A low-dispersion fiber feed Littrow-mounted grating spectrometer for education was designed and fabricated. The dispersion element is a reflective type blazed grating Edmundoptics NT 46-075 (spatial frequency 600 lines/mm, dimension 30mm×30mm, blazed angle 8.6 degree). The optical fiber coupler module for optical guiding from telescope to spectrometer is composed of a multi-mode FC connector - FC connector optical fiber patch cord (core/cladding diameter 50μm/125μm) and two 1.25" throw-tube couplers. The lens for collimating and imaging is a general purpose focal length 50 mm camera lens (f/1.8). The device for optical path control is a rectangular prism (size 25mm×25mm). The imaging camera sensor is a Meade DSI Pro 2 CCD sensor (black and white, 752×582 pixels and pixel size 8.3μm×8.6μm). Softwares for data logging and analysis consist of Meade Autostar Suite, NIH imagej and Vernier Logger Pro 3. The wavelength coverage range of the spectrometer is 205 nm at central wavelength 550 nm. The wavelength resolution is 1.7 nm.

In the midst of disaster, such as an earthquake or a nuclear radiation exposure area, there are huge risks to send human crews. Many robotic researchers have studied to send UGVs in order to replace human crews at dangerous environments. So far, two-dimensional camera information has been widely used for teleoperation of UGVs. Recently, three-dimensional information based teleoperations are attempted to compensate the limitations of camera information based teleoperation. In this paper, the 3D map information of indoor and outdoor environments reconstructed in real-time is utilized in the UGV teleoperation. Further, we apply the LTE communication technology to endure the stability of the teleoperation even under the deteriorate environment. The proposed teleoperation system is performed at explosive disposal missions and their feasibilities could be verified through completion of that missions using the UGV with the Explosive Ordnance Disposal (EOD) team of Busan Port Security Corporation.

This paper proposes a method to simultaneously estimate two degrees of freedom in wrist forces (extension - flexion, adduction - abduction) and one degree of freedom in grasping forces using Electromyography (EMG) signals of the forearms. To correlate the EMG signals with the forces, we applied a multi - layer perceptron(MLP), which is a machine learning method, and used the characteristics of the muscles constituting the forearm to generate learning data. Through the experiments, the similarity between the MLP target value and the estimated value was investigated by applying the coefficient of determination (R2) and root mean square error (RMSE) to evaluate the performance of the proposed method. As a result, the R2values with respect to the wrist flexionextension, adduction - abduction and grasping forces were 0.79, 0.73 and 0.78 and RMSE were 0.12, 0.17, 0.13 respectively.

Peg-in-hole assembly is the most representative task for a robot to perform under contact conditions. Various strategies for accomplishing the peg-in-hole task with a robot exist, but the existing strategies are not sufficiently practical to be used for various assembly tasks in a human environment because they require additional sensors or exclusive tools. In this paper, the peg-in-hole assembly experiment is performed with anthropomorphic hand arm robot without extra sensors or devices using “intuitive peg-in-hole strategy”. From this work, the probability of applying the peg-in-hole strategy to a common assembly task is verified.

Global positioning system (GPS) is widely used to measure the position of a vehicle. However, the accuracy of the GPS can be severely affected by surrounding environmental conditions. To deal with this problem, the GPS and odometry data can be combined using an extended Kalman filter. For stable navigation of an outdoor mobile robot using the GPS, this paper proposes two methods to evaluate the reliability of the GPS data. The first method is to calculate the standard deviation of the GPS data and reflect it to deal with the uncertainty of the GPS data. The second method is to match the GPS data to the traversability map which can be obtained by classifying outdoor terrain data. By matching of the GPS data with the traversability map, we can determine whether to use the GPS data or not. The experimental results show that the proposed methods can enhance the performance of the GPS‐based outdoor localization.

This paper focuses on a development of an anthropomorphic robot hand. Human hand is able to dexterously grasp and manipulate various objects with not accurate and sufficient, but inaccurate and scarce information of target objects. In order to realize the ability of human hand, we develop a robot hand and introduce a control scheme for stable grasping by using only kinematic information. The developed anthropomorphic robot hand, KITECH Hand, has one thumb and three fingers. Each of them has 4 DOF and a soft hemispherical finger tip for flexible opposition and rolling on object surfaces. In addition to a thumb and finger, it has a palm module composed the non-slip pad to prevent slip phenomena between the object and palm. The introduced control scheme is a quitely simple based on the principle of virtual work, which consists of transposed Jacobian, joint angular position, and velocity obtained by joint angle measurements. During interaction between the robot hand and an object, the developed robot hand shows compliant grasping motions by the back-drivable characteristics of equipped actuator modules. To validate the feasibility of the developed robot hand and introduced control scheme, collective experiments are carried out with the developed robot hand, KITECH Hand.