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Artificial Life and Robotics

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27.09.2018 | Original Article

Gait generation for a biped robot with knees and torso via trajectory learning and state-transition estimation

verfasst von: Satoshi Satoh, Kenji Fujimoto

Erschienen in: Artificial Life and Robotics | Ausgabe 4/2018

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Abstract

The proposed method can generate an optimal feedforward control input and the corresponding optimal walking trajectory minimizing the \(L_2\) norm of the control input by iteration of laboratory experiments. Since a general walking motion involves discontinuous velocity transitions caused by the collision with the ground, the proposed method consists of the combination of a trajectory learning part and an estimation part of the discontinuous state transition mapping using the stored experimental data. We apply the proposed method to a kneed biped robot with a torso, where we also provide a technique to generate an optimal gait not only being energy-efficient but also avoiding the foot-scuffing problem.

Vorheriger Artikel Generating and analyzing hierarchical interaction in a flock of robotic swarms

Nächster Artikel Agent-based models for first- and second-order emergent collective behaviours of social amoeba Dictyostelium discoideum aggregation and migration phases

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Titel: Gait generation for a biped robot with knees and torso via trajectory learning and state-transition estimation
verfasst von: Satoshi Satoh
Kenji Fujimoto
Publikationsdatum: 27.09.2018
Verlag: Springer Japan
Erschienen in: Artificial Life and Robotics / Ausgabe 4/2018
Print ISSN: 1433-5298
Elektronische ISSN: 1614-7456
DOI: https://doi.org/10.1007/s10015-018-0476-4

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