A multiscale analysis of multi-agent coverage control algorithms

Krishnan, Vishaal; Martínez, Sònia

doi:10.1016/j.automatica.2022.110516

Cited by 6 publications

(2 citation statements)

References 31 publications

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“…It designed a distributed control law to optimize the position of multiple vehicles to achieve sensing measurement of the task area. After that, based on the gradient descent of the cost function and the Voronoi partitions, many researchers have further studied the coverage control problem of mobile agent networks and considered the physical constraints of mobile agents such as speed limit [9, 10], limited sensing or communication range [11], information density [12] in the task area, and so on [13]. In [14], the authors studied the effect of measurement error on the coverage cost function.…”

Section: Introductionmentioning

confidence: 99%

Time‐sensitive coverage control for non‐holonomic and heterogeneous robots: An extremum seeking framework and application

Zhang

Deng

Zhou

et al. 2023

IET Control Theory & Appl

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This work addresses a time‐sensitive coverage control problem for multiple non‐holonomic mobile robots. The coverage objective is evaluated by the total arrivial time of robots reaching their dominant clients with the predefined constant but heterogeneous velocities. An extremum‐seeking control framework is proposed, including a numerical optimizer that iteratively produces the optimal waypoints of robots minimizing the total arriving time and a state regulator that drives the robot's positions to the optimal locations under both constraints non‐holonomic kinematic model and the constant‐velocity. An improved K‐means algorithm is first designed to produce the waypoint sequence. A theoretical analysis of the convergence of coverage objectives is provided. Then a fixed‐time state regulator for the robot's angular velocity is derived from regulating robot positions to the desired waypoints within each time step that varies with the heterogeneous traversing velocity and the distance between waypoints. Finally, the results are validated by a virtual robotic simulation platform and the experiment on real robots, showing that the proposed methods can efficiently deploy multiple heterogeneous robots for time‐sensitive services to clients with a discrete distribution. The simulation and experimental code are open‐sourced at https://gitee.com/ahu‐icip/simulation.git.

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Section: Introductionmentioning

confidence: 99%

Time‐sensitive coverage control for non‐holonomic and heterogeneous robots: An extremum seeking framework and application

Zhang

Deng

Zhou

et al. 2023

IET Control Theory & Appl

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“…The tasks of spatial self-organization are solved by developing methods and algorithms for the collective behavior of mobile agents as parts of a multi-agent system [11]. In some cases, specialized solutions are developed for separate problems of spatial self-organization, for example, the problem of multi-agent coverage control [12], the problem of decentralized cluster formation [13], the problem of controlling a team of mobile agents for object transportation [14], etc. In most cases, problems of spatial self-organization are solved with various types of uncertainty [15].…”

Section: Introductionmentioning

confidence: 99%

Coscheduling Spatial Self-organization and Distributed Data Collection in Multi-agent System

Botchkaryov¹

2022

ACPS

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The problem of coscheduling spatial self-organization control processes and distributed data collection processes in a multi-agent system has been considered. The goal of coscheduling is to find and use the possibilities of functional coordination of these processes and increase the efficiency of the multi-agent system due to their parallel execution. An analysis of the main features of spatial self-organization tasks that affect the solution of the problem of coscheduling has been carried out. Variants of the mobile agent robotic platform configuration and the problem of the dependence of spatial self-organization algorithms on the type of robotic platform have been considered. A method of coscheduling of spatial self-organization and distributed data collection by coordinated parallel execution of the corresponding data collection process and the process of controlling mobile agent motion has been proposed. The method of coscheduling is implemented using the interaction protocol of these processes and the algorithm for planning their parallel execution using functional decomposition. The simulation results of the proposed method of coscheduling are given. It is proved that the proposed method of coscheduling provides acceleration of computations in the decision-making module of the mobile agent due to more efficient parallelization. On average, for typical values of parameters of control processes, the proposed method of coscheduling provides acceleration of computations in the decision-making module of the mobile agent by 40.6 %.

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Voronoi-based adaptive area optimal coverage control for multiple manipulator systems with uncertain kinematics and dynamics

Yu,

Mi,

Han

et al. 2024

Communications in Nonlinear Science and Numerical Simulation

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A multiscale analysis of multi-agent coverage control algorithms

Cited by 6 publications

References 31 publications

Time‐sensitive coverage control for non‐holonomic and heterogeneous robots: An extremum seeking framework and application

Time‐sensitive coverage control for non‐holonomic and heterogeneous robots: An extremum seeking framework and application

Coscheduling Spatial Self-organization and Distributed Data Collection in Multi-agent System

Voronoi-based adaptive area optimal coverage control for multiple manipulator systems with uncertain kinematics and dynamics

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