This paper deals with the development of a passive modular hip exoskeleton system aimed at preventing musculoskeletal low back pain, which commonly occurs in heavy weight transport workers, by improving back muscle strength. The passive exoskeleton system has the advantage of being lightweight, making it suitable for modular exoskeleton systems. The cam and spring actuator designed in this study was applied to the passive modular exoskeleton system to build human hip and lumbar muscle strength. In order to evaluate the effectiveness of the passive modular exoskeleton system, a test was performed in which a subject lifted a 15 kg weight three times in a stoop posture, using heart rate measurement and Borg scale recording. According to the results, all subjects showed 26.83% lower maximum heart rate and 34.73% lower average heart rate than those who did not wear the system, and Borg scale evaluation result was lower. All subjects wore this system and did not experience back pain during the experiment. Through this study, we validated the effectiveness of the passive modular exoskeleton system and proved that this system can build the strength of industrial workers and be a solution to prevent musculoskeletal lumbar disease.

For the teleoperation of dual-arm robots with various tasks, the existence of a controller with a high degree of freedom is indispensable. Especially when precise work is required, additional information such as force feedback is very helpful for the operator. In transmitting such force information, a control device of exoskeleton-type with many points of contact with the human body can be one of the solutions. This paper proposes an optimal design method for the 7 degrees of freedom (DOF) exoskeleton systems. The proposed method optimizes the kinematic parameters by using kinematic performance indices related to the dexterity of the human and exoskeleton system. The manipulability ellipsoid is a representative index that can confirm the dexterity of the robot. In this study, we derived the objective function considering the human body model and then optimized it using a genetic algorithm. Unlike other HRI (Human-Robot Interaction) systems, exoskeleton robots share the end-effector as well as the base of the robot with the wearer. Therefore, it is hypothesized that the proposed performance index will be highly suitable for exoskeleton systems.

The purpose of this study was to suggest the method for automated locomotion modes (Level Walking, Stair Ascent, Stair Descent) detection based on the Radial Basis Function Support Vector Machine (RBF-SVM) for the hip gait assist robot. The universal hip gait assist robot had a limit in detection of the walking intention of users because of the limited sensors’ quantity. Through the offline training, using MATLAB, we trained the collected gait data of users wearing the hip gait assist robot and obtained the parameter of the RBF-SVM model. In the online test, using LabVIEW, we developed the algorithm for the locomotion modes decision of individuals using the optimized parameter of the RBF-SVM. Finally, we executed the gait test for three terrains through the walking environment’s test platform. As a result, the locomotion modes decision rate for three terrains was 98.5%, 99%, and 98% respectively. And the decision delay time of algorithm was 0.03 s, 0.03 s, and 0.06 s respectively.

In the case of paralysis caused by brain diseases and accidents, proper rehabilitation and supplementary systems for daily life are essential. In this study, we designed a system that can enable daily life by supporting the hand of a patient whose function has been lost to paralysis. The hand exoskeleton robot proposed in this study can be transported for the purpose of ADL (activities of daily living). It was designed to focus on the most important assistant for the thumb and index finger. The hand exoskeleton robot proposed in this study enables grasp and pinch motor skills during the human hand operation through the specific mechanism for the segment movement of the human finger simultaneously. Finally, the finger movement trace of the wearer through the vision system is measured, respectively.

Patients with complete paralysis that only walk with the assistance of exoskeleton robots because they lost their ability to walk. However, robots do not allow the exoskeleton robot to grasp the current state before walking and change the walking pattern. A "Stability Circle Region" was proposed to determine the current state of the exoskeleton robot. The Stability Circle is an area that can determine the possibility of a fall situation before the next walk using the link parameters of the robot and the current center of gravity of the patients. This study verified the validity of "stability circle" by simulating the change in the center of mass. Simulation results can be used to determine the stability of walking depending on whether the position of the center of mass before the walking is included in the circle area.

Automation of electronic connectors has been in demand, based on automation of assembly of electronic products. In this study, we propose a new classification of electronic connector, for grasping and assembling. We analyze characteristics of electrical connectors often used at actual industrial sites, from the perspective of the robot, not a person. As a result, it is appropriate to classify the grasp, according to the shape of the electric connector. For the assembly, we suggested that classification should be based on directions are different, because of interference of the electric wire and peripheral parts. We hope that this research will become a new basis, for electrical connector assembly.

In this paper, we propose a novel propulsion method for a Biomimetic underwater robot, which is a bio-inspired approach. The proposed propulsion method mimics the pectoral fins of a real fish. Pectoral fins of real fish are able to propel and change direction. We designed the propulsion mechanism of 1 D.O.F. that has two functions (propel and change direction). We named this propulsion system ’Flipper’. The proposed propulsion method can control forward, pitch and yaw motion using the Flipper. We made an experimental underwater robot system and verified the proposed propulsion method. We measured its maximum speed and turning motion using an experimental underwater robot system. We also analyzed the thrust force from the maximum speed, using the thrust equation. Experimental results showed that our propulsion method enabled the thrust system of the biomimetic robot.

This paper reports on the development of a roller-cam clutch mechanism. This mechanism can transfer bidirectional torque with high backdrivability, as well as increase actuation energy efficiency, in electrical exoskeleton robots. The developed mechanism was installed at the robot knee joint and unclutched during the swing phase which uses less metabolic energy, thereby functioning as a passive joint. The roller-cam clutch aimed to increase actuation energy efficiency while also producing high backdrivability by generating zero impedance for users during the swing phase. To develop the mechanism, mathematical modeling of the roller-cam clutch was conducted, with the design having more than three safety factors following optimization. Titanium (Ti-6AL-4V) material was used. Finally, modeling verification was done using ANSYS software.

This paper presents a natural corners-based SLAM (Simultaneous Localization and Mapping) with a robust data association algorithm in a real unknown environment. Corners are extracted from raw laser sensor data, which are chosen as landmarks for correcting the pose of mobile robot and building the map. In the proposed data association method, the extracted corners in every step are separated into several groups with small numbers of corners. In each group, local best matching vector between new corners and stored ones is found by joint compatibility, while nearest feature for every new corner is checked by individual compatibility. All these groups with local best matching vector and nearest feature candidate of each new corner are combined by partial compatibility with linear matching time. Finally, SLAM experiment results in an indoor environment based on the extracted corners show good robustness and low computation complexity of the proposed algorithms in comparison with existing methods.

This paper is about the development of an insole sensor system that can determine the model of an exoskeleton robot for lower limb that is a multi-degree of freedom system. First, the study analyzed the kinematic model of an exoskeleton robot for the lower limb that changes according to the gait phase detection of a human. Based on the ground reaction force (GRF), which is generated when walking, to proceed with insole sensor development, the sensing type, location, and the number of sensors were selected. The center of pressure (COP) of the human foot was understood first, prior to the development of algorithm. Using the COP, an algorithm was developed that is capable of detecting the gait phase with small number of sensors. An experiment at 3 km/h speed was conducted on the developed sensor system to evaluate the developed insole sensor system and the gait phase detection algorithm.

The need for human body posture robots has led researchers to develop dexterous design of exoskeleton robots. Quantitative techniques to assess human motor function and generate commands for robots were required to be developed. In this paper, we present a passivity based adaptive control algorithm for upper limb assist exoskeleton. The proposed algorithm can adapt to different subject parameters and provide efficient response against the biomechanical variations caused by subject variations. Furthermore, we have employed the Particle Swarm Optimization technique to tune the controller gains. Efficacy of the proposed algorithm method is experimentally demonstrated using a seven degree of freedom upper limb assist exoskeleton robot. The proposed algorithm was found to estimate the desired motion and assist accordingly. This algorithm in conjunction with an upper limb assist exoskeleton robot may be very useful for elderly people to perform daily tasks.

The forces and moments exerted on humanoid robot foot are important information for controlling or monitoring the robot. Multi-axis force/moment sensor can be installed under humanoid robot foot to measure forces and moments. The sensor should have large stiffness to support the robot weight and small size not to disturb the motion of the robot. In this paper, we designed a 6-aixs force/moment sensor which has good accuracy, large measuring range, and new compact structure. In addition, the proposed sensor is evaluated using analytical method and FEM(Finite Elements Method) method. Finally, it turned out that it has good performance.

There has been much recent research interest in developing numerous kinds of human-machine interface. This field currently requires more accurate and reliable sensing systems to detect the intended human motion. Most conventional human-machine interface use electromyography (EMG) sensors to detect the intended motion. However, EMG sensors have a number of disadvantages and, as a consequence, the human-machine interface is difficult to use. This study describes a muscle volume sensor (MVS) that has been developed to measure variation in the outline of a muscle, for use as a human-machine interface. We developed an algorithm to calibrate the system, and the feasibility of using MVS for detecting muscular activity was demonstrated experimentally. We evaluated the performance of the MVS via isotonic contraction using the KIN-COM<SUP>®</SUP> equipment at torques of 5, 10, and 15 Nm.

This study uses a muscle activation sensor and elbow joint model to develop an estimation algorithm for human elbow joint torque for use in a human-robot interface. A modular-type MVS (Muscle Volume Sensor) and calibration algorithm are developed to measure the muscle activation signal, which is represented through the normalization of the calibrated signal of the MVS. A Hill-type model is applied to the muscle activation signal and the kinematic model of the muscle can be used to estimate the joint torques. Experiments were performed to evaluate the performance of the proposed algorithm by isotonic contraction motion using the KIN-COM® equipment at 5, 10, and 15Nm. The algorithm and its feasibility for use as a human-robot interface are verified by comparing the joint load condition and the torque estimated by the algorithm.

Latest, many researchers do research on wearable robot. The purpose of the researches is very diverse, it will improve efficiency in the industry, taken to replace the many workers in the military field and taken to assist bodily functions run out by aging. However, there is no clear Differentiated strategy depending on the purpose for design and control of the wearable robot. Although a common purpose is to drive the robot by the sensor signal (intent signals), the optimization about the mechanism and control studies must be done according to the user’s physical ability and purpose. In this study, the study’s first phase for the development of wearable robotic gait rehabilitation, gait characteristics were analyzed elders and hemiplegia patients using a 3D gait analysis system (VICON512). As a result, asymmetric gait characteristics of the hemiplegia patients were found compared with the normal elderly.