Six-and-a-half-month-old children positively attribute goals to human action and to humanoid-robot motion

2005 ◽  
Vol 20 (2) ◽  
pp. 303-320 ◽  
Author(s):  
Kazunori Kamewari ◽  
Masaharu Kato ◽  
Takayuki Kanda ◽  
Hiroshi Ishiguro ◽  
Kazuo Hiraki
Keyword(s):  
Humanoid Robot ◽  
Human Action ◽  
Robot Motion

scholarly journals Multicriteria Optimal Humanoid Robot Motion Generation

10.5772/4865 ◽  
2007 ◽  
Author(s):  
Genci Capi ◽  
Yasuo Nasu ◽  
Mitsuhiro Yamano ◽  
Kazuhisa Mitobe
Keyword(s):  
Humanoid Robot ◽  
Robot Motion ◽  
Motion Generation

Multi-objective optimisation for humanoid robot motion planning

2016 ◽  
Vol 10 (2) ◽  
pp. 112
Author(s):  
Xiandong Xu ◽  
Yi Guan ◽  
Bingrong Hong ◽  
Wende Ke ◽  
Qiubo Zhong ◽  
...  
Keyword(s):  
Motion Planning ◽  
Humanoid Robot ◽  
Robot Motion ◽  
Robot Motion Planning ◽  
Multi Objective

1A1-B05 On the Number of DOF and its Effect on the Naturalness of Humanoid Robot Motion

2007 ◽  
Vol 2007 (0) ◽  
pp. _1A1-B05_1-_1A1-B05_2
Author(s):  
Daisuke KURAKI ◽  
Abdelaziz KHIAT ◽  
Etsuko UEDA ◽  
Yoshio MATSUMOTO ◽  
Tsukasa OGASAWARA
Keyword(s):  
Humanoid Robot ◽  
Robot Motion

Walking Control of Humanoid Robots on Uneven Ground Using Fuzzy Algorithm

2019 ◽  
pp. 352-361
Author(s):  
Saeed Abdolshah ◽  
Mohammad Abdolshah ◽  
Majid Abdolshah ◽  
S. Vahid Hashemi
Keyword(s):  
Output Signal ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Moment Of Inertia ◽  
Robot Motion ◽  
Human Being ◽  
Fuzzy Algorithm ◽  
Walking Control ◽  
The Moment ◽  
Flat Ground

Walking control of humanoid robots is a challenging issue. In this chapter, a method for modeling humanoid robots is presented considering the human being indices such as DOFs, mass and the moment of inertia of the segments. In the next step, a walking pattern on the flat ground is generated and the robot motion is simulated in the MSC. Visual Nastran 4D™ software. ZMP trajectory of the simulated humanoid robot in walking cycle has been obtained. An uneven ground is generated in the software, where the robot falls down during the motion. A fuzzy algorithm is employed to balance the robot; input is defined as the differences between the projections of ZMP in flat and uneven ground and output is a compensative signal to make the robot follow the flat ground ZMP pattern to refuse the robot falling. Output signal is distributed in different joints to make faster and more effective compensation. Although the type of uneven ground can be important, but the robot could successfully pass the designed uneven ground in MSC.Visual Nastran 4D.

Application of Camera Image Processing to Control of Humanoid Robot Motion

2014 ◽  
Vol 474 ◽  
pp. 179-185
Author(s):  
Rastislav Ďuriš
Keyword(s):  
Image Processing ◽  
Computer Vision ◽  
Humanoid Robot ◽  
Humanoid Robots ◽  
Robot Motion ◽  
Potential Applications ◽  
Processing Techniques ◽  
And Control ◽  
Detection And Localization ◽  
Wide Potential

The wide potential applications of humanoid robots require that the robots can move in general environment, overcome various obstacles, detect predefined objects and control of its motion according to all these parameters. The goal of this paper is address the problem of implementation of computer vision to motion control of humanoid robot. We focus on using of computer vision and image processing techniques, based on which the robot can detect and recognize a predefined color object in a captured image. An algorithm to detection and localization of objects is described. The results obtained from image processing are used in an algorithm for controlling of the robot movement.

OPTIMIZATION OF HUMANOID ROBOT MOTION DURING THE ELEVATION OF AN OBJECT

2007 ◽  
Author(s):  
HAMED AJABI NAEINI ◽  
MOSTAFA ROSTAMI
Keyword(s):  
Humanoid Robot ◽  
Robot Motion
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