• Bulletin of the Korean Chemical Society
• /
• v.26 no.2
• /
• pp.327-330
• /
• 2005

• Proceedings of the Korean Nuclear Society Conference
• /
• 2005.10a
• /
• pp.789-790
• /
• 2005

• International Journal of Control, Automation, and Systems
• /
• v.4 no.2
• /
• pp.217-226
• /
• 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.

• 제어로봇시스템학회:학술대회논문집
• /
• 2004.08a
• /
• pp.1530-1534
• /
• 2004

This paper presents a novel bio-mimetic quadruped walking robot with a waist joint, which connects the front and the rear parts of the body. The new robot, called ELIRO-1(Eating LIzard RObot version 1), can bend its body while the legs is transferred, thereby increasing the stride and speed of the robot. The waist-jointed walking robot can move easily from side to side, which is an important feature to guarantee a larger gait stability margin than that of a conventional single rigid-body walking robot. We design the mechanical structure of the robot, which is small and light to have high movability and high degree of human friendship. In this paper, we describe characteristics of the waist joint and leg mechanism as well as the analysis using ADAMS to select appropriate actuators. In addition, a hardware and software of the controller of ELIRO-1 are described.

• 제어로봇시스템학회:학술대회논문집
• /
• 1995.10a
• /
• pp.460-463
• /
• 1995

The present paper studies a robot manipulator's contact tasks on the uncertain flexible objects. The flexible object's distributed parameter model is approximated into a lumped "position state-varying" model. By using the well-known nonlinear feedback compensation, the robot's control space is decomposed into the position control subspace and the object's torque control subspace. The optimal state feedback is designed for the position loop, and the robot's contact force is controlled through controlling the resultant torque on the object using model-reference simple adaptive control. Experiments of a PUMA robot interacting with an aluminum plate show the effectiveness of this control approach. approach.

• Applied Chemistry for Engineering
• /
• v.31 no.4
• /
• pp.352-359
• /
• 2020

Inorganic nanoparticles have been actively applied to the bio-medical field by utilizing their physical properties derived from the nanometer size regime, such as optical and magnetic properties. In recent years, diagnostic detection methods have been developed by employing chemical activity, particularly enzyme-mimetic activities, as well as physical properties of inorganic nanoparticles. After the initial study of verifying the enzyme-mimetic activities, the scope of research has been expanded to the direct use of therapeutic effects with active control of activity through understanding of the catalytic mechanism. This review summarizes recent research works on the active control of the enzyme-mimetic activities and newly demonstrated applications on the diagnosis and treatment of diseases, focusing on inorganic nanoparticles, so-called "nanozyme". It is expected that the enzyme-mimetic activity of inorganic nanoparticles will be combined with their inherent physical properties, leading to the development of new diagnostic and therapeutic methods.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2011.06a
• /
• pp.399-400
• /
• 2011

• Journal of Biomedical Engineering Research
• /
• v.26 no.5
• /
• pp.257-264
• /
• 2005

This paper proposes an ionic polymer metal composite (IPMC) based artificial muscle to be applicable to the Myoelectric hand prosthesis. The IPMC consists of a thin polymer membrane with metal electrodes plated chemically on both faces, and it is widely applying to the artificial muscle because it is driven by relatively low input voltage. The control commands for the IPMC-based artificial muscle is given by electromyographic (EMG) signals obtained from human forearm. By an intended contraction of the human flexor carpi ulnaris and extensor carpi ulnaris muscles, we investigated the actuation behavior of the IPMC-based artificial muscle. To obtain higher actuation force of the IPMC, the single layered as thick as $800[{\mu}m]$ or multi-layered IPMC of which each layer can be as thick as $178[{\mu}m]$ are prepared. As a result, the bending force was up to the maximum 12[gf] from 1[gf] by actuating the single layered IPMC with $178[{\mu}m]$, but the bending displacement was reduced to 6[mm] from 30[mm]. The experimental results using an implemented IPMC control system show a possibility and a usability of the bio-mimetic artificial muscle.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.17 no.7
• /
• pp.862-868
• /
• 2007

The planar motion of the index finger in general human hands is usually implemented by the actuation of three joints. This task requires a technique to determine the joint combination for each fingertip position which is well-known as the inverse kinematics problem in robotics. Especially, it is an essential work for grasping and manipulation tasks by robotic and humanoid fingers. In this paper, an intelligent neural learning scheme for solving such inverse kinematics is presented. Specifically, a multi-layered neural network is utilized for effective inverse kinematics, where a dynamic neural learning algorithm is employed for fast learning. 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.

• JSTS:Journal of Semiconductor Technology and Science
• /
• v.14 no.4
• /
• pp.383-390
• /
• 2014

This paper presents an excitatory CMOS neuron oscillator circuit design, which can synchronize two neuron-bursting patterns. The excitatory CMOS neuron oscillator is composed of CMOS neurons and CMOS excitatory synapses. And the neurons and synapses are connected into a close loop. The CMOS neuron is based on the Hindmarsh-Rose (HR) neuron model and excitatory synapse is based on the chemical synapse model. In order to fabricate using a 0.18 um CMOS standard process technology with 1.8V compatible transistors, both time and amplitude scaling of HR neuron model is adopted. This full-chip integration minimizes the power consumption and circuit size, which is ideal for motion control unit of the proposed bio-mimetic micro-robot. The experimental results demonstrate that the proposed excitatory CMOS neuron oscillator performs the expected waveforms with scaled time and amplitude. The active silicon area of the fabricated chip is $1.1mm^2$ including I/O pads.