Tae-Seok Jin

A new approach using sensor data fusion for mobile robot navigation

To fully utilize the information from the sensors, this paper proposes a new sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable an accurate measurement. Exploration of an unknown environment is an important task for the new generation of mobile service robots. The mobile robots may navigate by means of a number of monitoring systems such as the sonar-sensing system or the visual-sensing system. Note that in the conventional fusion schemes, the measurement is dependent on the current data sets only. Therefore, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the accurate measurement. The theoretical basis is illustrated by examples and the effectiveness is proved through the simulations and experiments. The newly proposed, STSF (Space and Time Sensor Fusion) scheme is applied to the navigation of a mobile robot in an unstructured environment, as well as in structured environment, and the experimental results show the performance of the system.

A study on position determination for mobile robot navigation in an indoor environment

Position estimation is one of the most important functions for the mobile robot navigating in the unstructured environment. Most of previous localization schemes estimate current position and pose of mobile robot by applying various localization algorithms with the information obtained from sensors which are set on the mobile robot, or by recognizing an artificial landmark attached on the wall, or objects of the environment as natural landmark in the indoor environment. Several drawbacks about them have been brought up. To compensate the drawbacks, a new localization method that estimates the absolute position of the mobile robot by using a fixed camera on the ceiling in the corridor is proposed. And also, it can improve the success rate for position estimation using the proposed method, which calculates the real size of an object. This scheme is not a relative localization, which decreases the position error through algorithms with noisy sensor data, but a kind of absolute localization. The effectiveness of the proposed localization scheme is demonstrated through the experiments.

Obstacle avoidance algorithm for visual navigation using ultrasonic sensors and a CCD camera

An obstacle-avoidance algorithm is presented for autonomous mobile robots equipped with a CCD camera and ultrasonic sensors. This approach uses segmentation techniques to segregate the floor from other fixtures, and measurement techniques to measure the distance between the mobile robot and any obstacles. It uses a simple computation for the selection of a threshold value. This approach also uses a cost function, which is combined with image information, distance information, and a weight factor, to find an obstacle-free path. This algorithm, which uses a CCD camera and ultrasonic sensors, can be used for cases including shadow regions, and obstacles in visual navigation and in various lighting conditions.

Mobile robot navigation used space and time sensor fusion in an unknown environment

This paper proposes a sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the service robot itself. In the conventional fusion schemes, the measurement is dependent on the current data sets. As the results, more of sensors are required to measure a certain physical parameter or to improve the accuracy of the measurement. However in this approach, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is illustrated by examples and the effectiveness is proved through the simulations. Finally, the new space and time sensor fusion (NSTF) scheme is applied to the control of a mobile robot in an unstructured environment as well as structured environment.

The position/Orientation determination of a mobile-task robot using an active calibration scheme

A new method of estimating the pose of a mobile-task robot is developed based upon an active calibration scheme. The utility of a mobile-task robot is widely recognized, which is formed by the serial connection of a mobile robot and a task robot. To be an efficient and precise mobile-task robot, the control uncertainties in the mobile robot should be resolved. Unless the mobile robot provides an accurate and stable base, the task robot cannot perform various tasks. For the control of the mobile robot, an absolute position sensor is necessary. However, on account of rolling and slippage of wheels on the ground, there does not exist any reliable position sensor for the mobile robot. This paper proposes an active calibration scheme to estimate the pose of a mobile robot that carries a task robot on the top. The active calibration scheme is to estimate a pose of the mobile robot using the relative position/orientation to a known object whose location, size, and shape are known a priori. For this calibration, a camera is attached on the top of the task robot to capture the images of the objects. These images are used to estimate the pose of the camera itself with respect to the known objects. Through the homogeneous transformation, the absolute position/orientation of the camera is calculated and propagated to get the pose of a mobile robot. Two types of objects are used here as samples of work-pieces : a polygonal and a cylindrical object. With these two samples, the proposed active calibration scheme is verified experimentally.

A new space and time sensor fusion for vision-based navigation by mobile robot system

This paper proposes a sensor-fusion technique where the data sets for the previous moments are properly transformed and fused into the current data sets to enable accurate measurement, such as, distance to an obstacle and location of the mobile robot itself. In the conventional fusion schemes, the measurement is dependent on current data sets. As the results, more of sensors required to measure a certain physical parameter or to improve the accuracy of the measurement. However, in this approach, instead of adding more sensors to the system, the temporal sequence of the data sets are stored and utilized for the measurement improvement. Theoretical basis is illustrated by examples and the effectiveness is proved through the simulations and experiments. Finally, the new space and time sensor fusion (STSF) scheme is applied to the vision-based control of a mobile robot in an unstructured environment as well as structured environment.

A study on the optimal path of a mobile robot based on mobility and recurrency

In this article, we define mobility and recurrency, and acquire the desired mobility and recurrency which result from the dynamic space change between two wheels of a mobile robot, assuming that the robot has a fixed wheel radius. When a robot has a fixed interval between its two wheels, it cannot avoid a sudden, obstacle because of the constraints of mobility and recurrency. The focus of this article is on instant recurrency, but high, stable mobility. That is, by changing the space between the two wheels in the simulation, the mobile robot could gain high recurrency instantly, and also high mobility with stability. We supposed that mobility and recurrency, as defined in this article, are applied to the design of a mobile robot. We suggest a theoretical change based on the optimal design of a mobile robot. The experimental data support the validity of this definition of mobility and recurrency.

Dynamic multisensor fusion for mobile robot navigation in an indoor environment

In this study, as the preliminary step for developing a multi-purpose Autonomous robust carrier mobile robot to transport trolleys or heavy goods and serve as robotic nursing assistant in hospital wards. The aim of this paper is to present the use of multi-sensor data fusion such as sonar, CCD camera dn IR sensor for map-building mobile robot to navigate, and presents an experimental mobile robot designed to operate autonomously within both indoor and outdoor environments. Smart sensory systems are crucial for successful autonomous systems. We will give an explanation for the robot system architecture designed and implemented in this study and a short review of existing techniques, since there exist several recent thorough books and review paper on this paper. Instead we will focus on the main results with relevance to the intelligent service robot project at the Centre of Intelligent Design, Automation & Manufacturing (CIDAM). We will conclude by discussing some possible future extensions of the project. It is first dealt with the general principle of the navigation and guidance architecture, then the detailed functions recognizing environments updated, obstacle detection and motion assessment, with the first results form the simulations run.