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Nicolas Berger

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Papers by Nicolas Berger

Research paper thumbnail of Modélisation et résolution en programmation par contraintes de problèmes mixtes continu/discret de satisfaction de contraintes et d'optimisation

Directeur de thèse : Laurent G, Professeur Encadrant de thèse : Frédéric G, Maî... more Directeur de thèse : Laurent G, Professeur Encadrant de thèse : Frédéric G, Maître de Conférences Laboratoire : L 'I  N A U C 6241 – 2, rue de la Houssinière – BP 92208 ...

Research paper thumbnail of Some Interval Approximation Techniques for MINLP

Symposium on Abstraction, Reformulation and Approximation, 2009

MINLP problems are hard constrained optimization problems, with nonlinear constraints and mixed d... more MINLP problems are hard constrained optimization problems, with nonlinear constraints and mixed dis- crete continuous variables. They can be solved using a Branch-and-Bound scheme combining several methods, such as linear programming, interval analysis, and cut- ting methods. Our goal is to integrate constraint pro- gramming techniques in this framework. Firstly, global constraints can be introduced to reformulate MINLP problems thus

Research paper thumbnail of Finding the Maximal Pose Error in Robotic Mechanical Systems Using Constraint Programming

Trends in Applied Intelligent Systems, 2010

The position and rotational errors-also called pose errorsof the end-effector of a robotic mechan... more The position and rotational errors-also called pose errorsof the end-effector of a robotic mechanical system are partly due to its joints clearances, which are the play between their pairing elements. In this paper, we model the prediction of those errors by formulating two continuous constrained optimization problems that turn out to be NPhard. We show that techniques based on numerical constraint programming can handle globally and rigorously those hard optimization problems. In particular, we present preliminary experiments where our global optimizer is very competitive compared to the best-performing methods presented in the literature, while providing more robust results.

Research paper thumbnail of Modélisation et résolution en programmation par contraintes de problèmes mixtes continu/discret de satisfaction de contraintes et d'optimisation

Directeur de thèse : Laurent G, Professeur Encadrant de thèse : Frédéric G, Maî... more Directeur de thèse : Laurent G, Professeur Encadrant de thèse : Frédéric G, Maître de Conférences Laboratoire : L 'I  N A U C 6241 – 2, rue de la Houssinière – BP 92208 ...

Research paper thumbnail of Some Interval Approximation Techniques for MINLP

Symposium on Abstraction, Reformulation and Approximation, 2009

MINLP problems are hard constrained optimization problems, with nonlinear constraints and mixed d... more MINLP problems are hard constrained optimization problems, with nonlinear constraints and mixed dis- crete continuous variables. They can be solved using a Branch-and-Bound scheme combining several methods, such as linear programming, interval analysis, and cut- ting methods. Our goal is to integrate constraint pro- gramming techniques in this framework. Firstly, global constraints can be introduced to reformulate MINLP problems thus

Research paper thumbnail of Finding the Maximal Pose Error in Robotic Mechanical Systems Using Constraint Programming

Trends in Applied Intelligent Systems, 2010

The position and rotational errors-also called pose errorsof the end-effector of a robotic mechan... more The position and rotational errors-also called pose errorsof the end-effector of a robotic mechanical system are partly due to its joints clearances, which are the play between their pairing elements. In this paper, we model the prediction of those errors by formulating two continuous constrained optimization problems that turn out to be NPhard. We show that techniques based on numerical constraint programming can handle globally and rigorously those hard optimization problems. In particular, we present preliminary experiments where our global optimizer is very competitive compared to the best-performing methods presented in the literature, while providing more robust results.

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