• 로봇학회논문지
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• 제12권1호
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• pp.86-93
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• 2017

The Expanded Guide Circle (EGC) method has been originally proposed as the guidance navigation method for improving the efficiency of the remote operation using the sensory information. The previous algorithm is, however, concerned only for the omni-directional mobile robot, so it needs to suggest a suitable one for a mobile robot with non-holonomic constraints. The ego-kinematic transform is a method to map points of $R^2$ into the ego-kinematic space which implicitly represents non-holonomic constraints for admissible paths. Thus, robots with non-holonomic constraints in the ego-kinematic space can be considered as "free-flying object". In this paper, we propose an effective obstacle avoidance method for mobile robots with non-holonomic constraints by applying EGC method in the ego-kinematic space using the ego-kinematic transformation. This proposed method shows that it works better for non-holonomic mobile robots such as differential-drive robot than the original one. The simulation results show its effectiveness of performance.

• 센서학회지
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• 제19권3호
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• pp.246-257
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• 2010

Autonomous guided robot navigation which consists of following unknown paths and avoiding unknown obstacles has been a fundamental technique for unmanned robots in outdoor environments. The unknown path following requires techniques such as path recognition, path planning, and robot pose estimation. In this paper, we propose a novel sensor fusion system for autonomous guided robot navigation in outdoor environments. The proposed system consists of three monocular cameras and an array of nine infrared range sensors. The two cameras equipped on the robot's right and left sides are used to recognize unknown paths and estimate relative robot pose on these paths through bayesian sensor fusion method, and the other camera equipped at the front of the robot is used to recognize abrupt curves and unknown obstacles. The infrared range sensor array is used to improve the robustness of obstacle avoidance. The forward camera and the infrared range sensor array are fused through rule-based method for obstacle avoidance. Experiments in outdoor environments show the mobile robot with the proposed sensor fusion system performed successfully real-time autonomous guided navigation.

• 항공우주시스템공학회지
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• 제14권2호
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• pp.1-11
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• 2020

Detecting obstacles and generating a suitable path to avoid obstacles in real time is a prime mission requirement for UAVs. In areas, close to buildings and people, detecting obstacles in the path and estimating its own position (egomotion) in GPS degraded/denied environments are usually addressed with vision-based Simultaneous Localization and Mapping (SLAM) techniques. This presents possibilities and challenges for the feasible path generation with constraints of vehicle dynamics in the configuration space. In this paper, a near real-time feasible path is shown to be generated in the ORB-SLAM framework using a chain-based path planning approach in a force field with dynamic constraints on path length and minimum turn radius. The chain-based path plan approach generates a set of nodes which moves in a force field that permits modifications of path rapidly in real time as the reward function changes. This is different from the usual approach of generating potentials in the entire search space around UAV, instead a set of connected waypoints in a simulated chain. The popular ORB-SLAM, suited for real time approach is used for building the map of the environment and UAV position and the UAV path is then generated continuously in the shortest time to navigate to the goal position. The principal contribution are (a) Chain-based path planning approach with built in obstacle avoidance in conjunction with ORB-SLAM for the first time, (b) Generation of path with minimum overheads and (c) Implementation in near real time.

• 대한임베디드공학회논문지
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• 제9권4호
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• pp.237-243
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• 2014

In this paper we present an real-time obstacle avoidance technique of autonomous mobile robot with steering system and implementation of user interface for mobile devices with Android platform. The direction of autonomous robot is determined by virtual force field concept, which is based on the distance information acquired from 5 ultrasonic sensors. It is converted to virtual repulsive force around the autonomous robot which is inversely proportional to the distance. The steering system with PD(proportional and derivative) controller moves the mobile robot to the determined target direction. We also use PSD(position sensitive detector) sensors to supplement ultrasonic sensors around dead angle area. The mobile robot communicates with Android mobile device and PC via Ethernet. The video information from CMOS camera mounted on the mobile robot is transmitted to Android mobile device and PC. And the user can control the mobile robot manually by transmitting commands on the user interface to it via Ethernet.

• 한국건설관리학회논문집
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• 제5권5호
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• pp.151-162
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• 2004

Current methods for construction site modeling employ large, expensive laser range scanners that produce dense range point clouds of a scene from different perspectives. Days of skilled interpretation and of automatic segmentation may be required to convert the clouds to a finished CAD model. The dynamic nature of the construction environment requires that a real-time local area modeling system be capable of handling a rapidly changing and uncertain work environment. However, in practice, large, simple, and reasonably accurate embodying volumes are adequate feedback to an operator who, for instance, is attempting to place materials in the midst of obstacles with an occluded view. For real-time obstacle avoidance and automated equipment control functions, such volumes also facilitate computational tractability. In this research, a human operator's ability to quickly evaluate and associate objects in a scene is exploited. The operator directs a laser range finder mounted on a pan and tilt unit to collect range points on objects throughout the workspace. These groups of points form sparse range point clouds. These sparse clouds are then used to create geometric primitives for visualization and modeling purposes. Experimental results indicate that these models can be created rapidly and with sufficient accuracy for automated obstacle avoidance and equipment control functions.

• 제어로봇시스템학회논문지
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• 제13권9호
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• pp.883-895
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• 2007

In this work, we present the development of a patrol robot which is intended to navigate outdoor rough terrain. Proposed mechanism consists of six legs for overcoming an obstacle, and six wheels for traveling. Also, in order to absorb vibration in rough terrain effectively, the slide-spring system and tubed type tire are adopted to each leg and each wheel. The control system of robot consists of several imbedded boards for management of lots of diverse devices such as sensors designed for rough terrain, motor controllers, camera, micro controller and so on. And the base system of the robot is designed to operate in real time and to surveille in the vicinity of the robot, and the robot system is controlled by wireless LAN connected to GUI-based remote control system, while CAN communication connects the control board and the device controllers for sensors and motor controllers. For operating this robot system efficiently, we propose the control algorithms for autonomous navigation using GPS, stabilization maintenance by posture control, obstacle-avoidance by impedance control, and obstacle-overcoming with interference-avoidance between wheels. The performance of the robot and the proposed algorithms are tested and proved by a set of experiments in outdoor rough terrain.

• 한국지능시스템학회논문지
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• 제19권4호
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• pp.470-477
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• 2009

자기 자신의 형태를 변형하거나 물리적인 결합으로 재구성하여 새로운 환경에 적응하는 모듈형 자가 결합 로봇은 많은 연구가 필요한 분야이다. 본 논문에서는 물리적으로 결합 가능한 모듈형 로봇을 위한 영상기반의 자가 결합 제어기를 제안한다. 먼저 실시간 영상처리가 가능한 모듈형 로봇 플랫폼을 설계하고, 컬러기반 물체 인식 방법을 구현하였다. 모듈형 로봇은 자가 결합을 위해 결합될 로봇 근처의 부목표점까지 장애물들을 회피하면서 주행해 가야 한다. 본 논문에서는 부 목표점의 추적을 위하여 영상처리를 통해 얻은 거리와 방향각 정보들을 사용한 퍼지 주행 제어기와 장애물 회피를 위한 퍼지 제어기를 제안하고, 제안된 퍼지 제어기들과 로봇의 절대 거리 및 방향각 정보를 사용하여 모듈형 로봇을 위한 자가 결합제어기를 구현하였다. 실제 제작된 두 대의 모듈형 로봇을 사용하여 다양한 환경에서 로봇간 거리와 방향각이 다른 상황에서 실험을 수행하여 제안된 자가 결합 제어 방법의 성능을 검증하였다.

• 한국생산제조학회지
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• 제7권4호
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• pp.28-34
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• 1998

This paper presents a new method for local obstacle avoidance of indoor mobile robots. The method combines a directional approach called the lane method and a velocity space approach. The lane method divides working area into lanes and then chooses the best lane to follow for efficient and collision-free movement. Then, the heading direction to enter and follow the best lane is decided, and translational and rotational velocity considering physical limitations of a mobile robot are determined. Since this method combines both the directional and velocity space method, it shows collision-free motion as well as smooth motion taking the dynamic of the robot into account.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1254-1257
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• 1993

Presented in this paper is a newly developed motion planning method of an autonomous mobile robot(MAR) which can be applied to flexible manufacturing systems(FMS). The mobile robot is designed for transporting tools and workpieces between a set-up station and machines according to production schedules of the whole FMS. The proposed method is implemented based on an earlier developed real-time obstacle avoidance method which employs Kohonen network for pattern classification of sonar readings and fuzzy logic for local path planning. Particulary, a novel obstacle avoidance method for moving objects using a collision index, collision possibility measure, is described. Our method has been tested on the SNU mobile robot. The experimental results show that the robot successfully navigates to its target while avoiding moving objects.

• 대한전기학회논문지:시스템및제어부문D
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• 제52권8호
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• pp.471-481
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• 2003

The acts of biological immune system are similar to the navigation for autonomous mobile robots under dynamically changing environments. In recent years, many researchers have studied navigation algorithms using artificial immune networks. Conventional artificial immune algorithms consist of an obstacle-avoidance behavior and a goal-reaching behavior. To select a proper action, the navigation algorithm should combine the obstacle-avoidance behavior with the goal-reaching behavior. In this paper, the neural network is employed to combine the behaviors. The neural network is trained with the surrounding information. the outputs of the neural network are proper combinational weights of the behaviors in real-time. Also, a velocity control algorithm is constructed with the artificial immune network. Through a simulation study and experimental results for a autonomous mobile robot, we have shown the validity of the proposed navigation algorithm.