This paper proposes the leader-follower based formation control method for multiple mobile robots. The controller is designed using the measurements of the follower robot such as the relative distance and angle between the leader and the follower. This means that the follower robot does not require the information of the leader robot while keeping the desired formation. Therefore, the proposed control method can reduce the communication loss and the cost for hardware. From Lyapunov stability theory, it is shown that all error signals in the closed-loop system are uniformly ultimately bounded. Finally, simulation results demonstrate the effectiveness of the proposed control system.

This paper presents a consensus algorithm for uMAS (uncertain Multi-Agent Systems). Unlike previous results in which only nominal models for agents are considered, it is assumed that the uncertain agent model belongs to a known polytope set. In the middle of deriving the proposed algorithm, a convex set is found which includes all uncertainties in the problem using convexity of the polytope set. This set plays an important role in designing the consensus algorithm for uMAS. Based on the set, a consensus condition for uMAS is proposed and the corresponding consensus design problem is solved using LMI (Linear Matrix Inequality). Simulation result shows that the proposed consensus algorithm successfully leads to consensus of the state of uMAS.

This paper provides an overview of distance-based formation control. Firstly, in this paper, we introduce preliminary background materials that are used in defining the distance-based formation control. Then, based on the preliminary background, we briefly review main results developed thus far in this field. Lastly, we provide some issues that need to be studied further in future works.

This paper proposes a bidirectional platoon control law using a coupled distance error based on the backstepping-like feedback linearization control method for an interconnected mobile agent system with a string structure. Unlike the previous results where the single agent was controlled using the only own information without other agents, the proposed control law cannot show the only distance error convergence of each agent, but also the string stability of the whole system. Also, the control performances are improved by the proposed control law in spite of low performance of bidirectional control strategy in the previous results. The proposed bidirectional platoon control algorithm is based on the backstepping-like feedback linearization control method. The position errors between each agent and the preceding and the behind agents are coupled by weighted summation. By the proposed control law, the distance error of each agent can converge to zero while the string stability is guaranteed when the coupled errors can converge to zero. To this end, the back-stepping control method is employed. The pseudo velocity input is determined considering the kinematic relationship between agents and the string stability. Then, the actual dynamic control input is determined to make the actual velocity converge to the pseudo velocity input. The stability analysis and the simulation results of the proposed method are included in order to demonstrate the practical application of the proposed algorithm.

This paper suggests a hybrid path planning method of multi-robots, where a path deviation prevention for maintaining a specific formation is implemented by using repulsive function, $A^*$ algorithm and UKF (Unscented Kalman Filter). The repulsive function in potential field method is used to avoid collision among robots and obstacles. $A^*$ algorithm helps the robots to find optimal path. In addition, error estimation based on UKF guarantees small path deviation of each robot during navigation. The simulation results show that the swarm robots with designated formation successfully avoid obstacles and return to the assigned formation effectively.

This paper investigates a boundary tracking problem using multiple quadrotor UAVs to detect and track the boundary of physical events. We set the boundary estimation problem as a classification problem of the region in which the physical events occur, and employ SVL (Support Vector Learning). We also demonstrate a velocity vector field which is globally attractive to a desired closed path with circulation at the desired speed and a virtual phase for stabilizing the collective configuration of the multiple quadrotors. Experimental results with multiple quadrotors show that this study provides good performance of the collective boundary tracking.

This paper proposes a CPSO (Cooperative Particle Swarm Optimization)-based MPC (Model Predictive Control) scheme to deal with formation control problem of multiple nonholonomic mobile robots. In a distributed MPC framework, each robot needs to optimize control input sequence over a finite prediction horizon considering control inputs of the other robots where their cost functions are coupled by the state variables of the neighboring robots. In order to optimize the control input sequence, a CPSO algorithm is adopted and modified to fit into the formation control problem. Experiments are performed on a group of nonholonomic mobile robots to demonstrate the effectiveness of the proposed CPSO-based MPC for multi-robot formation.

A swarm robot system consists of with multiple mobile robots, each of which is called an agent. Each agent interacts with others and cooperates for a given task and a given environment. For the swarm robotic system, the loss of the entire work capability by malfunction or damage to a single robot is relatively small and replacement and repair of the robot is less costly. So, it is suitable to perform more complex tasks. The essential component for a swarm robotic system is an inter-robot collaboration strategy for teamwork. Recently, the swarm intelligence theory is applied to robotic system domain as a new framework of collective robotic system design. In this paper, FA (Firefly Algorithm) which is based on firefly's reaction to the lights of other fireflies and their social behavior is employed to optimize the group behavior of multiple robots. The main application of the firefly algorithm is performed on path planning of swarm mobile robots and its effectiveness is verified by simulations under various conditions.

In this paper, a simulation system based on X-Plane and MATLAB/Simulink for multiple UAVs is presented. For the conceptual design of this proposed system, a hierarchical system architecture for multiple UAVs is presented. This architecture has object-oriented data structure which consists of three objects (UAV status, mission and task, and environment) and a hierarchy consisting of four layers (decision making layer, task assignment layer, path and motion planning layer, and collision avoidance layer) is also proposed. In addition, this paper shows a implementation of simulation system based on the proposed system architecture using X-Plane and MATLAB/Simulink. The result of simulation from the developed system in this paper validate capability of application for multiple UAVs in real environment.

This paper suggests the target tracking method improved for the collaboration of the quad rotor type UAV (Unmanned Aerial Vehicle) and omnidirectional Unmanned Ground Vehicle. If UAV shakes or UGV moves rapidly, the existing method generates a phenomenon that the tracking object loses the tracking target. To solve the problems, we propose an algorithm that can track continually when they lose the target. The proposed algorithm stores the vector of the landmark. And if the target was lost, the control signal was inputted so that the landmark could move continuously to the direction running out. Prior to the experiment, Proportional and integral control were used in 4 motors in order to calibrate the Heading value of the omnidirectional mobile robot. The landmark of UGV was recognized as the camera adhered to UAV and the target was traced through the proportional-integral-derivative control. Finally, the performance of the target tracking controller and proposed algorithm was evaluated through the experiment.

This paper proposes an image feature-based real-time RGB-D (Red-Green-Blue Depth) 3D SLAM (Simultaneous Localization and Mapping) system. RGB-D data from Kinect style sensors contain a 2D image and per-pixel depth information. 6-DOF (Degree-of-Freedom) visual odometry is obtained through the 3D-RANSAC (RANdom SAmple Consensus) algorithm with 2D image features and depth data. For speed up extraction of features, parallel computation is performed with GPU acceleration. After a feature manager detects a loop closure, a graph-based SLAM algorithm optimizes trajectory of the sensor and builds a 3D point cloud based map.

This paper presents a modified disturbance observer (MDOB) for controlling two arms of a manipulator designed for a home service robot. The MDOB is slightly different from the original DOB in that it uses an accelerometer to measure acceleration of the robot arm. Then it uses the acceleration to estimate the disturbance to cancel out in the control loop. Relying on the acceleration information of the robot arm, a partial model-based control structure is formed. Experimental studies of position control of 2 DOF robot arm are conducted to evaluate the performance of the proposed position control by an MDOB method.

This paper deals with the indoor localization problem for a SSMR (Skid Steering Mobile Robot) subjected to wheel-ground friction and with one LRF (Laser Range Finder). In order to compensate for some friction effect, a friction related coefficient is estimated by the recursive least square algorithm and appended to the maneuvering command. Also to reduce odometric information based localization errors, the lines are extracted with scan points of LRF and matched with the ones of the corresponding map built in advance. The present friction compensation and scan map matching schemes have been applied to a laboratory SSMR, and experimental results are given to validate the localization performance along an indoor corridor.

This paper describes the design of a rectangular-type four-finger rehabilitation robot for flexibility rehabilitation of stroke patients' fingers and other patient's paralyzed fingers. The four-finger rehabilitation robot is composed of a body and each finger rehabilitation robot instrument. The four-finger rehabilitation robot could exercise four fingers (forefinger, middle finger ring finger and little finger) of patient for their rehabilitation. The four-finger rehabilitation robot instruments move according to the trace which spread out the patient's fingers and then turn them inward for the fingers' flexibility, while at the same time performing the force control with the reference forces for fingers' safety, simultaneously. A control characteristic test of the developed rectangular-type four-finger rehabilitation robot was carried out, and the results confirmed that the robot could be used for the flexibility rehabilitation exercise for the fingers of normal person and patients.

This paper presents an FIR (Finite Impulse Response) fixed-interval smoothing filter for fast and exact estimating state variables of a discrete nonlinear system with modeling uncertainty. Conventional IIR (Infinite Impulse Response) filter and smoothing filter can estimate state variables of a system with an exact model when the system is observable. When there is an uncertainty in the system model, however, conventional IIR filter and smoothing filter may cause large errors because the filters cannot estimate the state variables corresponding to the uncertain model exactly. To solve this problem, FIR filters that have fast estimation properties and have robustness to the modeling uncertainty have been developed. However, there is time-delay estimation phenomenon in the FIR filter. The FIR smoothing filter proposed in this paper makes up for the drawbacks of the IIR filter, IIR smoothing filter, and FIR filter. Therefore, the FIR smoothing filter has good estimation performance irrespective of modeling uncertainty. The proposed FIR smoothing filter is applied to the integrated navigation system composed of a magnetic compass based DR (Dead Reckoning) and a GPS (Global Positioning System) receiver. Even when the magnetic compass error that changes largely as the surrounding magnetic field is modeled as a random constant, it is shown that the FIR smoothing filter can estimate the varying magnetic compass error fast and exactly with simulation results.