• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2007년도 추계학술대회 학술발표 논문집
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• pp.159-162
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• 2007

The inverse kinematics problem in robotics is an essential work for grasping and manipulation tasks by robotic and humanoid hands. In this paper, an intelligent neural learning scheme for solving such inverse kinematics of humanoid fingers is presented. Specifically, a multi-layered neural network is utilized for effective inverse kinematics, where a dynamic neural learning algorithm is employed. Also, a bio-mimetic feature of general human fingers is incorporated to the learning scheme. The usefulness of the proposed approach is verified by simulations.

• International Journal of Control, Automation, and Systems
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• 제4권2호
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• pp.217-226
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• 2006

Grasping and manipulation by hands can be considered as one of inevitable functions to achieve the performances desired in humanoid operations. When a humanoid robot manipulates an object by his hands, each finger should be well-controlled to accomplish a precise manipulation of the object grasped. So, the trajectory of each joint required for a precise finger motion is fundamentally necessary to be planned stably. In this sense, this paper proposes an effective joint motion planning method for humanoid fingers. The proposed method newly employs a bio-mimetic concept for joint motion planning. A suitable model that describes an interphalangeal coordination in a human finger is suggested and incorporated into the proposed joint motion planning method. The feature of the proposed method is illustrated by simulation results. As a result, the proposed method is useful for a facilitative finger motion. It can be applied to improve the control performance of humanoid fingers or prosthetic fingers.

• 한국정밀공학회지
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• 제23권7호
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• pp.67-75
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• 2006

One of challenging topics for humanoid hands is to modulate a human-like motion of humanoid fingers handling an object. To this end, recognizing the motion behavior of human fingers is very important aspect. Based on this concept, this paper identifies the .joint trajectories of human fingers for an operation of hand opening and closing, and specifies an empirical model that coordinates an inter-articular relationship of human fingers doing the given motion. It is expected that the inter-articular model presented in this paper is applicable for humanoid fingers to mimic the natural motion of human fingers.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 추계학술대회 논문집
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• pp.179-180
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• 2010

• Advances in robotics research
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• 제2권3호
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• pp.183-199
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• 2018

This paper presents the development of a 3D printed humanoid robotic hands of SignBot, which can perform Malaysian Sign Language (MSL). The study is considered as the first attempt to ease the means of communication between the general community and the hearing-impaired individuals in Malaysia. The signed motions performed by the developed robot in this work can be done by two hands. The designed system, unlike previously conducted work, includes a speech recognition system that can feasibly integrate with the controlling platform of the robot. Furthermore, the design of the system takes into account the grammar of the MSL which differs from that of Malay spoken language. This reduces the redundancy and makes the design more efficient and effective. The robot hands are built with detailed finger joints. Micro servo motors, controlled by Arduino Mega, are also loaded to actuate the relevant joints of selected alphabetical and numerical signs as well as phrases for emergency contexts from MSL. A database for the selected signs is developed wherein the sequential movements of the servo motor arrays are stored. The results showed that the system performed well as the selected signs can be understood by hearing-impaired individuals.

• 로봇학회논문지
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• 제1권2호
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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.

• 제어로봇시스템학회논문지
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• 제15권9호
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• pp.952-958
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• 2009

This article presents a combined structure of serial and parallel mechanisms for a humanoid robot. The 3 DOF parallel structure is designed and added to the waist of the humanoid robot arm to give flexible bending and rotating motions. Forward and inverse kinematics of a serial and parallel robot have been analyzed to generate motions. Simulation studies of verifying kinematics solutions of the parallel robot have been done. Experimental studies of mimicking shake-hands motion have been conducted to show the feasibility and usability of the combined structure.

• 제어로봇시스템학회논문지
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• 제6권5호
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• pp.404-414
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• 2000

This paper addresses the development of a 3-fingered humanoid robot hand system and a real-time grasp synthesis of multifingered robot hands to find grasp configurations which satisfy the force closure condition of arbitrary shaped objects. We propose a fast and efficient grasp synthesis algorithm for planar polygonal objects, which yields the contact locations on a given polygonal object to obtain a force closure grasp by the multifingered robot hand. For an optimum grasp and real-time computation, we develop the preference and the hibernation process and assign physical constraints of the humanoid hand to the motion of each finger. The preferences consist of each sublayer reflecting the primitive preference similar to the conditional behaviors of humans for given objectives and their arrangements are adjusted by the heuristics inspired from human's grasping behaviors. The proposed method reduces the computational time significantly at the sacrifice of global optimality, and enables the grasp posture to be changable within two-finger and three-finger grasps. The performance of the presented algorithm is evaluated via simulation studies to obtain the force-closure grasps of polygonal objects with fingertip grasps. The architecture suggested is verified through experimental implementation to our robot hand system by solving the 2- or 3-finger grasp synthesis.

• 로봇학회논문지
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• 제16권3호
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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.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.113-118
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• 2005

Robots today have wider application fields than they ever have before. They need to work close to humans and fluid and compliable motions are expected of them. This requires redundant degrees of freedom for completing specific task. And conventional motion teaching method cannot be applied to redundant link structures. In this paper, the authors present a proficient, cost-effective and intuitive method for motion teaching. New software to apply this method to a humanoid is also presented. This new method utilizes current sensors to determine which joints to rotate. The experiment shown in this paper is a case of closed link where arms cannot move independently due to the restrictions in between the hands. After the input of several passing points of motion trajectory, the curve fitting is performed by the developed software. This software can insert new points, delete erroneous points and modify existing points. The developed motion teaching method is applied to the Kumdo robot, which is developed by the authors.