Sistema multiagente para controle de veículos autônomos

• Ano de defesa: 2014
• Autor(a) principal: Branisso, Lucas Binhardi
• Orientador(a): Kato, Edilson Reis Rodrigues
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• País: BR
• Palavras-chave em Português: Inteligência artificial; Sistemas multiagentes; Associação de tarefas; Veículos a motor - frotas; Algoritmos genéticos
• Palavras-chave em Inglês: Multi-agent system; Task assignemnt; Vehicle fleet; Warehouse; Genetic algorithm
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: https://repositorio.ufscar.br/handle/ufscar/570

Vehicle fleets are an important component in several applications, moving materials and people. Examples include material handling in warehouses, factories and port terminals, people transportation as in taxi fleets and emergency services, such as medical assistance, fire-fighters and police. Fleet operation is crucial for these applications: it can mean loss of money and commercial partners in case of industry, os loss of lives in case of emergency services. Controlling the fleet to achieve efficient levels of performance is a difficult problem, though, and becomes even harder as the fleet grows. Research in the area has been linking vehicle fleet operation to Multi-Agent Systems, because vehicle fleets are naturally distributed and Multi-agent System is a convenient abstraction to cope with distributed Artificial Intelligence problems. Therefore, it is proposed a Multi-Agent System to control vehicle fleets, focusing on material handling application in warehouses. The proposed system has three types of agents: Vehicle Agent, Loading Point Agent and Storage Point Agent. Agents interact amongst themselves through messages, trying to efficiently realize the material handling in a warehouse. System implementation is done through a simulation of a warehouse operation, built on top of MASON multi-agent system simulation platform. Task assignment strategies is also an important problem, therefore four strategies are shown and tested using the simulation: CNET, Fuzzy, DynCNET and FiTA. To enable comparison among these strategies, a Genetic Algorithm is employed to systematically search good parameters for each strategy. The proposed system, as well as the simulation, are offered as a framework for development of other vehicle fleets controlling multi-agent systems and/or task assignment strategies.