This paper considers multi-agent systems with interval time-varying delays and switching interconnection topology. By construction of a suitable Lyapunov-Krasovskii's functional, new delay-dependent consensus control conditions for the systems are established in terms of LMIs (Linear Matrix Inequalities) which can be easily solved by various effective optimization algorithms. One numerical example is given to illustrate the effectiveness of the proposed methods.

In this paper, we discuss an observer-based fault tolerant controller design for the unmanned aerial vehicle (UAV) systems with exogenous disturbance. To derive robust controller design conditions, we use $H_{\infty}$ design technique. The design conditions are derived in terms of linear matrix inequalities. An illustrative example is provided to show the effectiveness of the proposed methodology.

This paper presents a multi-robot path planner for environment exploration and monitoring. Robotics systems are being widely used as data measurement tools, especially in dangerous environment. For large scale environment monitoring, multiple robots are required in order to save time. The path planner should not only consider the collision avoidance but efficient coordination of robots for optimal measurements. Nonlinear spring force based planning algorithm is integrated with the spatial gradient following path planner. Perturbation/Correlation based estimation of spatial gradient is applied. An algorithm of tuning the stiffness for robot coordination is presented. The performance of the proposed algorithm is discussed with simulation results.

This paper presents an approach to implement the behaviors of multi-robots responding to user's input characters. The robots are appropriately displaced to express any input characters. Using our method, any user can easily and friendly control multirobots. The responses of the robots to the user's input are intuitive. We utilize the centroidal Voronoi algorithm and the continuoustime Lloyd algorithm, which have popularly been used for the optimal sensing coverage problems. Collision protection is considered to be applied for real robots. LED sensors are used to identify positions of multi-robots. Our approach is evaluated through experiments with five mobile robots. When a user draw alphabets, the robots are deployed correspondingly. By checking position errors, the feasibility of our method is validated.

The positions of vessels in JTIDS where each vessel broadcasts its position, can be found using the relative navigation method. Besides positioning, the relative navigation could be adopted for identification friend or foe, tracking targets, monitoring battle field and etc. In this paper, we have explained the fundamental operation and technical structure for the relative navigation and implemented the simulation platform to evaluate the basic function and performance of the system in arbitrary environment. Using platform, the availability of relative navigation within the group network and the characteristic of the algorithm for position prediction was verified. Based on the simulation result, it was verified that EKF based navigation algorithm could produce great initial error and need quite convergence time. To improve the performance, we proposed a new navigation algorithm which uses the minimum norm estimation algorithm until the EKF converges. The simulation results reveal the relative navigation can be effectively used in the formation flight and collision avoidance system.

In this paper, a operation network system equipped with onboard wireless communication systems and ground-based mission control systems is proposed for swarm flight of small UAVs. This operating system can be divided into two networks, UAV communication network and ground control system. The UAV communication network is intend to exchange the informations of navigation, mission and flight status with minimum time delay. The ground control system consisted of mission control systems and UDP network. Proposed operation network system can make a swarm flight of various UAVs, execute complex missions decentralizing mission to several UAVs and cooperte several missions. Finally, PILS environments are developed based on the total operating system.

In this study, we address the formation control problem of coupled unicycle-type mobile robots, each of which can interact with its neighboring robots by communicating their position outputs. Each communication link between two mobile robots is assumed to be established according to the given time-varying interconnection topology that switches within a finite set of connected fixed undirected networks and has a non-vanishing dwell time. Under this setup, we propose a distributed formation control algorithm by using the dynamics extension and feedback linearization methods, and by employing a consensus algorithm for linear multi-agent systems which provides arbitrary fast convergence rate to the agreement of the multi-agent system. Finally, the proposed result is demonstrated through a computer simulation.

With the increasing number of high-rise and large-scale buildings, modern buildings are becoming intelligent, and are incurring high construction costs and requiring careful maintenance. Maintenance works for high-rise buildings significantly depend on human labor, unlike other construction processes that are gradually being automated. The resulting accidents may produce very high social and economic losses. To address this problem, herein, this paper proposes robotic building maintenance system using multi-robot concept, in specific, cleaning a building facade which is directly subjected to minimize human labor; that improves the process efficiency and economic feasibility. The sensor for detecting contamination of building's outer-wall glass is proposed; Kalman filter was used for estimating robots' status with the contamination of the window glass. Task allocation of the sensor based multi-robots for an effective way of task execution is introduced and the feasibility was verified through the simulations.

This is a research of an adaptive longitudinal control system for situated cognition in wide range, traffic accidents reduction and safety driving environment by integrated system which graft a road infrastructure's information based on IT onto the intelligent vehicle combined automobile and IT technology. The road infrastructure installed by laser scanner in intersection, speed limited area and sharp curve area where is many risk of traffic accident. The road infra conducts objects recognition, segmentation, and tracking for determining dangerous situation and communicates real-time information by Ethernet with vehicle. Also, the data which transmitted from infrastructure supports safety driving by integrated with laser scanner's data on vehicle bumper.

This paper proposes a distributed autonomous control method of swarm robot behavior strategy based on artificial immune system and an optimization strategy for artificial immune system. The behavior strategies of swarm robot in the system are depend on the task distribution in environment and we have to consider the dynamics of the system environment. In this paper, the behavior strategies divided into dispersion and aggregation. For applying to artificial immune system, an individual of swarm is regarded as a B-cell, each task distribution in environment as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows: When the environmental condition changes, the agent selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other agent using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. In order to decide more accurately select the behavior strategy, the optimized parameter learning procedure that is represented by stimulus function of antigen to antibody in artificial immune system is required. In this paper, particle swarm optimization algorithm is applied to this learning procedure. The proposed method shows more adaptive and robustness results than the existing system at the viewpoint that the swarm robots learning and adaptation degree associated with the changing of tasks.

A foot motion is estimated using an inertial sensor unit, which is installed on a shoe. The inertial sensor unit consists of 3 axis accelerometer and 3 axis gyroscopes. Attitude and position of a foot are estimated using an inertial navigation algorithm. To increase estimation performance, a smoother is used, where the smoother employs a forward and backward filter structure. An indirect Kalman filter is used as a forward filter and backward filter. A new combining algorithm for the smoother is proposed to combine a forward indirect Kalman filter and a backward indirect Kalman filter. Through experiments, the estimation performance of the proposed smoother is verified.

In this paper, a ranging system is proposed that is able to measure 360 degree omnidirectional distances to environment objects. The ranging system is based on the structured light imaging system with catadioptric omnidirectional mirror. In order to make the ranging system robust against environmental illumination, efficient structured light image processing algorithms are developed; sequential integration of difference images with modulated structured light and radial search based on Bresenham line drawing algorithm. A dedicated FPGA image processor is developed to speed up the overall image processing. Also the distance equation is derived in the omnidirectional imaging system with a hyperbolic mirror. It is expected that the omnidirectional ranging system is useful for mapping and localization of mobile robot. Experiments are carried out to verify the performance of the proposed ranging system.

In this paper, a Smart Home Service Robot, McBot II, which performs mess-cleanup function etc. in house, is designed much more optimally than other service robots. It is newly developed in much more practical system than McBot I which we had developed two years ago. One characteristic attribute of mobile platforms equipped with a set of dependent wheels is their omni- directionality and the ability to realize complex translational and rotational trajectories for agile navigation in door. An accurate coordination of steering angle and spinning rate of each wheel is necessary for a consistent motion. This paper develops trajectory controller of 3-wheels omni-directional mobile robot using fuzzy azimuth estimator. A specialized anthropomorphic robot manipulator which can be attached to the housemaid robot McBot II, is developed in this paper. This built-in type manipulator consists of both arms with 4 DOF (Degree of Freedom) each and both hands with 3 DOF each. The robotic arm is optimally designed to satisfy both the minimum mechanical size and the maximum workspace. Minimum mass and length are required for the built-in cooperated-arms system. But that makes the workspace so small. This paper proposes optimal design method to overcome the problem by using neck joint to move the arms horizontally forward/backward and waist joint to move them vertically up/down. The robotic hand, which has two fingers and a thumb, is also optimally designed in task-based concept. Finally, the good performance of the developed McBot II is confirmed through live tests of the mess-cleanup task.

An equilibrium point of a WIP (Wheeled Inverted Pendulum) with changing its center of gravity is derived and validated by various numerical simulations. Generally, the WIP has two equilibrium points which are unstable and stable one. The unstable one is interested in this study. To keep the WIP over the unstable equilibrium point, the WIP is consistently being adjusted. A state feedback controller for the WIP needs a control reference for the equilibrium point. The control reference can be obtained by studying an equilibrium point of the WIP based on statics. By using Lagrange method, this study is deriving dynamic equations of the WIP both with and without changing its center of gravity. Various numerical simulations are carried out to show the validation of the equilibrium point.

Heliostat, as a concentrator to reflect the incident solar energy to the receiver, is the most important system in the tower-type solar thermal power plant since it determines the efficiency and ultimately the overall performance of solar thermal power plant. Thus, a good sun tracking ability as well as a good optical property of it are required. Heliostat sun tracking system uses usually an open loop control system. Thus the sun tracking error caused by heliostat's geometrical error, optical error and computational error cannot be compensated. Recently use of sun tracking error model to compensate the sun tracking error has been proposed, where the error model is obtained from the measured ones. This work is a development of heliostat sun tracking error measurement and compensation method using BCS (Beam Characterization System). We first developed an image processing system to measure the sun tracking error optically. Then the measured error is modeled in linear polynomial form and neural network form trained by the extended Kalman filter respectively. Finally error models are used to compensate the sun tracking error. We also developed the necessary image processing algorithms so that the heliostat optical properties such as maximum heat flux intensity, heat flux distribution and total reflected heat energy could be analyzed. Experimentally obtained data shows that the heliostat sun tracking accuracy could be dramatically improved using either linear polynomial type error model or neural network type error model. Neural network type error model is somewhat better in improving the sun tracking performance. Nevertheless, since the difference between two error models in compensation of sun tracking error is small, a linear error model is preferred in actual implementation due to its simplicity.