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pro vyhledávání: '"Jimmy Pettersson"'
Autor:
Jimmy Pettersson, Mattias Wahde
Publikováno v:
International Journal on Artificial Intelligence Tools. 16:507-536
A simulation software package (UFLibrary) implementing the utility function (UF) method for behavior selection in autonomous robots, is introduced and described by means of an example involving a simple exploration robot equipped with a repertoire of
Autor:
Mattias Wahde, Jimmy Pettersson
Publikováno v:
IEEE Transactions on Evolutionary Computation. 9:506-521
The generation of a complete robotic brain for locomotion based on the utility function (UF) method for behavioral organization is demonstrated. A simulated, single-legged hopping robot is considered, and a two-stage process is used for generating th
Publikováno v:
SMC
Anthropomorphic walking for a simulated bipedal robot has been realized by means of artificial evolution of central pattern generator (CPG) networks. The approach has been investigated through full rigid-body dynamics simulations in 3D of a bipedal r
Publikováno v:
SMC
In this paper, the performance of several evolutionary algorithms (EAs), involving different operators, is investigated in connection with the utility function (UF) method, a method for generating behavioral organization (selection) systems in autono
Publikováno v:
Proceedings of the 3rd International Symposium on Autonomous Minirobots for Research and Edutainment (AMiRE 2005) ISBN: 3540284966
AMiRE
AMiRE
In this paper, the performance of the utility function method for behavioral organization is investigated in the framework of a simple guard robot. In order to achieve the best possible results, it was found that high-order polynomials should be used
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::97955769129c1b0aee99081fd6c78b3e
https://doi.org/10.1007/3-540-29344-2_39
https://doi.org/10.1007/3-540-29344-2_39
Publikováno v:
IROS
This paper describes a method for optimization of waypoint selection for potential field navigation in autonomous robots. In the method presented here, a genetic algorithm (GA) is used for optimizing the potential field. The chromosome of each indivi