Continuification Control of Large-Scale Multiagent Systems Under Limited Sensing and Structural Perturbations

Maffettone, Gian Carlo; Porfiri, Maurizio; Bernardo, Mario di

doi:10.1109/lcsys.2023.3286773

Cited by 4 publications

(1 citation statement)

References 16 publications

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“…Typically, in these problems, it is assumed that the agents are relatively simple, and thus have limited communication and sensing capabilities, and limited computational resources; see, for example the robotic swarms described in Hauert et al (2009) ; Rubenstein et al (2014) ; Gardi et al (2022) . Sometimes, to cope with such big ensembles, macroscopic methods exploiting partial differential equations can be also suitable ( Biswal et al, 2021 ; Maffettone et al, 2023a , b ).…”

Section: Introductionmentioning

confidence: 99%

Distributed control for geometric pattern formation of large-scale multirobot systems

Giusti,

Maffettone,

Fiore

et al. 2023

Front. Robot. AI

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Introduction: Geometric pattern formation is crucial in many tasks involving large-scale multi-agent systems. Examples include mobile agents performing surveillance, swarms of drones or robots, and smart transportation systems. Currently, most control strategies proposed to achieve pattern formation in network systems either show good performance but require expensive sensors and communication devices, or have lesser sensor requirements but behave more poorly.Methods and result: In this paper, we provide a distributed displacement-based control law that allows large groups of agents to achieve triangular and square lattices, with low sensor requirements and without needing communication between the agents. Also, a simple, yet powerful, adaptation law is proposed to automatically tune the control gains in order to reduce the design effort, while improving robustness and flexibility.Results: We show the validity and robustness of our approach via numerical simulations and experiments, comparing it, where possible, with other approaches from the existing literature.

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