Consensus-Based Formation of Second-Order Multi-Agent Systems via Linear-Transformation-Based Partial Stability Approach

Qu, Xianlin; Wan, Yunfan; Zhou, Peng; Liu, Ligang

doi:10.1109/access.2019.2952924

Cited by 3 publications

(3 citation statements)

References 26 publications

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“…More similar to the developed method is an approach presented by Lee and Chwa that is decentralized-behavior-based [12] and only uses relative distance information between neighbors and obstacles. Another similar method is consensus formation control [13]. Limited work on entropy control or analysis of multi-agent systems exists in the literature, aside from a recent paper on potentially using cross-entropy to determine the robustness of a multi-agent group [14].…”

Section: Related Workmentioning

confidence: 99%

Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)

Smith¹,

Sevil²

2022

Robotics

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The aim of this research effort was to develop a framework for a structure from motion (SfM)-based 3D reconstruction approach with a team of autonomous small unmanned aerial systems (sUASs) using a distributed behavior model. The framework is composed of two major goals to accomplish this: a distributed behavior model for a team of sUASs and a SfM-based 3D reconstruction using team of sUASs. The developed distributed behavior model is based on the entropy of the system, and when the entropy of the system is high, the sUASs get closer to reducing the overall entropy. This is called the grouping phase. If the entropy is less than the predefined threshold, then the sUASs switch to the 3D reconstruction phase. The novel part of the framework is that sUASs are only given the object of interest to reconstruct the 3D model, and they use the developed distributed behavior to coordinate their motion for that goal. A comprehensive parameter analysis was performed, and optimum sets of parameters were selected for each sub-system. Finally, optimum parameters for two sub-systems were combined in a simulation to demonstrate the framework’s operability and evaluate the completeness and speed of the reconstructed model. The simulation results show that the framework operates successfully and is capable of generating complete models as desired, autonomously.

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Section: Related Workmentioning

confidence: 99%

Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)

Smith¹,

Sevil²

2022

Robotics

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“…More similar to our developed method is an approach presented by Lee and Chwa that is decentralized behavior-based [32] and only uses relative distance information between neighbors and obstacles. Another similar method is consensus formation control [33]. Moreover, a method that uses an artificial potential field for obstacle avoidance and a consensus algorithm for maintenance and reconstruction of the formation has been introduced in the literature [34].…”

Section: Introductionmentioning

confidence: 99%

Entropy-Based Distributed Behavior Modeling for Multi-Agent UAVs

Fina

Smith

Carnahan³

et al. 2022

Drones

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This study presents a novel distributed behavior model for multi-agent unmanned aerial vehicles (UAVs) based on the entropy of the system. In the developed distributed behavior model, when the entropy of the system is high, the UAVs get closer to reduce the overall entropy; this is called the grouping phase. If the entropy is less than the predefined threshold, then the UAVs switch to the mission phase and proceed to a global goal. Computer simulations are performed in AirSim, an open-source, cross-platform simulator. Comprehensive parameter analysis is performed, and parameters with the best results are implemented in multiple-waypoint navigation experiments. The results show the feasibility of the concept and the effectiveness of the distributed behavior model for multi-agent UAVs.

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“…Many researchers have recently studied consensus issues from different perspectives. This is mainly due to their broad application in many fields, including robots [5][6][7][8][9], formations [10][11][12][13], unmanned aerial vehicles [14][15][16][17][18], unmanned surface vehicles [15,[19][20][21], unmanned underwater vehicles [21,22], sensor networks [23,24], etc.…”

Section: Introductionmentioning

confidence: 99%

Modeling of a non-linear multi-agent distributed control system

Tan

Lee

Tai

et al. 2022

JMES

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The goal of our research aims to develop a mathematical model for consensus control system based on Lyapunov Theory and nonlinear dynamics functional equations. This paper describes a new solution that deals with the general-consensus problem and the leader-following consensus problem of non-linear multi-agent system (NMAS) in which the parameters of all follower agents can be different, and with an unforced agent as the leader in the multi-agent system (MAS). Different control rules were constructed for each different follower agent based on its own state variables and its communication with adjacent agents. Finally, numerical simulations are provided to demonstrate the feasibility of the developed mathematical model. The results have demonstrated the designed distributed control system satisfy the Lyapunov Theory since all the agents have converged to its steady state after a period of time.

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Consensus-Based Formation of Second-Order Multi-Agent Systems via Linear-Transformation-Based Partial Stability Approach

Cited by 3 publications

References 26 publications

Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)

Design of a Rapid Structure from Motion (SfM) Based 3D Reconstruction Framework Using a Team of Autonomous Small Unmanned Aerial Systems (sUAS)

Entropy-Based Distributed Behavior Modeling for Multi-Agent UAVs

Modeling of a non-linear multi-agent distributed control system

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