Multi-robot on-line sampling scheduler for persistent monitoring

dc.creatorArmando Alves Neto
dc.creatorDouglas Guimarães Macharet
dc.date.accessioned2025-05-05T16:12:08Z
dc.date.accessioned2025-09-08T23:24:14Z
dc.date.available2025-05-05T16:12:08Z
dc.date.issued2019
dc.identifier.doi10.1109/ICAR46387.2019.8981550
dc.identifier.urihttps://hdl.handle.net/1843/82022
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartof19th International Conference on Advanced Robotics (ICAR)
dc.rightsAcesso Restrito
dc.subjectModelos matemáticos
dc.subject.otherPath planning
dc.subject.otherSampling Rate , Knowledge Of The Phenomenon , Fourier Transform , Higher Frequency , Heuristic , Fast Fourier Transform , Balance Of System , Wireless Sensor , Continuous Line , Discrete Fourier Transform , Wireless Sensor Networks , Set Of Sensors , Regular Sampling , Traveling Salesman Problem , Swarm Robotics , Sink Node , Interpolation Error , Hamiltonian Path
dc.titleMulti-robot on-line sampling scheduler for persistent monitoring
dc.typeArtigo de evento
local.description.resumoThe employment of autonomous agents for persistent monitoring tasks has significantly increased in recent years. In this sense, the data collection process must take into account limited resources, such as time and energy, whilst acquiring a sufficient amount of data to generate accurate models of underlying phenomena. Many different schedulers in the literature act in an off-line manner, which means they define the sequence of visit and generate a set of paths before any observations are made. However, on-line approaches can adapt their behavior based on previously collected data, allowing to obtain more precise models. In this paper, we propose an on-line scheduler which evaluates the sampling rate of the signals being measured to assign different priorities to different Points-of-Interest (PoIs). Next, according to this priority, it is determined if a region must be visited more or less frequently to increase our knowledge of the phenomenon. Our methodology was evaluated through several experiments in a simulated environment.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.departmentICX - DEPARTAMENTO DE CIÊNCIA DA COMPUTAÇÃO
local.publisher.initialsUFMG
local.url.externahttps://ieeexplore.ieee.org/document/8981550

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