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Probabilistic constraint reasoning with Monte Carlo integration to Robot Localization
November 2014
Olga2014
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.
M. Sc. dissertation
Olga Meshcheryakova
Pedro Sousa, Jorge Cruz
DEE/FCT/UNL
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Olga Meshcheryakova, Probabilistic constraint reasoning with Monte Carlo integration to Robot Localization, Pedro Sousa and Jorge Cruz (superv.), DEE/FCT/UNL, November 2014.
<b>Olga Meshcheryakova</b>, <u>Probabilistic constraint reasoning with Monte Carlo integration to Robot Localization</u>, Pedro Sousa and <a href="/people/members/view.php?code=3f6f0c9973cdaeab1a3dd815682bb0ac" class="supervisor">Jorge Cruz</a> (superv.), DEE/FCT/UNL, November 2014.
@mastersthesis {Olga2014, author = {Olga Meshcheryakova}, title = {Probabilistic constraint reasoning with Monte Carlo integration to Robot Localization}, school = {DEE/FCT/UNL}, note = {Pedro Sousa and Jorge Cruz (superv.); }, abstract = {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.}, month = {November}, year = {2014}, }
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