The Community for Technology Leaders
2016 International Conference on Frontiers of Information Technology (FIT) (2016)
Islamabad, Pakistan
Dec. 19, 2016 to Dec. 21, 2016
ISBN: 978-1-5090-5300-1
pp: 184-188
Hilmi Fadli , School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung 40132, Indonesia
Egi Hidayat , School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung 40132, Indonesia
Carmadi Machbub , School of Electrical Engineering and Informatics, Institut Teknologi Bandung, Jalan Ganesha 10, Bandung 40132, Indonesia
ABSTRACT
Humanoid robot is a dynamic robot which can walk with two legs like a human. Many researchers are interested in this class of robot due to its flexibility in reaching a variety of terrain and also its similarity to humans. The main problem often faced by developers is the motion control system which is rather nontrivial. Having 25 DOF with only two legs, it is difficult to model the robot movement that achievesstatic and dynamic balance. To facilitate the developer, there needs to be a model that can be used as a foundation for the development of robot motion system. This model allows us to obtain the information which needed in the formation of the robot posture. Information such as joint position, position of the center of mass, ZMP position, support polygon and load torque of each joint can affect the balance of the formed posture. In addition, with the establishment of some proper posture, it is possible to determine several sequential postures that generate a walking pattern. The walking pattern enablesthe humanoid robot to move to another place. However, in reality, this robot movement often has a shifting trajectory from the target location due to various reasons. A Neuro-Fuzzy system has been designed and implemented to compensate error in trajectory tracking. This system processes the trajectory shift by fuzzy logic (linguistic) combined with learning algorithms that allows the system to change its own structures to produce better control output. Smaller shift on trajectory is achieved as the number of learning iteration increases.
INDEX TERMS
Neuro-Fuzzy, humanoid robot, NAO, modelling, walking pattern
CITATION
Hilmi Fadli, Egi Hidayat, Carmadi Machbub, "Design and implementation of walking pattern and trajectory compensator of NAO humanoid robot", 2016 International Conference on Frontiers of Information Technology (FIT), vol. 00, no. , pp. 184-188, 2016, doi:10.1109/FIT.2016.7857562
174 ms
(Ver 3.3 (11022016))