Recently, automatic parking assist systems are commercially available in some cars. In order to improve the reliability and the accuracy of parking control, pose uncertainty of a vehicle and some experimental issues should be solved. In this paper, following three schemes are proposed. (1) Odometry calibration scheme for the Car-Like Mobile Robot.(CLMR) (2) Accurate localization using Extended Kalman Filter(EKF) based redundant odometry fusion. (3) Trajectory tracking controller to compensate the tracking error of the CLMR. The proposed schemes are experimentally verified using a miniature Car-Like Mobile Robot. This paper shows that odometry accuracy and trajectory tracking performance can be dramatically improved by using the proposed schemes.

We propose a navigation algorithm using image-based visual servoing utilizing a fixed camera. We define the mobile robot navigation problem as an unconstrained optimization problem to minimize the image error between the goal position and the position of a mobile robot. The residual function which is the image error between the position of a mobile robot and the goal position is generally large for this navigation problem. So, this navigation problem can be considered as the nonlinear least squares problem for the large residual case. For large residual, we propose a method to find the second-order term using the secant approximation method. The performance was evaluated using the simulation.

The paper proposes a human-following behavior of mobile robot and an intelligent space (ISpace) is used in order to achieve these goals. An ISpace is a 3-D environment in which many sensors and intelligent devices are distributed. Mobile robots exist in this space as physical agents providing humans with services. A mobile robot is controlled to follow a walking human using distributed intelligent sensors as stably and precisely as possible. The moving objects is assumed to be a point-object and projected onto an image plane to form a geometrical constraint equation that provides position data of the object based on the kinematics of the intelligent space. Uncertainties in the position estimation caused by the point-object assumption are compensated using the Kalman filter. To generate the shortest time trajectory to follow the walking human, the linear and angular velocities are estimated and utilized. The computer simulation and experimental results of estimating and following of the walking human with the mobile robot are presented.

본 논문에서는 웨이퍼 레벨 밀봉 실장된 수직 운동 가속도 신호를 감지할 수 있는 초소형 Z축 가속도 센싱 엘리먼트를 제작하였다. 초소형 Z축 가속도 센싱 엘리먼트는 수직 방향의 정전용량 변화를 필요로 하기 때문에 단일 기판상에 수직 단차의 형성을 가능케 하는 확장된 희생 몸체 미세 가공 기술 (Extended Sacrificial Bulk Micromachining, ESBM) 을 이용하여 제작되었다. 확장된 희생 몸체 미세 가공 기술을 이용하면 정렬오

A robust position-sensing system is proposed in this paper for ubiquitous mobile robots which move indoor as well as outdoor. The Differential GPS (DGPS) which has position estimation error of less than 5 m is a general solution when the mobile robots are moving outdoor, while an active beacon system (ABS) with embedded ultrasonic sensors is selected as an indoor positioning system. The switching from the outdoor to indoor or vice versa causes unstable measurements on account of the reference and algorithm changes. To minimize the switching time in the position estimation and to stabilize the measurement, a robust position-sensing system is proposed. In the system, to minimize the switching delay, the door positions are stored and updated in a database. The reliability and accuracy of the robust positioning system based on DGPS and ABS are verified through the real experiments using a mobile robot prepared for this research and demonstrated.

This paper proposes a method of avoiding obstacles and tracking a moving object continuously and simultaneously by using new concepts of virtual tow point and fuzzy danger factor for differential wheeled mobile robots. Since differential wheeled mobile robot has smaller degree of freedom to control and are non-holonomic systems, there exist multiple solutions (trajectories) to control and reach a target position. The paper proposes 'fuzzy danger factor' for obstacles avoidance, 'virtual tow point' to solve non-holonomic object tracking control problem for unique solution and three kinds of fuzzy logic controller. The fuzzy logic controller is policy decision controller with fuzzy danger factor to decide which controller's result is more valuable when the mobile robot is tracking a moving object with obstacles to be avoided.

The most important thing for navigation of a mobile robot is to find the most suitable path and avoid the obstacles in the static and dynamic environment. This paper presents a method to search the optimal path in start space extended to time domain with considering a velocity and a direction of moving obstacles. A modified version of A* algorithm has been applied for path planning in this work and proposed a method of path search to avoid a collision with moving obstacle in space-tim domain with a velocity and an orientation of obstacles. The velocity and the direction for moving obstacle are assumed as linear form. The simulation result shows that a mobile robot navigates safely among moving obstacles of constant linear velocity. This work can be applied for not only a moving robot but also a legged humanoid robot and all fields where the path planning is required.

It is dangerous that an inspector overhauls defects and condition of the inner parts of an oil tanker because of many harmful gases, complex structures, and etc. However, these inspections are necessary to many oil tankers over old years. In this study, we proposed the design of mobile robot for inspection of CAS in oil tanker. The developed CAS inspection mobile robot has four modules, a measurement module of oil tanker's thickness, a corrosion inspection module, a climbing module of the surface on a wall, and a monitoring module. In order to get over at a check position, the driving control algorithm was developed. Magnetic wheels are used to move on the surface of a wall. This study constructed a communication network and the monitoring program to operate the developed mobile robot from remote sites. In order to evaluate the inspection ability, the experiments about performance of CAS inspection using the developed mobile robot have been carried out.

Mobile robots traveling on rough terrain need several algorithms to overcome obstacles. In this paper, we propose the step-type obstacle traversal algorithm to adapt the mobile robot with six arms and wheels to travel on rough terrain. Obstacle traversal is composed of two different stages: planning and control. In planning stage, the required joint torque of each arm as well as the interference between the wheels and the arms are analyzed to guarantee traversing obstacles. Control stage includes such steps as checking distance to obstacle, determining the height and length of obstacle, performing arm motion according to sensed torque data, and evaluating safety at every instance. The proposed algorithm is designed and implemented for CALEB 1 six legged robot developed in the laboratory and verified by simulation and experiment in outdoor environment.

Recently, with the development of service robots and with the new concept of ubiquitous world, the position estimation of mobile objects has been raised to an important problem. As pre-liminary research results, some of the localization schemes are introduced, which provide the absolute location of the moving objects subjected to large errors. To implement a precise and convenient localization system, a new absolute position estimation method for a mobile robot in indoor environment is proposed in this paper. Design and implementation of the localization system comes from the usage of active beacon systems (based upon RFID technology). The active beacon system is composed of an RFID receiver and an ultra-sonic transmitter: 1. The RFID receiver gets the synchronization signal from the mobile robot and 2. The ultra-sonic transmitter sends out the traveling signal to be used for measuring the distance. Position of a mobile robot in a three dimensional space can be calculated basically from the distance information from three beacons and the absolute position information of the beacons themselves. Since it is not easy to install the beacons at a specific position precisely, there exists a large localization error and the installation time takes long. To overcome these problems, and provide a precise and convenient localization system, a new auto calibration algorithm is developed in this paper. Also the extended Kalman filter has been adopted for improving the localization accuracy during the mobile robot navigation. The localization accuracy improvement through the proposed auto calibration algorithm and the extended Kalman filter has been demonstrated by the real experiments.

We present an implementation of particle filter algorithm for global localization and kidnap recovery of mobile robot. Firstly, we propose an algorithm for efficient particle initialization using sonar line features. And then, the average likelihood and entropy of normalized weights are used as a quality measure of pose estimation. Finally, we propose an active kidnap recovery by adding new particle set. New and independent particle set can be initialized by monitoring two quality measures. Added particle set can re-estimate the pose of kidnapped robot. Experimental results demonstrate the capability of our global localization and kidnap recovery algorithm.

Though the final goal of mobile robot navigation is to be autonomous, operators intelligent and skillful decisions are necessary when there are many scattered obstacles. There are several limitations even in the camera-based tele-operation of a mobile robot, which is very popular for the mobile robot navigation. For examples, shadowed and curved areas acnnot be viewed using a narrow view-angle camera, especially in bad weather such as on snowy or rainy days. Therefore, it is necessary to have other sensory information for reliable tele-operations. In this paper, sixteen ultrasonic sensors are attached around a mobile robot in a ring pattern to measure the distances to obstacles. Acollision vector is introduced in this paper as a new tool for obstacle avoidance, which is defined as a normal vector from an obstacle to the mobile robot. Based on this collision vector, a virtual reflection force is generated to avoid the obstacles and then the reflection force is transferred to an operator who is holding a joystick to control the mobile robot. Relying on the reflection force, the operator can control the mobile robot more smoothly and safely. For this bi-directional tele-operation, a master joystick system using a hall sensor was designed to resolve the existence of nonlinear sections, which are usual for a general joystick with two motors and potentiometers. Finally, the efficiency of a force reflection joystick is verified through the comparison of two vision-based tele-operation experiments, with and without force reflection.

The Mobile robots are increasingly being used to perform tasks in unknown environments. The potential of robots to undertake such tasks lies in their ability to intelligently and efficiently search in an environment. An algorithm has been developed for robots which explore the environment to measure the physical properties (dust in this paper). While the robot is moving, it measures the amount of dust and registers the value in the corresponding grid cell. The robot moves from local maximum to local minimum, then to another local maximum, and repeats. To reach the local maximum or minimum, simple gradient following is used. Robust estimation of the gradient using perturbation/correlation, which is very effective when analytical solution is not available, is described. By introducing the probability of each grid cell, and considering the probability distribution, the robot doesn’t have to visit all the grid cells in the environment still providing fast and efficient sensing. The extended algorithm to coordinate multiple robots is presented with simulation results.

Path planing method for an autonomous mobile robot is considered. For the practical applications, the simplified local potential field methods are applied under the constraints of the driving condition. To improve the performance, the fuzzy-approximated linear function method is also used.