This paper presents a new approach for mobile robot heading detection using MEMS Gyro north finding method in the indoor environment. Based on this, the robot heading angle measurement scheme is proposed; improved north finding theory and algorithm are also explained. Several approaches are applied to confirm system’s precision and effectiveness. In order to find out the heading angle, a single axis MEMS gyroscope to sense the angle between the robot heading direction and the north is used. To reach enough estimation accuracy and reduce detection time,the least square method (LSM) for the signal fitting and parameter estimation is applied. Through a turn‐table, we setup a carouseling system to decrease the substantial bias effect on gyroscope’s heading angle. For the evaluation of the proposed method, this system is implemented to the Pioneer robot platform. The performance and heading error are analyzed after the test. From the simulation and experimental results, system’s accuracy, usefulness and adaptability are shown.

Global positioning system (GPS) is widely used to measure the position of a vehicle. However, the accuracy of the GPS can be severely affected by surrounding environmental conditions. To deal with this problem, the GPS and odometry data can be combined using an extended Kalman filter. For stable navigation of an outdoor mobile robot using the GPS, this paper proposes two methods to evaluate the reliability of the GPS data. The first method is to calculate the standard deviation of the GPS data and reflect it to deal with the uncertainty of the GPS data. The second method is to match the GPS data to the traversability map which can be obtained by classifying outdoor terrain data. By matching of the GPS data with the traversability map, we can determine whether to use the GPS data or not. The experimental results show that the proposed methods can enhance the performance of the GPS‐based outdoor localization.

This paper propose a localization system of indoor mobile robots. The localization system includes camera and artificial landmarks for global positioning, and encoders and gyro sensors for local positioning. The Kalman filter is applied to take into account the stochastic errors of all sensors. Also we develop a dead reckoning system to estimate the global position when the robot moves the blind spots where it cannot see artificial landmarks, The learning engine using modular networks is designed to improve the performance of the dead reckoning system. Experimental results are then presented to verify the usefulness of the proposed localization system.

The human-following is one of the significant procedure in human-friendly navigation of mobile robots. There are many approaches of human-following technology. Many approaches have adopted various multiple sensors such as vision system and Laser Range Finder (LRF). In this paper, we propose detection and tracking approaches for human legs by the use of a single LRF. We extract four simple attributes of human legs. To define the boundary of extracted attributes mathematically, we used a Support Vector Data Description (SVDD) scheme. We establish an efficient leg-tracking scheme by exploiting a human walking model to achieve robust tracking under occlusions. The proposed approaches were successfully verified through various experiments.

This paper proposes a technique of indoor localization for mobile robot by so called indoor GPS and EKF. Basically the concept of indoor GPS is similar outdoor GPS, and the indoor GPS gets distances between Anchors and Tag by a ranging method of CSS and then estimates the coordinate by distances and known Anchor positions. After we performed performance test of indoor GPS system in ideal and multipath environment, we configured that the indoor GPS has internal error factors and external error factors. This paper handled a multipath problem belonging to external error factors. At first various direct physical method are introduced to fix the multipath problems, and as expected we got errors corrected considerably. And then the method of selective anchors for indoor GPS is applied. With these two level improvement of indoor GPS performance, EKF(Extended Kalman Filter) is applied to mobile robot in indoor environment. The usefulness of the proposed methods are shown by a series of experiments in a environment giving contaminated data by multipath.

In many industries, the accurate and quick checking of goods in storage is of great importance. Most today's inventory checking is based on bar code scanning, but the relative position between a bar code and an optical scanner should be maintained in close distance and proper angle for the successful scanning. This requirement makes it difficult to fully automate the inventory information/control systems. The use of RFID technology can be a solution for overcoming this problem. The mobile robot presented in this paper is equipped with an RFID tag scanning system, that automates the otherwise manual or semi-automatic inventory checking process. We designed the robot system in a quite practical approach, and the developed system is close to the commercialization stage. In our experiments, the robot could collect information of goods stacked on shelves autonomously without any failure and maintain corresponding database while it navigated predefined paths between the shelves using vision.

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.

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, a wall climbing robot, called LAVAR, is developed, which is using an impeller for adhering. The adhesion mechanism of the robot consists of an impeller and two-layered suction seals which provide sufficient adhesion force for the robot body on the non smooth vertical wall and horizontal ceiling. The robot uses two driving-wheels and one ball-caster to maneuver the wall surface. A suspension mechanism is also used to overcome the obstacles on the wall surface. For its design, the whole adhering mechanism is analyzed and the control system is built up based on this analysis. The performances of the robot are experimentally verified on the vertical and horizontal flat surfaces.

This paper suggests a multiple robot simulator which considers the uncertainties in robot motion and sensing. A mobile robot moves with errors due to some kinds of uncertainties from actuators, wheels, electrical components, environments. In addition, sensors attached to a mobile robot can't make accurate output information because of uncertainties of the sensor itself and environment. Uncertainties in robot motion and sensing leads researchers find difficulty in building mobile robot navigation algorithms. Generally, a robot algorithm without considering unexpected uncertainties fails to control its action in a real working environment and it leads to some troubles and damages. Thus, the authors propose a simulator model which includes robot motion and sensing uncertainties to help making robust algorithms. Sensor uncertainties are applied in range sensors which are widely used in mobile robot localization, obstacle detection, and map building. The paper shows performances of the proposed simulator by comparing it with a simulator without any uncertainty.

Recently researches on the window cleaning robot are being conducted actively. Moving mechanisms of these window cleaning robots are divided into two categories, which are towed type and walking type. Towed type is focused on fast cleaning on the flat surface of building and walking type has priority on cleaning task on relatively complex surface with overcoming obstacles. Currently commercialized towed type window cleaning robot has weakness that it is hard to adhere closely with the wall and easy to be affected by wind. In case of walking type it has the problem that the position errors are continuously accumulated during motion. In this paper, we propose new towed and walking type mechanism which can compensate previous weaknesses. After that we estimate the performance of each proposed mechanism by simulation.

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.

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 goal-directed reactive obstacle avoidance method based on lane method. The reactive collision avoidance is necessarily required for a robot to navigate autonomously in dynamic environments. Many methods are suggested to implement this concept and one of them is the lane method. The lane method divides the environment into lanes and then chooses the best lane to follow. The proposed method does not use the discrete lane but chooses a line closest to the original target line without collision when an obstacle is detected, thus it has a merit in the aspect of running time and it is more proper for narrow corridor environment. If an obstacle disturbs the movement of a robot by blocking a target path, a robot generates a temporary target line, which is parallel to an original target line and tangential to an obstacle circle, to avoid a collision with an obstacle and changes to and follows that line until an obstacle is removed. After an obstacle is clear, a robot returns to an original target line and proceeds to the goal point. Obstacle is recognized by laser range finder sensor and represented by a circle. Our method has been implemented and tested in a corridor environment and experimental results show that our method can work reliably.

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.

One of the main problems of topological localization in a real indoor environment is variations in the environment caused by dynamic objects and changes in illumination. Another problem arises from the sense of topological localization itself. Thus, a robot must be able to recognize observations at slightly different positions and angles within a certain topological location as identical in terms of topological localization. In this paper, a possible solution to these problems is addressed in the domain of global topological localization for mobile robots, in which environments are represented by their visual appearance. Our approach is formulated on the basis of a probabilistic model called the Bayes filter. Here, marginalization of dynamics in the environment, marginalization of viewpoint changes in a topological location, and fusion of multiple visual features are employed to measure observations reliably, and action-based view transition model and action-associated topological map are used to predict the next state. We performed experiments to demonstrate the validity of our proposed approach among several standard approaches in the field of topological localization. The results clearly demonstrated the value of our approach.

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.