A humanoid robot hand with one thumb and two fingers has been developed. Each finger has the specially designed compact joints, called "MEC Joint", which convert the rotation of a motor to the swing motion of a pendulum. The robot hand with the MEC Joints is compact and relatively light but strong enough to grasp objects in the same manner as human being does in daily activities. In this paper the kinematic model and the torque characteristics of the MEC Joint are presented and compared with the results of the dynamic simulation and the dynamometer test. The dynamic behavior of the thumb and two fingers with MEC Joints are also presented by computer simulation.

This paper proposes a performance index for the multiple shape object handling of dual arm manipulator to determine whether a robot is good or not. When the dual-arm manipulator grasps a fixed object and is posed, the dual-arm manipulator should procure a space to freely control the manipulator. As a performance evaluation parameter, each joint torque from current sensor signal is utilized. From the current information, torque and energy for each joint are estimated. In this paper an performance index for an unstructured object is defined by an energy-cost function, and stability analysis for each motion is derived by the maximum force to the object. The maximum force to the object is computed by the inertia of object and acceleration information of end-effector. The acceleration data are derived by the double derivation of each encoder signal. Manipulability measure which implies how efficiently the dual-arm manipulator can move with the grasped object, can be represented by the intersection of the two manipulability ellipsoids for the left and right arms. Effectiveness of the proposed algorithm has been verified through the practical simulations and real experiments.

In spite of the difficulties and uncertain characteristic of cable driven method, surgical robot instrument has adopted it as driving mechanism for various reasons. To overcome the problem of cable system, previous research applied SMCSPO (sliding mode control with sliding perturbation observer) algorithm as robust controller to control the instrument and found that the value of SPO (sliding perturbation observer) followed force disturbance, reaction force loaded on the tip very similarly. Thus, this paper confirms that the perturbation observer is sufficient estimator which finds out the mount of loaded force on the surgical robot instrument. To prove the proposition, simulation using the similar model with an actual instrument and experimental evaluation are performed. The results show that it is possible to substitute SPO for sensors to measure the reaction force. This estimated reaction force will be used to realize haptic function by sending the reaction force to a master device for a surgeon. The results will contribute to create surgical benefit such as shortening the practice time of a surgeon and giving haptic information to surgeon by using it as haptic signal to protect an organ by making force boundary.

This paper suggests a new observation model for Extended Kalman Filter based Simultaneous Localization and Mapping (EKF-SLAM). Since the EKF framework linearizes non-linear functions around the current estimate, the conventional line model has large linearization errors when a mobile robot locates faraway from its initial position. On the other hand, the model that we propose yields less linearization error with respect to the landmark position and thus suitable in a large-scale environment. To achieve it, we build up a three-dimensional space by adding a virtual axis to the robot's two-dimensional coordinate system and extract a plane by using a detected line on the two-dimensional space and the virtual axis. Since Jacobian matrix with respect to the landmark position has small value, we can estimate the position of landmarks better than the conventional line model. The simulation results verify that the new model yields less linearization errors than the conventional line model.

This paper presents a method to optimize motion planning for industrial manipulators with redundancy. For optimal motion planning, first of all, particular inverse kinematic solution is needed to improve efficiency for manipulators with redundancy working in various environments. In this paper, we propose three kinds of methods for solving inverse kinematics problems; numerical and combined approach. Also, we introduce methods for optimal motion planning using potential function considering the order of priority. For efficient movement in industrial settings, this paper presents methods to plan motions by considering colliding obstacles, joint limits, and interference between whole arms. To confirm improved performance of robot applying the proposed algorithms, we use two kinds of robots with redundancy. One is a single arm robot with 7DOF and another is a dual arm robot with 15DOF which consists of left arm, right arm with each 7DOF, and a torso part with 1DOF. The proposed algorithms are verified through several numerical examples as well as by real implementation in robot controllers.

This paper proposes how to improve the performance of CSS-based indoor localization system. CSS based localization utilizes signal flight time between anchors and tag to estimate distance. From the distances, the 3-dimensional position is calculated through trilateration. However the error in distance caused from multi-path effect transfers to the position error especially in indoor environment. This paper handles a problem of reducing error in raw distance information. And, we propose the new localization method by pattern matching instead of the conventional localization method based on trilateration that is affected heavily on multi-path error. The pattern matching method estimates the position by using the fact that the measured data of near positions possesses a high similarity. In order to gain better performance of localization, we use EKF(Extended Kalman Filter) to fuse the result of CSS based localization and robot model.