Zero-moment point trajectory modelling of a biped walking robot using an adaptive neuro-fuzzy system

Kim, D.; Seo, Sam-Jun; Park, G.-T.

doi:10.1049/ip-cta:20045007

Cited by 40 publications

(37 citation statements)

References 4 publications

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“…In general most bipedal robots use the target ZMP (Zero Moment Point) (Vukobratovic et al 1990) control algorithm for locomotion in particular on different and uneven terrains, which requires precise modeling and actuation with high control gains (Kim et al 2005;Huang et al 2008). However, there are also other interesting approaches for bipedal walking on different terrains.…”

Section: Discussion and Outlookmentioning

confidence: 99%

Using efference copy and a forward internal model for adaptive biped walking

2010

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To behave properly in an unknown environment, animals or robots must distinguish external from selfgenerated stimuli on their sensors. The biologically inspired concepts of efference copy and internal model have been successfully applied to a number of robot control problems. Here we present an application of this for our dynamic walking robot RunBot. We use efference copies of the motor commands with a simple forward internal model to predict the expected self-generated acceleration during walking. The difference to the actually measured acceleration is then used to stabilize the walking on terrains with changing slopes through its upper body component controller. As a consequence, the controller drives the upper body component (UBC) to lean forwards/backwards as soon as an error occurs resulting in dynamical stable walking. We have evaluated the performance of the system on four different track configurations. Furthermore we believe that the experimental studies pursued here will sharpen our understanding of how the efference copies influence dynamic locomotion Electronic supplementary material The online version of this article (doi:10.1007/s10514-010-9199-7) contains supplementary material, which is available to authorized users.J. Schröder-Schetelig Max Planck Institute for Dynamics and Self-Organization, Bunsenstraße 10, 37073 Göttingen, Germany e-mail: johannes.schroeder-schetelig@ds.mpg.de P. Manoonpong · F. Wörgötter ( ) Bernstein Center for Computational Neuroscience (BCCN), University of Göttingen, Friedrich-Hund-Platz 1, 37077 Göttingen, Germany e-mail: worgott@physik3.gwdg.de P. Manoonpong e-mail: poramate@physik3.gwdg.de control to the benefit of modern neural control strategies in robots.

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Section: Discussion and Outlookmentioning

confidence: 99%

Using efference copy and a forward internal model for adaptive biped walking

2010

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“…Kim et al [14][15][16][17] employed various computational intelligence methods to design a model of robotic locomotion based on the determination of the ZMP trajectories. The ZMP, which is defined as the point on the ground about which the sum of all the moments of the active forces equals zero, is indispensable in ensuring dynamic stability of a biped robot.…”

Section: Introductionmentioning

confidence: 99%

ZMP based neural network inspired humanoid robot control

Kim

Park³

2011

Nonlinear Dyn

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This paper concerns ZMP-based control that is inspired by artificial neural networks for humanoid robot walking on varying sloped surfaces. Humanoid robots are currently one of the most exciting research topics in the field of robotics, and maintaining stability while they are standing, walking or moving is a key concern. To ensure a steady and smooth walking gait of such robots, a feedforward type of neural network architecture, trained by the back-propagation algorithm, is employed. The inputs and outputs of the neural network architecture are the ZMPx and ZMPy errors of the robot, and the x, y positions of the robot, respectively. The neural network developed allows the controller to generate the desired balance of the robot positions, resulting in a steady gait for the robot as it moves around on a flat floor, and when it is descending or ascending slopes. In this paper, experiments of humanoid robot walking are carried out, in which the actual position data from a prototype robot are measured in real-time situations, and fed into a neural network inspired controller designed for stable bipedal walking. In addition, natural walking motions on the different surfaces with varying slopes are obtained and the performance of the resulting controller is shown to be satisfactory.

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“…Each foot is equipped with four force sensors and rubber bushing which protects the sensors and the robot from the touchdown impacts. The specifications of the proposed humanoid robot are given in Table 1 and more detailed information and a block diagram of the robotic system can be found in [8].…”

Section: Humanoid Robot System and Its Zmpmentioning

confidence: 99%

“…Vukobratovic et al, [7] investigated the walking dynamics and has proposed ZMP as a good index for walking stability. Kim et al [8,9] employed various computational intelligence methods to design a model of robotic locomotion based on the determination of the ZMP trajectories. The ZMP, which is defined as the point on the ground about which the sum of all the moments of the active forces equals zero, is indispensable in ensuring dynamic stability of a biped robot.…”

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confidence: 99%

Neural Network Control of Humanoid Robot

Kim¹,

Kim²,

Park³

2010

Journal of Institute of Control, Robotics and Systems

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This paper handles ZMP based control that is inspired by neural networks for humanoid robot walking on varying sloped surfaces. Humanoid robots are currently one of the most exciting research topics in the field of robotics, and maintaining stability while they are standing, walking or moving is a key concern. To ensure a steady and smooth walking gait of such robots, a feedforward type of neural network architecture, trained by the back propagation algorithm is employed. The inputs and outputs of the neural network architecture are the ZMPx and ZMPy errors of the robot, and the x, y positions of the robot, respectively. The neural network developed allows the controller to generate the desired balance of the robot positions, resulting in a steady gait for the robot as it moves around on a flat floor, and when it is descending slope. In this paper, experiments of humanoid robot walking are carried out, in which the actual position data from a prototype robot are measured in real time situations, and fed into a neural network inspired controller designed for stable bipedal walking.Keywords: neural network, humanoid robot control, ZMP (Zero Moment Point) I. 서론 Throughout history, the human body and mind have inspired artists, engineers, and scientists. The field of humanoid robotics focuses on the creation of robots that are directly inspired by human capabilities. These robots usually share similar kinematics to humans, as well as similar sensing and behavior. The motivations that have driven the development of humanoid robots vary widely [1]. The primary motives are expected to assist human beings, cooperate with people and be stable enough not to fall down to avoid hurting nearby humans, other objects as well as damaging their own bodies. Keeping the humanoid robots stable and steady when they are standing, walking or moving is one of the fundamental functions and most important issue that needs to be addressed. To handle this important issue, many intelligent control schemes have been proposed [2][3][4][5]. However, most of these proposed schemes have produced restricted simulation results only, and so it is difficult to analyze the real stabilities of actual humanoid robots based on their walking patterns.As for the indices of biped walking robots to improve robotic stability, the zero moment point (ZMP) has been introduced and is commonly used for the gait planning of biped humanoid robots. This is a key point in the control of ASIMO [6], a 26-DOF humanoid robot developed by Honda Motor Company in 2000. Vukobratovic et al., [7] investigated the walking dynamics and has proposed ZMP as a good index for walking stability. Kim et al. [8,9] employed various computational intelligence methods to design a model of robotic locomotion based on the determination of the ZMP trajectories. The ZMP, which is defined as the point on the ground about which the sum of all the moments of the active forces equals zero, is indispensable in ensuring dynamic stability of a biped robot. If the ZMP is inside the ground s...

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Zero-moment point trajectory modelling of a biped walking robot using an adaptive neuro-fuzzy system

Cited by 40 publications

References 4 publications

Using efference copy and a forward internal model for adaptive biped walking

Using efference copy and a forward internal model for adaptive biped walking

ZMP based neural network inspired humanoid robot control

Neural Network Control of Humanoid Robot

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