• Proceedings of the Korean Society of Precision Engineering Conference
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• 2009.10a
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• pp.359-360
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• 2009

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.358-364
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• 2007

Intelligent walking modeling of humanoid robot using learning based neuro-fuzzy system is presented in this paper. Walking pattern, trajectory of the zero moment point (ZMP) in a humanoid robot is used as an important criterion for the balance of the walking robots but its complex dynamics makes robot control difficult. In addition, it is difficult to generate stable and natural walking motion for a robot. To handle these difficulties and explain empirical laws of the humanoid robot, we are modeling practical humanoid robot using neuro-fuzzy system based on the two types of natural motions which are walking trajectories on a t1at floor and on an ascent. Learning based neuro-fuzzy system employed has good learning capability and computational performance. The results from neuro-fuzzy system are compared with previous approach.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.5
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• pp.96-103
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• 2009

This paper describes a humanoid robot's intelligent foot with two six-axis force/moment sensors. The developed humanoid robots didn't get the intelligent feet for walking on uneven surface safely. In order to walk on uneven surface safely, the robot should measure the reaction forces and moments applied on the sales of the feet, and they should be controlled with the measured the forces and moments. In this paper, an intelligent foot for a humanoid robot was developed. First, the body of foot was designed to be rotated the toe and the heel to all directions, second, the six-axis force/moment sensors were manufactured, third, the high-speed controller was manufactured using DSP(digital signal processor), fourth, the humanoid robot's intelligent foot was manufactured using the body of foot, two six-axis force/moment sensors and the high-speed controller, finally, the characteristic test of the intelligent foot was carried out. It is thought that the foot could be used for a humanoid robot.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.725-735
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• 2006

This paper deals with the problem of self-learning cooperative motion control for the pushing task of a humanoid robot in the sagittal plane. A model with 27 linked rigid bodies is developed to simulate the system dynamics. A simple genetic algorithm(SGA) is used to find the cooperative motion, which is to minimize the total energy consumption for the entire humanoid robot body. And the multi-layer neural network based on backpropagation(BP) is also constructed and applied to generalize parameters, which are obtained from the optimization procedure by SGA, in order to control the system.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.4
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• pp.315-319
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• 2006

Support vector machines in biped humanoid robot are presented in this paper. The trajectory of the ZMP in biped walking robot poses an important criterion for the balance of the walking robots but complex dynamics involved make robot control difficult. We are establishing empirical relationships based on the dynamic stability of motion using SVMs. SVMs and kernel method have become very popular method for learning from examples. We applied SVM to model the practical humanoid robot. Three kinds of kernels are employed also and each result has been compared. As a result, SVM based on kernel method have been found to work well. Especially SVM with RBF kernel function provides the best results. The simulation results show that the generated ZMP from the SVM can be improve the stability of the biped walking robot and it can be effectively used to model and control practical biped walking robot.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.2
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• pp.374-382
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• 2007

Robot mobility and intelligence become more important for robots to be used in various fields other than automation. The main purpose of providing mobility to a robot is to extend the robot's manipulability. In this paper, we introduce a small humanoid robot that can autonomously play golf as an example of incorporating robot intelligence, mobility, and manipulability. The robot has 12 degrees of freedom for legs and has various basic walking patterns. It can move to a desired position and change orientation by combining the basic waking patterns. The robot has a color CCD camera and can extract coordinates of the objects in the environments. The small humanoid robot has 8 degrees of freedom for arms and can play golf autonomously with two kinds of dexterous swing motions. Kinematic analysis of the robot arms, vision data processing for the recognition of the environments, algorithm for playing robotic golf have been performed or proposed. The experimental results show that the robot can play golf autonomously.

• Journal of Institute of Control, Robotics and Systems
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• v.15 no.10
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• pp.1029-1038
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• 2009

This paper proposes a gain switching algorithm for joint position control of a hydraulic humanoid robot. Accurate position control of the lower body is one of the basic requirements for robust balance and walking control. Joint position control is more difficult for hydraulic robots than it is for electric robots because of an absence of reduction gear and better back-drivability of hydraulic joints. Backdrivability causes external forces and torques to have a large effect on the position of the joints. External ground reaction forces therefore prevent a simple proportional-derivative (PD) controller from realizing accurate and fast joint position control. We propose a state feedback controller for joint position control of the lower body, define three modes of state feedback gains, and switch the gains according to the Zero Moment Point (ZMP) and linear interpolation. Dynamic equations of hydraulic actuators were experimentally derived and applied to a robot simulator. Finally, the performance of the algorithm is evaluated with dynamic simulations.

• The Journal of Korea Robotics Society
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• v.16 no.3
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• pp.260-269
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• 2021

To verify performance or conduct experiments using actual robots, a lot of costs are needed such as robot hardware, experimental space, and time. Therefore, a simulation environment is an essential tool in robotics research. In this paper, we develop the HUMIC simulator using ROS and Gazebo. HUMIC is a humanoid robot, which is developed by HCIR Lab., for human-robot interaction and an upper body of HUMIC is similar to humans with a head, body, waist, arms, and hands. The Gazebo is an open-source three-dimensional robot simulator that provides the ability to simulate robots accurately and efficiently along with simulated indoor and outdoor environments. We develop a GUI for users to easily simulate and manipulate the HUMIC simulator. Moreover, we open the developed HUMIC simulator and GUI for other robotics researchers to use. We test the developed HUMIC simulator for object detection and reinforcement learning-based navigation tasks successfully. As a further study, we plan to develop robot behavior intelligence based on reinforcement learning algorithms using the developed simulator, and then apply it to the real robot.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.328-332
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• 2023

The Army has presented eight game-changers for future warfare through 'Army Vision 2050,' including Intelligent Combat Robots, Super Soldiers, Energy Weapons, Hypersonic Weapons, Non-lethal Weapons, Autonomous Mobile Equipment, Intelligent Command and Control Systems, and Energy Supply Systems. This study focuses on Intelligent Combat Robots, considering them as the most crucial element among the mentioned innovations. How will Intelligent Combat Robots be utilized on the future battlefield? The future battlefield is expected to take the form of combined human-robot warfare, where advancements in science and technology allow intelligent robots to replace certain human roles. Especially, tasks known as Dirty, Difficult, Dangerous, and Dull (4D) in warfare are expected to be assigned to robots. This study suggests three forms of Intelligent Robots: humanoid robots, biomimetic robots, and swarm drones.

• The Journal of Korea Robotics Society
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• v.1 no.2
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• pp.188-196
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• 2006

During the communication and interaction with a human using motions or gestures, a humanoid robot needs not only to look like a human but also to behave like a human to make sure the meanings of the motions or gestures. Among various human-like behaviors, arm motions of the humanoid robot are essential for the communication with people through motions. In this work, a mathematical representation for characterizing human arm motions is first proposed. The human arm motions are characterized by the elbow elevation angle which is determined using the position and orientation of human hands. That representation is mathematically obtained using an approximation tool, Response Surface Method (RSM). Then a method to generate human-like arm motions in real time using the proposed representation is presented. The proposed method was evaluated to generate human-like arm motions when the humanoid robot was asked to move its arms from a point to another point including the rotation of its hand. The example motion was performed using the KIST humanoid robot, MAHRU.