In this study, an anthropomorphic robot Hand, called “SKKU Hand III” is presented. The hand has thirteen DOF(Degree-Of-Freedom) and is designed based on the skeletal structure of the human hand. Each finger module(except thumb module) has three DOF and four joints with a saddle joint mechanism which has two DOF at the base joint. Two distal joints of the finger module are mechanically coupled by a timing belt and pulleys. The thumb module is composed of a finger module and an additional actuator, which makes it possible to realize the opposition between the thumb and the other fingers. In addition, the palm DOF of the human hand is mimicked with a spatial link mechanism between the index finger and the thumb. Thus, it can grasp objects more stably and more strongly. For the modularization of the robotic hand all the driving circuits are embedded in the hand, and only the communication lines supporting CAN protocol with DC power cable are given as an interface. Therefore, it is possible to apply it to any robot system the interface. To validate the feasibility of the SKKU Hand III, a series of the representative grasp experiments such as power, precision, intermediate grasp etc. are carried out with the object around us and its operation is demonstrated.

In this paper, we propose remote navigation control for intelligent robot using particle swarm optimization(PSO). The proposed system consists of interfaces for intelligent robot navigation and user interface in order to control the intelligent robot remotely. And communication interfaces using TCP/IP socket is used. To do this, we first design the fuzzy navigation controller based on expert's knowledge for intelligent robot navigation. At this time, we use the PSO algorithm in order to identify the membership functions of fuzzy control rules. And then, we propose the remote system in order to navigate the robot remotely. Finally, we show the effectiveness and feasibility of the developed controller and remote system through some experiments.

It is difficult to find a practical solution for the backward-motion control of a car-like mobile robot with n passive trailers. Unlike an omni-directional robot, a car-like mobile robot has nonholonomic constraints and limitations of the steering angle. For these reasons, the backward motion control problem of a car-like mobile robot with n passive trailers is not trivial. In spite of difficulties, backing up a trailer system is useful for parking control. In this study, we proposed a mechanical alteration which is connecting n passive trailers to the front bumper of a car to improve the backward motion control performance. Theoretical verification and simulations show that the backward-motion control of a general car with n passive trailers can be successfully carried out by using the proposed approach.

병렬기구를 이용하여 항만공사를 위한 수중로봇을 개발하였다. 수중으로 큰 피복석을 옮기기 위해 수중로봇은 크레인에 의해 권양된다. 수중로봇의 요오와 피치운동은 유압 실린더에 의해 제어되지만 롤 운동은 제어되지 않는다. 롤 운동을 위해 로봇 양쪽에 프로펠러가 장착되어 제어된다. 본 논문은 수중로봇의 롤 운동제어에 관한 것이다. 롤 운동 각도를 측정하기 위해 자이로 센서가 사용되었다. 로봇의 롤 운동을 2차 비선형 시스템으로 나타내고 반복 리스트 스퀘어 방법과 적응인식 방법으로 동적 모델을 찾았다. 동적 모델로 외란을 보상하기 위한 제어입력을 계산하고 PD 제어, 반복 리스트 스퀘어 모델 베이스 제어, 적응 모델 베이스 제어를 롤 운동제어에 적용했다. 수중로봇의 시스템을 설명하고 제안한 제어기의 시뮬레이션과 실험결과를 보인다.

The field of robots is being widely accepted as a new technology today. Many robots are produced continuously to impart amusement to people. Especially the robot which operates with a wheelbarrow was enough of a work of art to arouse excitement in the audiences. All the wheelbarrow robots share the same technology in that the direction of roll and pitch are acting as balance controllers, allowing the robots to maintain balance for a long period by continuously moving forward and backward. However one disadvantage of this technology is that they cannot avoid obstacles in their way. Therefore movement in sideways is a necessity. For the control of rotation of yawing direction, the angle and direction of rotation are adjusted according to the velocity and torque of rotation of a motor. Therefore this study aimed to inquire into controlling yawing direction, which is responsible for rotation of a robot. This was followed by creating a simulation of a wheelbarrow robot and equipping the robot with a yawing direction controlling device in the center of the body so as to allow sideway movements.

Recently, automatic parking assist systems are commercially available in some cars. In order to improve the reliability and the accuracy of parking control, pose uncertainty of a vehicle and some experimental issues should be solved. In this paper, following three schemes are proposed. (1) Odometry calibration scheme for the Car-Like Mobile Robot.(CLMR) (2) Accurate localization using Extended Kalman Filter(EKF) based redundant odometry fusion. (3) Trajectory tracking controller to compensate the tracking error of the CLMR. The proposed schemes are experimentally verified using a miniature Car-Like Mobile Robot. This paper shows that odometry accuracy and trajectory tracking performance can be dramatically improved by using the proposed schemes.

This paper proposes the trot gait pattern generation and online control methods for a quadruped robot to carry heavy loads and to move fast on uneven terrain. The trot pattern is generated from the frequency modulated pattern generation method based on the frequency modulated oscillator in order for the legged robots to be operated outdoor environment with the static and dynamic mobility. The efficiency and performance of the proposed method are verified through computer simulations and experiments using qRT-1/-2. In the experiments, qRT-2 which has two front legs driven by hydraulic linear actuators and two rear casters is used. The robot can trot at the speed up to 1.3 m/s on even surface, walk up and down the 20 degree inclines, and walk at 0.7 m/s on uneven surface. Also it can carry over 100 kg totally including 40 kg payload.

Robot system development consists of several sub-tasks such as layout design, motion planing, and sensor programming etc. In general, on-line programming and debugging for such tasks demands burdensome time and labor costs, which motivates an off-line graphic simulation system. MSRS(Microsoft Robotics Studio) released in recent years is an appropriate tool for the graphic simulation system since it supports CCR(Concurrency and Coordination Runtime), DSS(Decentralized System Services), and dynamics simulation based on PhysX and graphic animation as well. In this paper, we developed an MSRS based network simulation system for quadruped walking robots, which controls virtual 3D graphic robots existing in remote side through internet.

In this paper, applications of neural networks to vibration control of flexible single link robot manipulator are ocnsidered. The architecture of neural networks is a hidden layer, which is comprised of self-recurrent one. Tow neural networks are utilized in a control system ; one as an identifier is called neuro identifier and the othe ra s a controller is called neuro controller. The neural networks can be used to approximate any continuous function to any desired degree of accuracy and the weights are updated by dynamic error-backpropagation algorithm(DEA). To guarantee concegence and to get faster learning, an approach that uses adaptive learning rates is developed by introducing a Lyapunov function. When a flexible manipulator is ratated by a motor through the fixed end, transverse vibration may occur. The motor torque should be controlle dinsuch as way, that the motor is rotated by a specified angle. while simulataneously stabilizing vibration of the flexible manipulators so that it is arrested as soon as possible at the end of rotation. Accurate vibration control of lightweight manipulator during the large body motions, as well as the flexural vibrations. Therefore, dynamic models for a flexible single link manipulator is derived, and LQR controller and nerual networks controller are composed. The effectiveness of the proposed nerual networks control system is confirmed by experiments.

In this paper, applications of multilayer neural networks to control of flexible robot beam are considered. The multilayer nerual networks can be used to approximate any continuous function to a desired degree of accuracy and the weights are updated by Gradient Method. When a flexible beam is rotated by a motor through the fixed end, transverse vibration may occur. The motor torque should be controlled insuch a way that the motor rotates by a specified angle, while simultaneously stabilizing vibration of the flexible manipulators so that is arrested as soon as possbile at the end of rotation. Accurate control of lightweight beam during the large changes in configuration common to robotic tasks requires dynamic models that describe both rigid body motions, as well as the flexural vibrations. Therefore, a linear dynamic state-space model of for a single link flexible robot beam is derived and PD controller, LQP controller, and inverse dynamical neural networks controller are composed. The effectiveness the proposed control system is confirmed by computer simulation.