Utilize este identificador para referenciar este registo: http://hdl.handle.net/10362/14145
Título: Probabilistic constraint reasoning with Monte Carlo integration to robot localization
Autor: Meshcheryakova, Olga
Orientador: Sousa, Pedro
Cruz, Jorge
Palavras-chave: Continuous constraint programming
Interval analysis
Monte Carlo integration
Robot localization
Data de Defesa: Set-2014
Resumo: This work studies the combination of safe and probabilistic reasoning through the hybridization of Monte Carlo integration techniques with continuous constraint programming. In continuous constraint programming there are variables ranging over continuous domains (represented as intervals) together with constraints over them (relations between variables) and the goal is to find values for those variables that satisfy all the constraints (consistent scenarios). Constraint programming “branch-and-prune” algorithms produce safe enclosures of all consistent scenarios. Special proposed algorithms for probabilistic constraint reasoning compute the probability of sets of consistent scenarios which imply the calculation of an integral over these sets (quadrature). In this work we propose to extend the “branch-and-prune” algorithms with Monte Carlo integration techniques to compute such probabilities. This approach can be useful in robotics for localization problems. Traditional approaches are based on probabilistic techniques that search the most likely scenario, which may not satisfy the model constraints. We show how to apply our approach in order to cope with this problem and provide functionality in real time.
URI: http://hdl.handle.net/10362/14145
Designação: Dissertação
Aparece nas colecções:FCT: DEE - Dissertações de Mestrado

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