object has a certain velocity, we must state the velocity with respect to a given reference frame. In most examples, this reference frame has been Earth. For instance, if a statement reads that a person is sitting in a train moving at 10 m/s east, then it implies that the person on the train is moving relative to the surface of Earth at this velocity,...
9.6K
Types of Collisions - II01:19

Types of Collisions - II

9.5K
When two or more objects collide with each other, they can stick together to form one single composite object (after collision). The total mass of the object after the collision is the sum of the masses of the original objects, and it moves with a velocity dictated by the conservation of momentum. Although the system's total momentum remains constant, the kinetic energy decreases, and thus such a collision is an inelastic collision. Most of the collisions between objects in daily life are...
9.5K
Types Of Collisions - I01:04

Types Of Collisions - I

8.9K
When two objects come in direct contact with each other, it is called a collision. During a collision, two or more objects exert forces on each other in a relatively short amount of time. A collision can be categorized as either an elastic or inelastic collision. If two or more objects approach each other, collide and then bounce off, moving away from each other with the same relative speed at which they approached each other, the total kinetic energy of the system is said to be conserved. This...
8.9K

You might also read

Related Articles

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

Sort by
Same author

Flexible Prescribed-Time Optimal Control With Adaptive State-Input Constraint Bounds via Actor-Critic Learning.

IEEE transactions on neural networks and learning systems·2026
Same author

Toward Comprehensive Information-Theoretic Multi-View Learning.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Functional connectivity-based classification and subtyping of major depression for precision mental health: An ensemble graph neural network approach.

PLOS digital health·2026
Same author

DAMind: Zero-Shot Visual Cross-Domain Alignment and Representation for EEG Decoding.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Beyond depression symptoms: the default mode network as a predictor of antidepressant response.

Npj mental health research·2026
Same author

Fast Multi-view Discrete Clustering via Spectral Embedding Fusion.

IEEE transactions on pattern analysis and machine intelligence·2025
Same journal

A robust ATUB-Net for bearing fault diagnosis under unbalanced sample scenarios.

ISA transactions·2026
Same journal

Data-driven trajectory tracking control of UAV systems under a novel probability-selection event-triggered mechanism.

ISA transactions·2026
Same journal

Predefined-time affine formation tracking control of unmanned surface vehicles with input saturation via adaptive fuzzy observers.

ISA transactions·2026
Same journal

Adaptive fault-tolerant safety-guaranteed fuzzy event-triggered rendezvous control for heterogeneous USV-UUV systems.

ISA transactions·2026
Same journal

Two-stage maximum likelihood weighted recursive least squares algorithm for nonlinear systems and an application in wind tunnel systems.

ISA transactions·2026
Same journal

Enhancing interpretable soft sensing with embedded hybrid modeling: the GraphTrans approach for industrial processes.

ISA transactions·2026
See all related articles

Related Experiment Video

Updated: Jan 7, 2026

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.0K

Fixed-time formation control for multiagent systems with velocity-based collision avoidance.

Shuangsi Xue1, Zihang Guo2, Junkai Tan2

  • 1State Key Laboratory of Electrical Insulation and Power Equipment, and School of Electrical Engineering, Xi'an Jiaotong University, Xi'an, 710049, China; State Key Laboratory of Human-Machine Hybrid Augmented Intelligence, and The Institute of Artificial Intelligence and Robotics, Xi'an Jiaotong University, Xi'an, 710049, China.

ISA Transactions
|December 26, 2025
PubMed
Summary
This summary is machine-generated.

This study presents a novel fixed-time formation strategy for multiagent systems (MASs) using an extended state observer (ESO) and event-triggered control. The approach ensures collision-free formation tracking despite disturbances and reduces communication load.

Keywords:
Collision avoidanceEvent-triggeredFixed-timeFormation controlMultiagent systems

More Related Videos

Laboratory Drop Towers for the Experimental Simulation of Dust-aggregate Collisions in the Early Solar System
09:44

Laboratory Drop Towers for the Experimental Simulation of Dust-aggregate Collisions in the Early Solar System

Published on: June 5, 2014

13.3K
Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task
05:04

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task

Published on: September 21, 2017

6.4K

Related Experiment Videos

Last Updated: Jan 7, 2026

Operation of the Collaborative Composite Manufacturing CCM System
10:09

Operation of the Collaborative Composite Manufacturing CCM System

Published on: October 1, 2019

7.0K
Laboratory Drop Towers for the Experimental Simulation of Dust-aggregate Collisions in the Early Solar System
09:44

Laboratory Drop Towers for the Experimental Simulation of Dust-aggregate Collisions in the Early Solar System

Published on: June 5, 2014

13.3K
Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task
05:04

Bouncing Ball with a Uniformly Varying Velocity in a Metronome Synchronization Task

Published on: September 21, 2017

6.4K

Area of Science:

  • Robotics
  • Control Systems Engineering
  • Artificial Intelligence

Background:

  • Multiagent systems (MASs) face challenges in formation tracking due to external disturbances and unknown dynamics.
  • High computational load and communication costs are significant issues in complex MASs.
  • Existing fixed-time control strategies can suffer from high initial speeds, leading to potential collisions.

Purpose of the Study:

  • To develop a robust and efficient formation tracking strategy for collision-free MASs.
  • To mitigate the impact of external disturbances and unknown system dynamics on control performance.
  • To reduce computational and communication burdens within MASs.

Main Methods:

  • Design of an extended state observer (ESO) with sliding mode and bias radial basis function neural network (RBFNN) for rapid state estimation.
  • Formulation of a fixed-time formation control strategy utilizing feedback from the ESO.
  • Implementation of a distributed event-triggered mechanism to optimize controller update intervals.
  • Integration of a velocity-based artificial potential field (APF) to manage initial speeds and prevent collisions.

Main Results:

  • The proposed strategy achieves semi-globally ultimately fixed-time boundedness (SGUFTB) of the system, proven via Lyapunov theory.
  • The event-triggered mechanism effectively reduces communication and computational load.
  • The velocity-based APF successfully prevents inter-agent collisions and reduces actuator strain.
  • Comparative simulations with five omnidirectional robots validate the strategy's effectiveness.

Conclusions:

  • The developed fixed-time formation strategy enhances control performance and robustness in MASs.
  • The combination of ESO, event-triggered control, and APF offers an efficient solution for collision-free formation tracking.
  • The strategy demonstrates significant improvements in system stability and resource management.