Toward Shape Optimization of Soft Robots
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
Toward Shape Optimization of Soft Robots
Author(s) :
Morzadec, Thomas [Auteur]
Deformable Robots Simulation Team [DEFROST ]
Marchal, Damien [Auteur]
Deformable Robots Simulation Team [DEFROST ]
Centre National de la Recherche Scientifique [CNRS]
Duriez, Christian [Auteur]
Deformable Robots Simulation Team [DEFROST ]
Deformable Robots Simulation Team [DEFROST ]
Marchal, Damien [Auteur]

Deformable Robots Simulation Team [DEFROST ]
Centre National de la Recherche Scientifique [CNRS]
Duriez, Christian [Auteur]

Deformable Robots Simulation Team [DEFROST ]
Conference title :
RoboSoft 2019 - IEEE International Conference on Soft Robotics
City :
Séoul
Country :
Corée du Sud
Start date of the conference :
2019-04-14
HAL domain(s) :
Informatique [cs]/Modélisation et simulation
English abstract : [en]
In this paper we present our work on shape optimization for soft robotics where the shape is optimized for a given soft robot usage. To obtain a parametric optimization with a reduced number of parameters, we rely on an ...
Show more >In this paper we present our work on shape optimization for soft robotics where the shape is optimized for a given soft robot usage. To obtain a parametric optimization with a reduced number of parameters, we rely on an approach where the designer progressively refines the parameter space and the fitness function until a satisfactory design is obtained. In our approach, we automatically generate FEM simulations of the soft robot and its environment to evaluate a fitness function while checking the consistency of the solution. Finally, we have coupled our framework to an evolutionary optimization algorithm, and demonstrated its use for optimizing the design of a deformable leg of a locomotive robot.Show less >
Show more >In this paper we present our work on shape optimization for soft robotics where the shape is optimized for a given soft robot usage. To obtain a parametric optimization with a reduced number of parameters, we rely on an approach where the designer progressively refines the parameter space and the fitness function until a satisfactory design is obtained. In our approach, we automatically generate FEM simulations of the soft robot and its environment to evaluate a fitness function while checking the consistency of the solution. Finally, we have coupled our framework to an evolutionary optimization algorithm, and demonstrated its use for optimizing the design of a deformable leg of a locomotive robot.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
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