Detalhes bibliográficos
Ano de defesa: |
2020 |
Autor(a) principal: |
Santana, Killdary Aguiar |
Orientador(a): |
Não Informado pela instituição |
Banca de defesa: |
Não Informado pela instituição |
Tipo de documento: |
Dissertação
|
Tipo de acesso: |
Acesso aberto |
Idioma: |
por |
Instituição de defesa: |
Não Informado pela instituição
|
Programa de Pós-Graduação: |
Não Informado pela instituição
|
Departamento: |
Não Informado pela instituição
|
País: |
Não Informado pela instituição
|
Palavras-chave em Português: |
|
Link de acesso: |
http://www.repositorio.ufc.br/handle/riufc/52702
|
Resumo: |
One of the biggest challenges encountered in mobile robots is planning their trajectory and has received attention from researchers, both in industry and academia. The great motivator of researchers in this area is the development of solutions that allow greater autonomy for robots. The complexity of the trajectory planning problem of these equipments has motivated the development of several algorithms. This comes from the need to integrate your navigation with your sensing, efficiency and route planning, as well as the need to save significant resources and the involvement of multiple agents. Most missions that must fulfill several points are quite complex, because in addition to the costs to complete them can also have weights that determine your priority of service. This complexity is increased by the possibility that several robots in different locations collaboratively participate in the mission. This paper presents a calculation model for multiple robots with energy constraints and base shifting using the combination of the team orientation problem and the multiple backpack problem. To verify the efficiency of the solution, two meta heuristic algorithms were developed, a genetic algorithm and a method that uses particle swarm optimization. The algorithms were tested on 20 self-generated instances with different robot numbers, battery capacities and deposits. For each instance 3 experiments were performed for normal execution, with reduction of robots and change of bases. |