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There are many researches to develop robots that improve its mobility and task planning to adapt in various uneven environments. In this paper, we propose the design method and task planning of quadruped robot which can have top-bottom docking structure. The proposed quadruped robot is designed to adjust leg length using linear actuators and perform top-bottom docking and undocking using octagonal cone shaped docking module. Also, to stable walking and information gathering in the various environments, a geomagnetic sensor, PSD sensor, LRF sensor and camera. We propose an obstacle avoidance method and the topbottom docking algorithm of the two quadruped robots using linear actuator. The robot can overcome obstacles using adjusting leg length and activate the top-bottom docking function. The top-bottom docking robots of two quadruped robot can walk 4 legged walking and 6 legged walking, and use 4 arms or 2 arms the upper. We verified that the docking robots can carry objects using 4 leg of the upper robot.

In this paper, we propose a the image-based automatic flight co ntrol system for the mini drone. Automatic flight system with a camera on the ceiling and marker s on the floor and landing position is designed in an indoor environment. Images from the ceiling c amera is used not only to recognize the makers and landing position but also to track the drone mot ion. PC sever identifies the locationof the drone and sends control commands to the mini drone. Flig ht controller of the mini drone isdesigned using state-machine algorithm, PID control and way-point position control method. From the , The proposed automatic flight control system is verified through the experiments of the mini drone. We see that known makers in environment are recognized a nd the drone can follows the trajectories with the specific ㄱ, ㄷ and ㅁ shapes. Also, experim ental results show that the dronecan approach and correctly land on the target positions which are set at different height.Key Words : Mini Drone, Image-Based Control, Indoor Automatic Flight, Automatic LandingReceived: Nov. 22, 2015Revised : Dec. 10, 2015Accepted: Dec. 16, 2015

In this paper, an image-based luminance measurement system(LMS) is proposed to measure the luminance of outdoor signboards. We design the LMS that can improve disadvantages of efficiency of the point-luminance meter and portability of face-luminance meter using the image of DSLR camera and print out the luminance value by using the proposed luminance analysis algorithm in real time. Outdoor signboards have various size and shape, and are also installed on the various place. Luminance of the signboard is influenced by measurement location, angle, color, etc. Therefore, we measure the change of luminance value in accordance with measurement location for accurate luminance measurement and then consider the lumi-nance value according to the measurement distance. We obtain a numerical relation between luminance value and measure-ment location. The proposed LMS is verified through comparative experiment with point-luminance meter.Key Words : Luminance measure system, Outdoor advertisement sign, Image of DSLR camera, Measurement position, Luminance correction algorithm

In this paper, we design a hybrid wheeled and legged mobile robot with a waist joint. The proposed hybrid mobile robot is designed to have a hybrid structure with leg and wheel for the efficient movement in flat and uneven surfaces. The proposed robot have a waist joint that is used to stably transform from wheeled driving to legged walking of the robot and to overcome non-flat surface. In order to recognize various environments we use LRF sensor, PSD sensor, CCD camera. Also, a motion planning method for hybrid mobile robot with a waist joint is proposed to select wheeled driving motion and legged walking motion of the robot based the environment types. We verify the efficient mobility of the developed hybrid mobile robot through navigation experiments using the proposed motion planning method in various environments.

This paper proposes a optimum design method for the Fuzzy PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV). Since an attraction type levitation system is intrinsically unstable, it is difficult to completely satisfy the desired performance through the methods designed by conventional controllers. In the paper, the Fuzzy PID controller with fixed parameters are applied and then the optimum parameters of fuzzy PID controller are selected by genetic algorithm. For the fitness function of genetic algorithm, the performance index of PID controller is used. To verify the performance of the proposed method, we used Matlab/simulink model of Maglev and compared the proposed method with the performance of PID controller. The simulation results show that the proposed method is more effective than conventional PID controller.

In the paper, velocity controller of switching module and temperature controller for the high-voltage static var compensator are proposed. Because of the continuous increase in demand for electric power, transmission and distribution facilities of power plant are required. There is a bottleneck problem of transportation routes according to new construction and expansion of power transmission facilities. Therefore there are researches to maximize the utilization of existing facilities and to increase transmission capacity without new construction. The previous static var compensator detects voltage of input circuit of power, switches the SCR directly and generates switching noise. The proposed method increases switching velocity and decreases noise using switching control based on the voltage between both sides of SCR. Also the proposed method enhance the stability using realtime temperature control for heating of the system from increase of switching velocity. We experiment the velocity and temperature control of the proposed high-voltage static var compensator in the real environment and verify the performance of the proposed system by applying in the real field.

In order to apply a biped robot in real world, the robot requires a robust walking and a function of localization, path planning and navigation. Recently, localization and path planning using RFID of mobile robot has been studying. However, when the biped robot walks, it has unstability and tends to leave the path. In the paper we propose a method of walking stabilization using FSR(Force Sensing Resistor), Gyro and accelerometer for the real biped robot. Also a path tracking algorithm using RFID sensor attached in robot's foot is proposed based on localization of the robot. The proposed algorithm is verified from walking experiments using real biped robot on uneven terrain and path tracking experiments on the RFID environments.

There are many researches to develop robots that improve its mobility to adapt in various uneven environments. In the paper, a hybrid wheeled and legged mobile robot is designed and a obstacle avoidance algorithm is proposed based on low power walking using LRF(Laser Range Finder). In order to stabilize the robot`s motion and reduce energy consumption, we implement a low-power walking algorithm through comparison of the current value of each motors and correction of posture balance. A low-power obstacle avoidance algorithm is proposed by using LRF sensor. We improve walking stability by distributing power consumption and reduce energy consumption by selecting a shortest navigation path of the robot. The proposed methods are verified through walking and navigation experiments with the developed hybrid robot.

Tracking and recognition of robots are required for the cooperation task of robots in various environments. In the paper, a tracking control system of moving robot using stereo image processing, code-book model and fuzzy controller is proposed. First, foreground and background images are separated by using code-book model method. A candidate region is selected based on the color information in the separated foreground image and real distance of the robot is estimated from matching process of depth image that is acquired through stereo image processing. The open and close processing of image are applied and labeling according to the size of mobile robot is used to recognize the moving robot effectively. A fuzzy tracking controller using distance information and mobile information by stereo image processing is designed for effective tracking according to the movement velocity of the target robot. The proposed fuzzy tracking control method is verified through tracking experiments of mobile robots with stereo camera.

This paper proposes a novel optimum design method for the PID controller of magnetic levitation-based Rail-Guided Vehicle(RGV) by a genetic algorithm using clone selection method and a new performance index function with performances of both time and frequency domain. Generally, since an attraction type levitation system is intrinsically unstable and requires a delicate controller that is designed considering overshoot and settling time, it is difficult to completely satisfy the desired performance through the methods designed by conventional performance indexes. In the paper, the conventional performance indexes are analyzed and then a new performance index for Maglev-based RGV is proposed. Also, an advanced genetic algorithm which is designed using clonal selection algorithm for performance improvement is proposed. To verify the proposed algorithm and the performance index, we compare the proposed method with a simple genetic algorithm and particle swarm optimization. The simulation results show that the proposed method is more effective than conventional optimization methods.

There are many researches to develop robots that improve its mobility to adapt in various uneven environments. In the paper, a hybrid mobile robot that can dock with the other robot and transforms between wheeled robot and legged robot is proposed. The hybrid mobile robot platform has docking device with a peg and a cup module. In addition, the robot is possible to walk and drive according to condition of the road. A navigation algorithm of the hybrid mobile robot is proposed to improve the mobility of robots using docking algorithm based on image processing on the broken road and uneven terrain. The proposed method recognizes road condition through PSD sensor attached in front and bottom of the robot and selects an appropriate navigation method according to terrain surface. The proposed docking and navigation methods are verified through experiments using hybrid mobile robots.

In this paper we propose a low-power walking compensation method for biped robot based on consumption energy analysis. Firstly, basic walking motions that can reduce energy consumption of robot movements are implemented based on consumption energy analysis according to robot axes. We define knee bent motion as a basic walking motion. It can improve energy consumption and motion stability by lowering center of gravity of the biped robot. We analyze consumption energy of left and right leg of the robot using motor currents and propose a compensation method of walking motions to reduce unbalance of consumption energy between left leg and right leg. It can also improve energy consumption and walking stability of the robot. The proposed low-power compensation method based on consumption energy analysis is verified by walking experiments of a small biped robot with an embedded system.

The accurate estimation of position and direction of the mobile robot is essential for preparing precise movement and works in the inner complex environment. In this paper, we propose a robust estimation method of location and direction using the velocity of mobile robot in the inner GPS environment. The estimation using the inner GPS with ultrasonic sensors have to consider with various acoustic noise and sensor errors. We design a robust estimation method using a membership function based on uncertainty of the obtained information and robot velocity. The simulation results of the proposed method show effectiveness in the contaminated environment with position errors.

In the paper, we propose an improved mobility method of modular robots by physical docking in the uneven environments. The modular robot system consists of autonomous docking device, 3 DOF robotic arm, motion controller, and main controller. Real-time location and direction of the robot are estimated using inner GPS and they are used to control direction and path of each robot for physical docking between modular robots. We design a navigation algorithm of modular robot using physical docking and cooperative navigation in the environment with broken road and low stair. The proposed method is verified by navigation experiments of three developed modular robots in the uneven environments.

Modular reconfigurable robots with physical docking capability easily adapt to a new environment and many studies are necessary for the modular robots. In this paper, we propose a vision-based fuzzy autonomous docking controller for the modular docking robots. A modular docking robot platform which performs real-time image processing is designed and color-based object recognition method is implemented on the embedded system. The docking robot can navigate to a subgoal near a target robot while avoiding obstacles. Both a fuzzy obstacle avoidance controller and a fuzzy navigation controller for subgoal tracking are designed. We propose an autonomous docking controller using the fuzzy obstacle avoidance and navigation controllers, absolute distance information and direction informations of robots from PSD sensors and a compass sensor. We verify the proposed docking control method by docking experiments of the developed modular robots in the various environments with different distances and directions between robots.

In this paper, we presents a behavior planning for humanoid robots using behavior primitive in 3 dimensional workspace. Also, we define behavior primitives that humanoid robot accomplishes various tasks effectively. Humanoid robot obtains information of the outside environment and its inner information from various sensors in complex workspace with various obstacles. We verify our approach on a developed small humanoid robot using embedded vision and sensor system in a experimental environment. The experimental results show that the humanoid robot performs its tasks fast and effectively.

In this paper, we propose a footstep planning method of biped robot based on the Particle Swarm Optimization(PSO). We define configuration and locomotion primitives for biped robots in the 2 dimensional workspace. A footstep planning method is designed using learning process of PSO that is initialized with a population of random objects and searches for optima by updating generations. The footstep planner searches for a feasible sequence of locomotion primitives between a starting point and a goal, and generates a path that avoids the obstacles. We design a path optimization algorithm that optimizes the footstep number and planning cost based on the path generated in the PSO learning process. The proposed planning method is verified by simulation examples in cluttered environments.

In this Paper, we Propose a new neuro-fuzzy modeling using clustering-based learning method. In the proposed clustering method, number of clusters is automatically inferred and its parameters are optimized simultaneously, Also, a neuro-fuzzy model is learned based on clustering information at same time. In the previous modelling method, clustering and model learning are performed independently and have no exchange of its informations. However, in the proposed method, overall neuro-fuzzy model is generated by using both clustering and model learning, and the information of modelling output is used to clustering of input. The proposed method improve the computational load of modeling using Subtractive clustering method. Simulation results show that the proposed method has an effectiveness compared with the previous methods.

In this paper are proposed 2nd-order PD-type iterative learning control algorithms for linear continuous-time system and linear discrete-time system. In contrast to conventional methods, the proposed learning algorithms are constructed based on both time-domain performance and iteration-domain performance. The convergence of the proposed learning algorithms is proved. Also, it is shown that the proposed method has robustness in the presence of external disturbances and the convergence accuracy can be improved. A numerical example is provided to show the effectiveness of the proposed algorithms.

In the paper, an intelligent biped walking robot that can overcome the obstacle is developed. Walking algorithms are designed based on the analysis of the human`s manner of walking. Infrared sensors are used to detect the obstacles in the working environment and the remote controller of the biped robot use a RF module. The experiment results show that the developed biped walking robot can perform the stable static walking, attention walking, rotation and side stepping to avoid the obstacle, and hurdling the obstacle using the distance correction algorithm that is designed based on the distance information between the biped robot and the obstacle.