Adapting to sensing and actuation variations in multi-robot coverage

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dc.creatorAlyssa Pierson
dc.creatorLucas Figueiredo
dc.creatorLuciano Cunha de Araújo Pimenta
dc.creatorMac Schwager
dc.date.accessioned2025-04-03T15:12:39Z
dc.date.accessioned2025-09-09T00:38:47Z
dc.date.available2025-04-03T15:12:39Z
dc.date.issued2017
dc.identifier.doihttps://doi.org/10.1177/0278364916688103
dc.identifier.issn0278-3649
dc.identifier.urihttps://hdl.handle.net/1843/81264
dc.languageeng
dc.publisherUniversidade Federal de Minas Gerais
dc.relation.ispartofThe international journal of robotics research
dc.rightsAcesso Restrito
dc.subjectRobôs - Sistemas de controle
dc.subjectRobótica
dc.subject.othermethod of using adaptive weightings to adjust for individual variations in performance within multi-robot coverage control
dc.subject.othermodify the Voronoi boundaries between neighboring robots, which adjusts a robot’s cell size relative to its neighbors
dc.subject.othermethod incorporates performance error into the decentralized algorithm while maintaining stability and performance
dc.titleAdapting to sensing and actuation variations in multi-robot coverage
dc.typeArtigo de periódico
local.citation.epage354
local.citation.issue3
local.citation.spage337
local.citation.volume36
local.description.resumoThis article considers the problem of multi-robot coverage control, where a group of robots has to spread out over an environment to provide coverage. We propose a new approach for a group of robots carrying out this collaborative task that will adapt online to performance variations among the robots. Two types of performance variations are considered: variations in sensing performance (e.g. differences in sensor types, calibration, or noise), and variations in actuation (e.g. differences in terrain, vehicle types, or lossy motors). The robots have no prior knowledge of the relative strengths of their performance compared to the others in the team. We present an algorithm that learns the relative performance variations among the robots online, in a distributed fashion, and automatically compensates by assigning the weak robots a smaller portion of the environment and the strong robots a larger portion. Using a Lyapunov-type proof, we show that the robots converge to a locally optimal coverage configuration. The algorithm is also demonstrated in both MATLAB simulations and experiments with Pololu m3pi ground robots.
local.publisher.countryBrasil
local.publisher.departmentENG - DEPARTAMENTO DE ENGENHARIA ELETRÔNICA
local.publisher.initialsUFMG
local.url.externahttps://journals.sagepub.com/doi/full/10.1177/0278364916688103

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