Skip navigation
Use este identificador para citar ou linkar para este item: https://repositorio.ufpe.br/handle/123456789/20827
Título: Probabilistic analysis applied to robots
Autor(es): ARAÚJO, Rafael Pereira de
Palavras-chave: Verificação de Modelos Probabilísticos; Linguagem Específica de Domínio; Algoritmos de Movimentação de Robôs; PRISM; Fórmulas Probabilísticas Temporais; Engenharia Directionada a Modelos
Data do documento: 15-Set-2016
Editor: Universidade Federal de Pernambuco
Resumo: Robots are increasingly being used in industry and starting their way to our homes as well. Nonetheless, the most frequently used techniques to analyze robots motion are based on simulations or statistical experiments made from filming robots’ movements. In this work we propose an alternative way of performing such analysis by using Probabilistic Model Checking with the language and tool PRISM. With PRISM we can perform simulations as well as check exhaustively whether a robot motion planning satisfies specific Probabilistic Temporal formulas. Therefore we can measure energy consumption, time to complete missions, etc., and all of these in terms of specific motion planning algorithms. As consequence we can also determine if an algorithm is superior to another in certain metrics. Furthermore, to ease the use of our work, we hide the PRISM syntax by proposing a more user-friendly DSL. As a consequence, we created a translator from the DSL to PRISM by implementing the translation rules and also, a preliminary investigation about its relative completeness by using the grammatical elements generation tool LGen. We illustrate those ideas with motion planning algorithms for home cleaning robots.
URI: https://repositorio.ufpe.br/handle/123456789/20827
Aparece na(s) coleção(ções):Dissertações de Mestrado - Ciência da Computação

Arquivos deste item:
Arquivo Descrição TamanhoFormato 
dissertacao_mestrado_rafael_araujo.pdf1,29 MBAdobe PDFVer/Abrir


Este arquivo é protegido por direitos autorais



Este item está licenciada sob uma Licença Creative Commons Creative Commons