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Related Experiment Video

Updated: Jun 5, 2025

The HoneyComb Paradigm for Research on Collective Human Behavior

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

Spatial barycentric coordinates based distributed formation control for multi-agent systems.

Fangyuan Li¹, Jialing Ning¹, Hui Liu²

¹School of Electrical and Information Engineering, Zhengzhou University, China; Robot Perception and Control Engineering Research Center in Henan Province, China.

ISA Transactions

|December 13, 2024

Summary

This study presents a new formation control algorithm for multi-agent systems without GPS. It uses inter-agent distances and anchor node positions to achieve desired formations in 3D space.

Keywords:

Barycentric coordinates Distributed algorithm Formation control Multi-agent systems

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Spatial Separation of Molecular Conformers and Clusters

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Published on: January 9, 2014

Area of Science:

Robotics
Control Theory
Distributed Systems

Background:

Many multi-agent systems require specific formations for task execution.
Limited access to localization technologies like GPS poses a challenge for formation control.
Existing methods may not be suitable for decentralized systems operating in 3D space.

Purpose of the Study:

To investigate formation control for multi-agent systems in 3D space without relying on GPS.
To develop a distributed algorithm for achieving desired formations using inter-agent distances and anchor node positions.
To ensure unique representation of formation shapes using generalized spatial barycentric coordinates.

Main Methods:

Representing agent positions using generalized spatial barycentric coordinates.
Deriving conditions for unique formation shape representation.
Proposing a distributed spatial formation control algorithm based on this representation.
Validating the algorithm through simulation studies.

Main Results:

A novel method for representing agent positions in 3D space using generalized spatial barycentric coordinates.
Conditions established for the unique representation of formation shapes.
A distributed algorithm demonstrated to guide agents to desired formations effectively.
Simulation results confirm the algorithm's correctness and effectiveness.

Conclusions:

The proposed approach enables robust formation control for multi-agent systems lacking GPS.
Generalized spatial barycentric coordinates provide a viable framework for decentralized formation tasks.
The developed algorithm offers a practical solution for real-world applications requiring precise formations.