Modelagem em programação linear inteira mista para alocação de tarefas em sistemas multi-robôs

• Ano de defesa: 2025
• Autor(a) principal: Latini, Igor Pardal
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Tecnológica Federal do Paraná; Curitiba; Brasil; Programa de Pós-Graduação em Engenharia Elétrica e Informática Industrial; UTFPR
• Palavras-chave em Português: Robótica; Programação linear; Automação - Aplicações industriais; Robôs - Sistemas de controle; Robôs móveis; Robotics; Linear programming; Automation - Industrial applications; Robots - Control systems; Mobile robots; CNPQ::ENGENHARIAS::ENGENHARIA ELETRICA::ELETRONICA INDUSTRIAL, SISTEMAS E CONTROLES ELETRONICOS; Engenharia Elétrica
• Link de acesso: http://repositorio.utfpr.edu.br/jspui/handle/1/37815

Immersed in a context of growing search for productivity and operational improvements, the use of mobile robotics in industrial environments is especially important, being fundamental to meet the needs of factories. This usage highlights the presence of multiple robots, acting cooperatively to execute the available tasks in each process. However, this practice raises the question of how to allocate each task to each robot in order to obtain the best possible distribution. Based on this inquiry, this work presents the development of an optimization model using Mixed-Integer Linear Programming (MILP), with the objective of determining that organization, considering the characteristics of each robot and each task. Using the minimization of delays in task execution as an objective function, the developed system applies several restrictions created with reference to different configurations, such as task precedence, navigation method and cooperation between robots, to establish the order and optimized assignment of activities. By running the model in ten distinct study cases, exemplifying each parameterization possibility and including those based on real situations, the results obtained provide the best allocation for each case, informing which robot should execute which task and when that activity should be started. Thus, the proposed modeling demonstrates being capable of incorporating different scenarios and optimizing activity distribution for heterogeneous robots, facilitating the comparison between configurations and enabling better planning in an industrial environment.