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.

하천은 지역마다 독특한 하도특성을 지니고 있다. 우리나라는 전 국토의 70 %가 산지로 구성되어 있으며, 이 가운데 노년기에 지형도 적지 않다. 이를 가로 지르는 우리나라 하천에서 특이한 하도형태의 모습을 보이는 구간이 곳곳에 산재하고 있다. 그 대표적인 모습이 충적하도와 침식하도가 연속해 발생하는 과정에서 나타나는 하천 협소부이다. 하천 협소부란 하천지형학에서 일반적으로 쓰이고 있는 용어이며, 하도수리학에서는 지배유량에 종속되는 일정하폭의 범위를 크게

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.

최근 컨테이너터미널 간의 경쟁이 심화되면서 생산성 측면뿐 만 아니라 비용경제성 측면에도 관심이 부각되고 있다. 특히, 에너지 소모량 및 장비투입 규모가 큰 트랜스퍼 크레인 부문에 대한 비효율적인 작업요소가 컨테이너터미널 경쟁력 제고에 있어서 걸림돌이 되고 있으며 이에 대한 개선은 인적, 물적 운영비용의 절감과 함께 생산성의 향상에도 긍정적인 영향을 미칠 것으로 기대된다. 따라서 본 연구에서는 현재 국토해양부 주관으로 진행 중인 'RFID를 활용한 RTLS 기반 항만물류효율화 사업'을 통하여 제공 가능한 컨테이너터미널 반 출입 대상 컨테이너의 시간적 가시성을 토대의 트랜스퍼 크레인의 배정 및 이동경로에 대한 클러스터링 기반 최적화모델을 제안하고 시뮬레이션 기법을 통하여 기대효과수준을 확인하였다.

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.

We present the initial results of on-going research for building a novel Mobile Haptic Interface (MHI) that can provide an unlimited haptic workspace in large immersive virtual environments. When a user explores a large virtual environment, the MHI can sense the position and orientation of the user, place itself to an appropriate configuration, and deliver force feedback, thereby enabling a virtually limitless workspace. Our MHI (PoMHI v0.5) features with omnidirectional mobility, a collision-free motion planning algorithm, and force feedback for general environment models. We also provide experimental results that show the fidelity of our mobile haptic interface.

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.