• Proceedings of the KIEE Conference
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• 2000.07d
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• pp.2834-2836
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• 2000

This paper introduces the behavior-based learning controller for mobile robot using topological map. When the mobile robot navigates to the goal position, it utilizes given information of topological map and its location. Under navigating in unknown environment, the robot classifies its situation using ultrasonic sensor data, and calculates each motor schema multiplied by respective gain for all behaviors, and then takes an action according to the vector sum of all the motor schemas. After an action, the information of the robot's location in given topological map is incorporated to the learning module to adapt the weights of the neural network for gain learning. As a result of simulation, the robot navigates to the goal position successfully after iterative gain learning with topological information.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.10
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• pp.965-971
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• 2015

This paper suggests a feature point-based Iterative Closest Point (ICP) algorithm to compensate for the disparity error in building a two-dimensional map. The ICP algorithm is a typical algorithm for matching a common object in two different images. In the process of building a two-dimensional map using the laser scanner data, warping and distortions exist in the map because of the disparity between the two sensor values. The ICP algorithm has been utilized to reduce the disparity error in matching the scanned line data. For this matching process in the conventional ICP algorithm, pre-known reference data are required. Since the proposed algorithm extracts characteristic points from laser-scanned data, reference data are not required for the matching. The laser scanner starts from the right side of the mobile robot and ends at the left side, which causes disparity in the scanned line data. By finding the matching points between two consecutive frame images, the motion vector of the mobile robot can be obtained. Therefore, the disparity error can be minimized by compensating for the motion vector caused by the mobile robot motion. The validity of the proposed algorithm has been verified by comparing the proposed algorithm in terms of map-building accuracy to conventional ICP algorithm real experiments.

• Journal of the Korean Society for Precision Engineering
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• v.25 no.4
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• pp.69-76
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• 2008

The self-organizing feature map(SOFM) among a number of neural network uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are moved toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors of the 1-dimensional string, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the opposite direction of input vector. According to simulation results one can conclude that the method using string and the modified neural network is useful tool to mobile robot for the global path planning.

• The Journal of Korea Robotics Society
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• v.9 no.1
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• pp.1-10
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• 2014

In recent years, the research of 3D mapping technique in urban environments obtained by mobile robots equipped with multiple sensors for recognizing the robot's surroundings is being studied actively. However, the map generated by simple integration of multiple sensors data only gives spatial information to robots. To get a semantic knowledge to help an autonomous mobile robot from the map, the robot has to convert low-level map representations to higher-level ones containing semantic knowledge of a scene. Given a 3D point cloud of an urban scene, this research proposes a method to recognize the objects effectively using 3D graph model for autonomous mobile robots. The proposed method is decomposed into three steps: sequential range data acquisition, normal vector estimation and incremental graph-based segmentation. This method guarantees the both real-time performance and accuracy of recognizing the objects in real urban environments. Also, it can provide plentiful data for classifying the objects. To evaluate a performance of proposed method, computation time and recognition rate of objects are analyzed. Experimental results show that the proposed method has efficiently in understanding the semantic knowledge of an urban environment.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.6
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• pp.585-593
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• 2006

In this paper, we propose a vision-based simultaneous localization and map-building (SLAM) algorithm. SLAM problem asks the location of mobile robot in the unknown environments. Therefore, this problem is one of the most important processes of mobile robots in the outdoor operation. To solve this problem, Extended Kalman filter (EKF) is widely used. However, this filter requires computational power (${\sim}O(N)$, N is the dimension of state vector). To reduce the computational complexity, we applied compressed extended Kalman filter (CEKF) to stereo image sequence. Moreover, because the mobile robots operate in the outdoor environments, we should estimate full d.o.f.s of mobile robot. To evaluate proposed SLAM algorithm, we performed the outdoor experiments. The experiment was performed by using new wheeled type mobile robot, Robhaz-6W. The performance results of CEKF SLAM are presented.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10b
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• pp.491-495
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• 1993

In this paper, we present a methodology to model an indoor environment of a mobile robot using fuzzy numbers and to make a global map of the robot environment using graph theory. We describe any geometric primitive of robot environment as a parameter vector in parameter space and represent the ill-known values of the prameterized geometric primitive by means of fuzzy numbers restricted to appropriate membership functions. Also we describe the spatial relations between geometric prinitives using graph theory for local maps. For making the global map of the mobile robot environment, the correspondence problem between local maps is solved using a fuzzy similarity measure and a Bipartite graph matching technique.

• IEMEK Journal of Embedded Systems and Applications
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• v.3 no.2
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• pp.102-108
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• 2008

This paper describes a mapping and localization based on wave algorithm[11] and Kalman filter for effective SLAM. Each robot in a multi robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robot scans actively seek to verify their relative locations. For simultaneous localization the algorithm which is well known as Kalman Filter (KF) is used. For modelling the robot position we wish to know three parameters (x, y coordinates and its orientation) which can be combined into a vector called a state variable vector. The Kalman Filter is a smart way to integrate measurement data into an estimate by recognizing that measurements are noisy and that sometimes they should ignored or have only a small effect on the state estimate. In addition to an estimate of the state variable vector, the algorithm provides an estimate of the state variable vector uncertainty i.e. how confident the estimate is, given the value for the amount of error in it.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.201-204
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• 2002

Although the reuse of the cached data for scrolling the map reduces the amount of passed data between client and server, it needs the conversions of data coordinates, selective deletion of objects and cache compaction at client. The conversion is time intensive operation due to limited resources of mobile phones such as low computing power, small memory. Therefore, for the efficient map control in the vector map service based mobile phone, it is necessary to study the method for reducing wireless network bandwidth and for overwhelming the limited resources of mobile phone as well. This paper proposes the methods for caching pre-received spatial objects in client-server systems for mobile GIS. We also analyze the strengths and drawbacks between the reuse of cached data and transmission of raw data respectively.

• Journal of KIISE:Computing Practices and Letters
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• v.12 no.1
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• pp.9-20
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• 2006

Web Map Service on mobile environment, provided with cost effective mobile GIS contents and user preferred POIs(Points of interests), is strongly required at this moment when mobile and portable Internet is rapidly spreaded on mobile user community as information technology and mobile HW are evolved in its speed, bandwidth and features. As the mobile and portable Internet becomes popular in mobile services, LBS(Location Based Service) is positioned on a mobile client as one of the best information infra services. Recently mobile GIS (Geographic Information System) comes in service to support LBS, but it is constrained and limited on its system configuration and its presentation methodology of a map, and also it depends on its run time environment. In this paper, we made an effort to design and implement a GIS computing platform with server based thin client for mobile WMS(Web Map Service). For its cost effective Platform which could be easily building up POIs and GIS contents, we're not using commercial GIS solution but NGII's (National Geographic Information Institute's) DXF numerical map, and representing the maps, GIS contents and POIs by SVG(Scalable Vector Graphics) recommended by OGC(OpenGis Consortium). We're also adapting de facto standard of XML web service technology such as WSDL, SOAP to provide real time mobile GIS service on PDA as well as mobile terminal by applying a GPS receiver for a user's location information on mobile environment.

• Spatial Information Research
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• v.13 no.4 s.35
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• pp.355-364
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• 2005

Vector data sets (e.g. maps) are currently major sources of displaying, querying, and identifying locations of spatial features in a variety of applications. Especially in mobile environment, the needs for using spatial data is increasing, and the relative large size of vector maps need to be smaller. Recently, there have been several studies about vector map compression. There was clustering-based compression method with novel encoding/decoding scheme. However, precedent studies did not consider that spatial data have to be updated periodically. This paper explores the problem of existing clustering-based compression method. We propose an adaptive approximation method that is capable of handling data updates as well as reducing error levels. Experimental evaluation showed that when an updated event occurred the proposed adaptive approximation method showed enhanced positional accuracy compared with simple cluster based compression method.