Fuzzy Modeling of Zero Moment Point Trajectory for a Biped Walking Robot

Kim, Dong-Won; Kim, Nak Hyun; Seo, Sam-Jun; Park, Gwi-Tae

doi:10.1007/978-3-540-30133-2_94

Cited by 13 publications

(11 citation statements)

References 7 publications

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“…The concept of the zero moment point (ZMP) [12] is known to give good results in order to control this interaction. As an important criterion for the stability of the walk, the trajectory of the ZMP beneath the robot foot during the walk is investigated [11][12][13][14][15][16][17]. Through the ZMP, we can synthesize the walking patterns of humanoid robot and demonstrate the walking motion with real robots.…”

Section: Practical Biped Humanoid Robotmentioning

confidence: 99%

Use of an Evolutionary Inductive Self-Organizing Network for Uncertain Nonlinear and Robotic Systems

Kim

Seo

Kim

2012

International Journal of Advanced Robotic Systems

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We discuss a new design methodology for an inductive self-organizing network using an evolutionary algorithm and its practical applications. The inductive self-organizing network centres on the idea of a group method for data handling. The performances of this network depend strongly on the number of input variables available to the model and the number of input variables and type (order) of the polynomials to each node. They must be fixed by the designer in advance before the architecture is constructed. So the trial and error method must be used with its heavy computation burden and low efficiency. Moreover it does not guarantee that the obtained model is the best one. In this paper, we propose an evolutionary inductive selforganizing network to alleviate these problems. The order of the polynomial, the number of input variables and the optimum input variables are encoded as a chromosome and the fitness of each chromosome is computed. So the appropriate information for each node is evolved accordingly and tuned gradually throughout the genetic iterations. We can show that the proposed model is a sophisticated and versatile architecture which can construct models for limited data sets, as well as heavy complex robotic systems.

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Section: Practical Biped Humanoid Robotmentioning

confidence: 99%

Use of an Evolutionary Inductive Self-Organizing Network for Uncertain Nonlinear and Robotic Systems

Kim

Seo

Kim

2012

International Journal of Advanced Robotic Systems

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“…Examples of plants which are difficult to model are arc furnaces (Wilson (1997)) and helicopter rotors (Lohar (2000)). Biped robots on the other hand can be modeled but they exhibit time varying nonlinearities (Wolkotte (2003); Kim et al (2004); Caballero et al (2004) …”

Section: Model Free Controlmentioning

confidence: 99%

Model-free Subspace Based Dynamic Control of Mechanical Manipulators

Saleem¹,

Sultan²

2008

Robotics Automation and Control

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“…The data is divided into training data set (Nos. [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] and testing data set (Nos. 21-40).…”

Section: Evaluation Of the Eisonmentioning

confidence: 99%

“…The concept of the zero moment point (ZMP) [2] is known to give good results in order to control this interaction. As an important criterion for the stability of the walk, the trajectory of the ZMP beneath the robot foot during the walk is investigated [1][2][3][4][5][6][7]. Through the ZMP, we can synthesize the walking patterns of humanoid robot and demonstrate walking motion with real robots.…”

Section: Introductionmentioning

confidence: 99%