This paper proposes a robust image stabilization system for a mobile robot using an Extended Kalman Filter (EKF). Though image information is one of the most efficient data used for robot navigation, it is subjected to noise which is the result of internal vibration as well as external factors such as uneven terrain, stairs, or marshy surfaces. The camera vibration deteriorates the image resolution by destroying the image sharpness, which seriously prevents mobile robots from recognizing their environment for navigation. In this paper, an inclinometer was used to measure the vibration angle of the camera system mounted on the robot to obtain a reliable image by compensating for the angle of the camera vibration. In addition the angle prediction obtained by using the EKF enhances the image response analysis for real time performance. The experimental results show the effectiveness of the proposed system used to compensate for the blurring of the images.

We presents a dynamic modeling of 4-wheel 2-DOF. WMR. The classic dynamic model utilizes a greatly simplified wheel motion representation and using of a simplified dynamic model confronts with a problem for accurate position control of wheeled mobile robot. In this paper, we treats the dynamic model for describes relationship between the wheel actuator force/torque and WMR motion through the use of Newton's equilibrium laws. To calculate the WMR position in real time, we introduced the Dead-Reckoning algorithms and the simulation result show that the proposed dynamic model is useful. We can be easily extend the proposed WMR model to mobile robot of similar type and this type of methodology is useful to analyze, design and control any kinds of rolling robots.

One of the requirements for autonomous vehicles on off-road is to move stably in unstructured environments. Such capacity of autonomous vehicles is one of the most important abilities in consideration of mobility. So, many researchers use contact and/or non-contact methods to determine a terrain whether the vehicle can move on or not. In this paper we introduce an algorithm to classify terrains using visual information(one of the non-contacting methods). As a pre-processing, a contrast enhancement technique is introduced to improve classification of terrain. Also, for conducting classification algorithm, training images are grouped according to materials of the surface, and then Bayesian classification are applied to new images to determine membership to each group. In addition to the classification, we can build Traversability map specified by friction coefficients on which autonomous vehicles can decide to go or not. Experiments are made with Load-Cell to determine real friction coefficients of various terrains.

Marionette controlling robot has a problem that generates interference in rotation and intersection, therefore, the research on the independent shifter to move freely on the stage is required. Connecting omni-directional mobile robot with marionette controlling robot can solve this problem. Omni-directional mobile robot makes itself rotate and translate in 2D plane freely. Magnetic device is used to connect the moving part with the control part of the robot to minimize the intereference generated by the movement of robot. When robot moves, it can move to all directions with the suitalbe setting of banlance power. The moment of inertia is minimized by dividing the robot to the upper and lower parts in the marionette performance stage. Rotation and interference problem of independent omni-wheel Robot can be solved by using the permanent magnet. The efficiency and safety of the marionette controlling robot is proved by the experiment.

A gradient method can provide a global optimal path in indoor environments. However, the optimal path can be often generated in narrow areas despite a sufficient wide area which lead to safe navigation. This paper presents a novel approach to path planning for safe navigation of a mobile robot. The proposed algorithm extracts empty regions using a ray-casting method and then generates temporary waypoints in wider regions in order to reach the goal fast and safely. The experimental results show that the proposed method can generate paths in the wide regions in most cases and the robot can reach the goal safely at high speeds.

Various robot platforms have been designed and developed to perform given tasks in a hazardous environment for the purpose of surveillance, reconnaissance, search and rescue, and etc. We have considered a terrain adaptive hybrid robot platform which is equipped with rapid navigation on flat floors and good performance on overcoming stairs or obstacles. Since our special consideration is posed to its flexibility for real application, we devised a design of a transformable robot structure which consists of an ordinary wheeled structure to navigate fast on flat floor and a variable tracked structure to climb stairs effectively. Especially, track arms installed in front side, rear side, and mid side are used for navigation mode transition between flatland navigation and stairs climbing. The mode transition is determined and implemented by adaptive driving mode control of mobile robot. The wheel and track hybrid mobile platform apparatus applied off-road driving mechanism for various professional service robots is verified through experiments for navigation performance in real and test-bed environment.

For indoor mobile robots, the performance of autonomous navigation is affected by a variety of factors. In this paper, we focus on the characteristics of indoor absolute positioning systems. Two commercially available sensor systems are experimentally tested under various conditions. Mobile robot navigation experiments were carried out, and the results show that resultant performance of navigation is highly dependent upon the characteristics of positioning systems. The limitations and characteristics of positioning systems are analyzed from both quantitative and qualitative point of view. On the basis of the analysis, the relationship between the positioning system characteristics and the controller design are presented.

In this paper, we present a global localization and position error compensation method in a known indoor environment using magnet hall sensors. In previous our researches, it was possible to compensate the pose errors of xe, ye, θe correctly on the surface of indoor environment with magnets sets by regularly arrange the magnets sets of identical pattern. To improve the proposed method, new strategy that can realize the global localization by changing arrangement of magnet pole is presented in this paper. Total six patterns of the magnets set form the unique landmarks. Therefore, the virtual map can be built by using the six landmarks randomly. The robots search a pattern of magnets set by rotating, and obtain the current global pose information by comparing the measured neighboring patterns with the map information that is saved in advance. We provide experimental results to show the effectiveness of the proposed method for a differential drive wheeled mobile robot.

In this paper, we propose remote navigation control for intelligent robot using particle swarm optimization(PSO). The proposed system consists of interfaces for intelligent robot navigation and user interface in order to control the intelligent robot remotely. And communication interfaces using TCP/IP socket is used. To do this, we first design the fuzzy navigation controller based on expert's knowledge for intelligent robot navigation. At this time, we use the PSO algorithm in order to identify the membership functions of fuzzy control rules. And then, we propose the remote system in order to navigate the robot remotely. Finally, we show the effectiveness and feasibility of the developed controller and remote system through some experiments.

This paper proposes a low-complexity indoor localization method of mobile robot under the dynamic environment by fusing the landmark image information from an ordinary camera and the distance information from sensor nodes in an indoor environment, which is based on sensor network. Basically, the sensor network provides an effective method for the mobile robot to adapt to environmental changes and guides it across a geographical network area. To enhance the performance of localization, we used an ordinary CCD camera and the artificial landmarks, which are devised for self-localization. Experimental results show that the real-time localization of mobile robot can be achieved with robustness and accurateness using the proposed localization method.

This paper presents the development and control of a two wheeled car-like mobile robot using balancing mechanism whose heading control is done by turning the handle. The mobile inverted pendulum is a combined system of a mobile robot and an inverted pendulum system. A sensor fusion technique of low cost sensors such as a gyro sensor and a tilt sensor to measure the balancing angle of the inverted pendulum robot system accurately is implemented. Experimental studies of the trajectory following control task has been conducted by command of steering wheel while balancing.

It is difficult to find a practical solution for the backward-motion control of a car-like mobile robot with n passive trailers. Unlike an omni-directional robot, a car-like mobile robot has nonholonomic constraints and limitations of the steering angle. For these reasons, the backward motion control problem of a car-like mobile robot with n passive trailers is not trivial. In spite of difficulties, backing up a trailer system is useful for parking control. In this study, we proposed a mechanical alteration which is connecting n passive trailers to the front bumper of a car to improve the backward motion control performance. Theoretical verification and simulations show that the backward-motion control of a general car with n passive trailers can be successfully carried out by using the proposed approach.

Low-cost sensors have been widely used for mobile robot navigation in recent years. However, navigation performance based on low-cost sensors is not good enough to be practically used. Among many navigation techniques, building of an accurate map is a fundamental task for service robots, and mapping with low-cost IR sensors was investigated in this research. The robot’s orientation uncertainty was considered for mapping by modifying the Bayesian update formula. Then, the data association scheme was investigated to improve the quality of a built map when the robot’s pose uncertainty was large. Six low-cost IR sensors mounted on the robot could not give rich data enough to align the range data by the scan matching method, so a new sample-based method was proposed for data association. The real experiments indicated that the mapping method proposed in this research was able to generate a useful map for navigation.

This paper presents a remote monitoring and simulation system for a building cleaning mobile robot. It provides a tool of convenient 3D graphical map construction including network camera image viewer and status information of the robot. The 3D map is reconstructed from existing 2D building CAD data with DXF format using OpenGL graphic API. Through this system, it is possible to monitor and control the cleaning mobile robot from remote place. A practical experiment is performed to show the reliability and convenience of the monitoring system. The proposed system is expected to give efficient the way of control and monitoring to building cleaning mobile robot.

Most omni-directional mobile robots have to change their trajectory for avoiding obstacles regardless of the size of the obstacles. However, an omni-directional mobile robot having kinematic redundancy can maintain the trajectory while the robot avoids small obstacles. This works deals with the kinematic modeling and motion planning of an omni-directional mobile robot with kinematic redundancy. This robot consists of three wheel mechanisms. Each wheel mechanism is modeled as having four joints, while only three joints are necessary for creating the omni-directional motion. Thus, each chain has one kinematic redundancy. Two types of wheel mechanisms are compared and its kinematic modeling is introduced. Finally, several motion planning algorithms using the kinematic redundancy are investigated. The usefulness of this robot is shown through experiment.

Recently, simultaneous localization and mapping (SLAM) approaches employing Rao-Blackwellized particle filter (RBPF) have shown good results. However, no research is conducted to analyze the result representation of SLAM using RBPF (RBPF-SLAM) when particle diversity is preserved. After finishing the particle filtering, the results such as a map and a path are stored in the separate particles. Thus, we propose several result representations and provide the analysis of the representations. For the analysis, estimation errors and their variances, and consistency of RBPF-SLAM are dealt in this study. According to the simulation results, combining data of each particle provides the better result with high probability than using just data of a particle such as the highest weighted particle representation.

Collision avoidance is a fundamental and important task of an autonomous mobile robot for safe navigation in real environments with high uncertainty. Obstacles are classified into static and dynamic obstacles. It is difficult to avoid dynamic obstacles because the positions of dynamic obstacles are likely to change at any time. This paper proposes a scheme for vision-based avoidance of dynamic obstacles. This approach extracts object candidates that can be considered moving objects based on the labeling algorithm using depth information. Then it detects moving objects among object candidates using motion vectors. In case the motion vectors are not extracted, it can still detect the moving objects stably through their color information. A robot avoids the dynamic obstacle using the dynamic window approach (DWA) with the object path estimated from the information of the detected obstacles. The DWA is a well known technique for reactive collision avoidance. This paper also proposes an algorithm which autonomously registers the obstacle color. Therefore, a robot can navigate more safely and efficiently with the proposed scheme.

This paper describes a new method for indoor environment mapping and localization with stereo camera. For environmental modeling, we directly use the depth and color information in image pixels as visual features. Furthermore, only the depth and color information at horizontal centerline in image is used, where optical axis passes through. The usefulness of this method is that we can easily build a measure between modeling and sensing data only on the horizontal centerline. That is because vertical working volume between model and sensing data can be changed according to robot motion. Therefore, we can build a map about indoor environment as compact and efficient representation. Also, based on such nodes and sensing data, we suggest a method for estimating mobile robot positioning with random sampling stochastic algorithm. With basic real experiments, we show that the proposed method can be an effective visual navigation algorithm.

This paper describes the recognition method of moving objects in mobile robot with an omnidirectional camera. The moving object is detected using the specific pattern of an optical flow in omnidirectional image. This paper consists of two parts. In the first part, the pattern of an optical flow is investigated in omnidirectional image. The optical flow in omnidirectional image is influenced on the geometry characteristic of an omnidirectional camera. The pattern of an optical flow is theoretically and experimentally investigated. In the second part, the detection of moving objects is presented from the estimated optical flow. The moving object is extracted through the relative evaluation of optical flows which is derived from the pattern of optical flow. In particular, Focus-Of-Expansion (FOE) and Focus-Of-Contraction (FOC) vectors are defined from the estimated optical flow. They are used as reference vectors for the relative evaluation of optical flows. The proposed algorithm is performed in four motions of a mobile robot such as straight forward, left turn, right turn and rotation. Experimental results using real movie show the effectiveness of the proposed method.

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision-based global localization algorithm.