• Journal of Computing Science and Engineering
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• v.2 no.4
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• pp.321-338
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• 2008

This paper presents a Modified Multiple Depth First Search algorithm for the exploration of the indoor environments occupied with obstacles in random distribution. The proposed algorithm was designed and implemented to employ one or a team of Khepera II mini robots for the exploration process. In case of multi-robots, the BlueCore2 External Bluetooth module was used to establish wireless networks with one master robot and one up to three slaves. Messages are sent and received via the module's Universal Asynchronous Receiver/Transmitter (UART) interface. Real exploration experiments were performed using locally developed teleworkbench with various autonomy features. In addition, computer simulation tool was also developed to simulate the exploration experiments with one master robot and one up to ten slaves. Computer simulations were in good agreement with the real experiments for the considered cases of one to one up to three networks. Results of the MMDFS for single robot exhibited 46% reduction in the needed number of steps for exploring environments with obstacles in comparison with other algorithms, namely the Ants algorithm and the original MDFS algorithm. This reduction reaches 71% whenever exploring open areas. Finally, results performed using multi-robots exhibited more reduction in the needed number of exploration steps.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.1
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• pp.66-71
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• 2006

Recently, there are lots of concerning on ubiquitous environment of robots and URC (Ubiquitous Robotic Companion). In this paper, a practical ubiquitous interface system far controlling mobile robots in indoor environments was proposed. The interface system was designed as a manager-agent model including a PC manager, a mobile manager, and robot agents for being able to be accessed by any network. In the system, the PC manager has a 3D virtual environment and shows real images for a human-friendly interface, and share the computation load of the robot such as path planning and managing geographical information. It also contains Hybrid Format Manager(HFM) working for transforming the image, position, and control data and interchanging them between the robots and the managers. Mobile manager working in the minimized computing condition of handsets has a mobile interface environment displaying the real images and the position of the robot and being able to control the robots by pressing keys. Experimental results showed the proposed system was able to control robots rising wired and wireless LAN and mobile Internet.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.4
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• pp.361-368
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• 2015

This paper reports an unscented Kalman filter approach for localization of a mobile robot in an indoor environment. The method proposes a new model of measurement uncertainty which adjusts the error covariance according to the measured distance. The method also uses non-zero off diagonal values in error covariance matrices of motion uncertainty and measurement uncertainty. The method is tested through experiments in an indoor environment of 100*40 m working space using a differential drive robot which uses Laser range finder as an exteroceptive sensor. The results compare the localization performance of the proposed method with the conventional method which doesn't use adaptive measurement uncertainty model. Also, the experiment verifies the improvement due to non-zero off diagonal elements in covariance matrices. This paper contributes to implementing and evaluating a practical UKF approach for mobile robot localization.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.14 no.12
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• pp.6420-6426
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• 2013

The indoor areas for the commercial use of automatic monitoring systems of mobile robot localization improves the cognitive abilities and the needs of the environment with this emerging and existing mobile robot localization, and object recognition methods commonly around its great sensor are leveraged. On the other hand, there is a difficulty with a problem-solving self-location estimation in indoor mobile robots using only the sensors of the robot. Therefore, in this paper, a self-position estimation method for an enhanced and effective mobile robot is proposed using a marker and CCTV video that is already installed in the building. In particular, after recognizing a square mobile robot and the object from the input image, and the vertices were confirmed, the feature points of the marker were found, and marker recognition was then performed. First, a self-position estimation of the mobile robot was performed according to the relationship of the image marker and a coordinate transformation was performed. In particular, the estimation was converted to an absolute coordinate value based on CCTV information, such as robots and obstacles. The study results can be used to make a convenient self-position estimation of the robot in the indoor areas to verify the self-position estimation method of the mobile robot. In addition, experimental operation was performed based on the actual robot system.

• The Journal of Korea Robotics Society
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• v.3 no.3
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• pp.194-202
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• 2008

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.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.11
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• pp.937-942
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• 2003

We present a self-localization method for mobile robots using vertical line features of indoor environment. When a 2D map including feature points and color information is given, a mobile robot moves to the destination, and acquires images from the surroundings having vertical line edges by one camera. From the image, vertical line edges are detected, and pattern vectors meaning averaged color values of the left and right regions of the each line are computed by using the properties of the line and a region growing method. The pattern vectors are matched with the feature points of the map by comparing the color information and the geometrical relationship. From the perspective transformation and rigid transformation of the corresponded points, nonlinear equations are derived. Localization is carried out from solving the equations by using Newton's method. Experimental results show that the proposed method using mono view is simple and applicable to indoor environment.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.7
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• pp.651-658
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• 2011

An efficient modified $D^*$ lite algorithm is suggested, which can perform online path planning for mobile robots in dynamic indoor environment. Online path planning should plan and execute alternately in a short time, and hence it enables the robot avoid unknown dynamic obstacles which suddenly appear on robot's path. Based on $D^*$ Lite algorithm, we improved representation of edge cost, heuristic function, and priority queue management, to build a modified $D^*$ Lite algorithm. Performance of the proposed algorithm is revealed via extensive simulation study.

• The Magazine of the IEIE
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• v.23 no.12
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• pp.115-125
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• 1996

This paper presents a new algorithm for the self-localization of a mobile robot using one degree perspective Invariant(Cross Ratio). Most of conventional model-based self-localization methods have some problems that data structure building, map updating and matching processes are very complex. Use of a simple cross ratio can be effective to the above problems. The algorithm is based on two basic assumptions that the ground plane is flat and two locally parallel sloe-lines are available. Also it is assumed that an environmental map is available for matching between the scene and the model. To extract an accurate steering angle for a mobile robot, we take advantage of geometric features such as vanishing points. Feature points for cross ratio are extracted robustly using a vanishing point and intersection points between two locally parallel side-lines and vertical lines. Also the local position estimation problem has been treated when feature points exist less than 4points in the viewed scene. The robustness and feasibility of our algorithms have been demonstrated through real world experiments In Indoor environments using an indoor mobile robot, KASIRI-II(KAist Simple Roving Intelligence).

• Journal of the Korean Society for Precision Engineering
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• v.26 no.8
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• pp.89-95
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• 2009

In this paper, a navigation algorithm is proposed using RFID tags in indoor environments. Firstly, a stochastic sensor model of RFID is derived and the design factors including the maximum identifiable distance, the identification direction and the read success rate are obtained through experiments. The obstacle avoidance algorithm is developed with consideration of those factors for a variety of RFID antenna configurations and different indoor environments. The algorithm is tested by computer simulations and implemented on a mobile robot.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.1-8
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• 2002

It has been integrated into several navigation systems. This paper shows that system recognizes difficult indoor roads without any specific marks such as painted guide line or tape. In this method the robot navigates with visual sensors, which uses visual information to navigate itself along the read. The Neural Network System was used to learn driving pattern and decide where to move. In this paper, I will present a vision-based process for AMR(Autonomous Mobile Robot) that is able to navigate on the indoor read with simple computation. We used a single USB-type web camera to construct smaller and cheaper navigation system instead of expensive CCD camera.