This paper describes the development of a chest-wearable robot that can efficiently perform self walking rehabilitation without a helper. The features of the developed robot are divided into three parts. First, as a mechanical characteristic, the conventional elbow crutch is attached at the forearm. However, the proposed robot is attached to the patient's chest, enabling them to feel free to use their hands and eliminate the burden of the arms. Second, as a characteristic of the driving algorithm, pressure sensors attached to the chest automatically perceive the patient's walking intention and move the robot-leg thereafter. Also, for safety, it stops operating when an obstacle is found in front of the patient by using ultrasonic sensors and generates a beeping sound. Finally, by using the scotch yoke mechanism, supporting legs are moved up and down using a rotary servo motor without excessive torque that is generated by large ground reaction forces. We showed that the developed robot can effectively perform self walking rehabilitation through walking experiments, and its performance was verified using Electromyograph (EMG) sensors.

In this study, the gait imbalance evaluation algorithm based on two axes angle using encoder is proposed. This experiment was carried out to experiment with a healthy adult male to 10 people. The device is attached to the hip and knee joint in order to measure the angle during the gait. Normal and imbalance gait angle data were measured using an encoder attached to the hip and knee joints. Also, in order to verify the reliability of estimation of asymmetrical gait using hip and knee angle, it was compared with the result of asymmetrical gait estimation using foot pressure. SI (Symmetry Index) was used as an index for determining the gait imbalance. As a result, normal gait and 1.5cm imbalance gait were evaluation as normal gait through SI using an encoder. And imbalance gait of 3cm, 4cm, and 6cm were judge by imbalance gait. Whereas all gait experiments except normal gait were evaluation as imbalance gait through SI using the pressure. It was possible to determine both the normal gait and imbalance gait through measurement for the angle and the pressure.

This paper presents the design of a lab-built powered knee prosthesis based on a BLDC motor, a sensored impedance control using a force sensor, and a sensorless impedance control through torque estimation. Firstly, we describe the structure of the lab-built powered knee prosthesis and its limitations. Secondly, we decompose the gait cycle into five stages and apply the position-based impedance control for the powered knee prosthesis. Thirdly, we perform an experiment for the torque estimation and the sensorless impedance control of the prosthesis. The experimental results show that we can use the torque estimation to control the low impedance during the swing phase, although the estimated torque data has a delay compared with the measured torque by a load cell.

This paper describes the design of an elbow rehabilitation robot based on force measurement that enables a severe stroke patient confined to their bed to receive elbow rehabilitation exercises. The developed elbow rehabilitation robot was providewitha two-axis force/torque sensor which can detect force Fz and torque Tz, thereby allowing it to measure therotational force (Tz) exerted on the elbow and the signal force Fz which can be used as a safety device. The robot was designed and manufactured for severe stroke patients confined to bed, and the robot program was manufactured to perform flexibility elbow rehabilitation exercises. Asa result of the characteristics test of the developed rehabilitation robot, the device was safely operated while the elbow rehabilitation exercises were performed. Therefore, it is thought that the developed rehabilitation robot can be used for severe stroke patients.

Need to develop human body's posture supervised robots, gave the push to researchers to think over dexterous design of exoskeleton robots. It requires to develop quantitative techniques to assess human motor function and generate the command to assist in compliance with complex human motion. Upper limb rehabilitation robots, are one of those robots. These robots are used for the rehabilitation of patients having movement disorder due to spinal or brain injuries. One aspect that must be fulfilled by these robots, is to cope with uncertainties due to different patients, without significantly degrading the performance. In this paper, we propose chattering free sliding mode control technique for this purpose. This control technique is not only able to handle matched uncertainties due to different patients but also for unmatched as well. Using this technique, patients feel active assistance as they deviate from the desired trajectory. Proposed methodology is implemented on seven degrees of freedom (DOF) upper limb rehabilitation robot. In this robot, shoulder and elbow joints are powered by electric motors while rest of the joints are kept passive. Due to these active joints, robot is able to move in sagittal plane only while abduction and adduction motion in shoulder joint is kept passive. Exoskeleton performance is evaluated experimentally by a neurologically intact subjects while varying the mass properties. Results show effectiveness of proposed control methodology for the given scenario even having 20 % uncertain parameters in system modeling.

Hot forging industry workers are prone to encounter several health risks due to lack of automation and poor working environment conditions. These workers particularly suffer from muscle fatigue owing to the constant handling of heavy products during the forging process. Thus we developed an assistant robot for workers who carry out hot forging tasks. The purpose of a robot is to compensate gravity-loads for heavy products. To verify the functionality of a robot, we performed a muscle fatigue analysis using Electromyography (EMG) signals. Four muscles of the upper extremity were chosen to measure muscle activity. And experiment conditions were setup to imitate the hot forging process. Post experimental analysis of the captured muscle activity revealed a reduction in the median frequency of the EMG signals, which means clear fatigue reduction due to a robot's assistance. The developed assist robot with compact and economical components can be efficiently utilized at forging work sites to create better working conditions for operators.

In this paper, we propose an inverse optimal distributed protocol for the formation and velocity consensus of nonholonomic mobile robots. The communication among mobile robots is described by a simple undirected graph, and the mobile robots' kinematics are considered. The group of mobile robots driven by the proposed protocols asymptotically achieves the desired formation and group velocity in an inverse optimal fashion. The design of the protocols is based on dynamic feedback linearization and the proposed linear quadratic (LQ) inverse optimal second-order consensus protocol. A numerical simulation is given to verify the effectiveness of the proposed scheme.

As electricity consumption is increasing rapidly these days, an urgent. need exists to minimize consumption through smart and intelligent ways in order to prevent a future energy crisis. For this purpose, development of an intelligent peak power management system should be required. As the number of appliances and consumer electrical devices increase, power consumption in unit business tends to grow. Generally, electricity consumption can be minimized using a peak power management system capable of. effectively controlling the load power by continuously monitoring the power. In this work, a peak power management system which consists of arduino microprocessor equipped with ethernet and Zigbee shield is presented. To verify the feasibility of the proposed scheme, laboratory-scale experiments are carried out.

A hybrid power system was developed for agricultural machines with a 20kW output capacity, and it was attached to a multi-purpose cultivator to improve the performance of the cultivator. The hybrid system combined heterogeneous sources: an internal-combustion engine and an electric power motor. In addition, a power splitter was developed to simplify the power transmission structure. The cultivator using a hybrid system was designed to have increased fuel efficiency and output power and reduced exhaust gas emissions, while maintaining the functions of existing cultivators. The fuel consumption for driving the cultivator in the hybrid engine vehicle (HEV) mode was 341g/kWh, which was 36% less than the consumption in the engine (ENG) mode for the same load. The maximum power take off output of the hybrid power system was 12.7kW, which was 38% more than the output of the internal-combustion engine. In the HEV mode, harmful exhaust gas emissions were reduced; i.e., CO emissions were reduced by 36~41% and NOx emissions were reduced by 27~51% compared to the corresponding emissions in the ENG mode. The hybrid power system improved the fuel efficiency and reduced exhaust gas emissions in agricultural machinery. The hybrid system's lower exhaust gas emissions have considerable advantages in closed work environments such as crop production facilities. Therefore, agricultural machinery with less exhaust gas emissions should be commercialized.

We presented a quad-rotor controlling algorithm design by using sensor fusion in this paper. The controller design technique was performed by a PD controller with a Kalman filter and compensation algorithm for increasing the stability and reliability of the quad-rotor attitude. In this paper, we propose an attitude estimation algorithm for quad-rotor based sensor fusion by using the Kalman filter. For this reason, firstly, we studied the platform configuration and principle of the quad-rotor. Secondly, the bias errors of a gyro sensor, acceleration and geomagnetic sensor are compensated. The measured values of each sensor are then fused via a Kalman filter. Finally, the performance of the proposed algorithm is evaluated through experimental data of attitude estimation. As a result, the proposed sensor fusion algorithm showed superior attitude estimation performance, and also proved that robust attitude estimation is possible even in disturbance.

An on-line quality control of hot steel products is one of the important issues in the steel industry because of cost minimization. In recent years, relative depth information of surface defects is increasingly required for strict quality control. In this paper, a 3D shape reconstruction scheme for defects on a hot steel surface based on a multi-spectral photometric stereo method is proposed. After simultaneously illuminating a hot steel surface by using vertical/horizontal linearly polarized lights of green and blue light sources, the corresponding 4 images are obtained. The photometric stereo method is then applied with the aid of a GPU (Graphic Processing Unit) to reconstruct the shape of the target surface based on these images. The proposed scheme was validated through experiments.

This paper proposes a robust 3D mapping system for a UAV (Unmanned Aerial Vehicle) that carries a LRF (Laser Range Finder) using the sinusoidal trajectory algorithm. In the case of previous 3D mapping research, the UAV usually takes off vertically and flights up and down while the LRF is measuring horizontally. In such cases, the measuring range is limited and it takes a long time to do mapping. By using the sinusoidal trajectory algorithm proposed in this research, the 3D mapping can be time-efficient and the measuring range can be widened. The 3D mapping experiments have been done to evaluate the performance of the sinusoidal trajectory algorithm by scanning indoor walls.

This paper presents a maximum velocity trajectory planning algorithm for differential mobile robots with wheel velocity constraint to cope with physical limits in the joint space for two-wheeled mobile robots (TMR). In previous research, the convolution operator was able to generate a central velocity that deals with the physical constraints of a mobile robot while considering the heading angles along a smooth curve in terms of time-dependent parameter. However, the velocity could not track the predefined path. An algorithm is proposed to compensate an error that occurs between the actual and driven distance by the velocity of the center of a TMR within a sampling time. The velocity commands in Cartesian space are also converted to actuator commands to drive two wheels. In the case that the actuator commands exceed the maximum velocity the trajectory is redeveloped with the compensated center velocity. The new center velocity is obtained according to the curvature of the path to provide a maximum allowable velocity meaning a time-optimal trajectory. The effectiveness of the algorithm is shown through numerical examples.

Implementation of memristor-based multilayer neural networks and their hardware-based learning architecture is investigated in this paper. Two major functions of neural networks which should be embedded in synapses are programmable memory and analog multiplication. "Memristor", which is a newly developed device, has two such major functions in it. In this paper, multilayer neural networks are implemented with memristors. A Random Weight Change algorithm is adopted and implemented in circuits for its learning. Its hardware-based learning on neural networks is two orders faster than its software counterpart.

CSTR (Continuous Stirred Tank Reactor) which plays a key role in the chemical plants exhibits highly nonlinear behavior as well as time-varying behavior during operation. The control of CSTRs in the whole operating range has been a challenging problem to control engineers. So, a variety of feedback control forms and their tuning methods have been implemented to guarantee the satisfactory performance. This paper presents a scheme of designing a nonlinear PID controller incorporating with a GA (Genetic Algorithm) for the temperature control of a CSTR. The gains of the NPID controller are composed of easily implementable nonlinear functions based on the error and/or the error rate and its parameters are tuned using a GA by minimizing the ITAE (Integral of Absolute Error). Simulation works for reference tracking and disturbance rejecting performances and robustness to parameter changes show the feasibility of the proposed method.

As flying robots are becoming popular, there are increased needs to use themforsuch purposes as parcel delivery, serving in restaurants, and stage performances. To control flying robots such as quad copters, localization is essential. In order to properly position flying robots, many techniques are in development, including IR (infra-red)-based systemswhich catch markers on a flying robot in order that it can position itself. However, this technique demonstrates only short coverage. Furthermore, localization from inertial sensors diverges as time passes. For this reason, this paper suggests a TWR (two-way ranging) based positioning technique. Despite the weaknesses in currently available TWR system, this paper suggests a self-positioning and outlier detection technique in order to provide reliable position information with a faster update rate. The self-positioning system sends a shorter message which reduces wireless traffic. By detecting and removing outlier measurements, a positioning result with better accuracy is acquired. Finally, this paper shows that the suggesting system detects outlierssequentially from less than half the number of anchors in localization system according to the degree of outlier in measurement and the noise level. By performing an outlier algorithm, better positioning accuracy is acquired as shown in the experimental result.