• 로봇학회논문지
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• 제3권3호
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• pp.212-218
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• 2008

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.

• 한국전자파학회논문지
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• 제25권1호
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• pp.99-107
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• 2014

돌발적으로 발생하는 도로 장애물은 고속으로 주행하는 차량의 안전운행을 위협한다. 따라서, 악천후 시에도 도로상의 돌발 장애물을 탐지하고, 충돌을 방지할 수 있는 전천후 레이다 센서에 대한 관심이 높아지고 있다. 본 논문에서는 도로 장애물 탐지 및 경보를 위한 Ka-대역 펄스 도플러 레이다 시험모델의 설계 제작 및 성능시험 결과를 제시한다. 레이다 센서는 도로 환경조건에 적합하고, 다양한 고정 및 이동 장애물에 대하여 탐지, 추적 및 차선 식별 기능이 요구된다. 개발된 레이다는 안테나, 송수신기, 신호처리기 및 제어기로 구성된다. 주요 적용 기술은 고정 장애물 탐지를 위한 클러터 맵 기반 변화 탐지 기법, 이동 장애물 속도 탐지를 위한 도플러 추정 기법, 그리고 차선 식별을 위한 3-horn 모노펄스 방위각 추정 및 추적 기법이 포함된다. 개발된 레이다 시스템의 장애물 탐지와 차량 감시 성능은 성능시험과 환경시험을 통하여 설계 요구 조건을 만족하는 것으로 확인되었다.

• 디지털융복합연구
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• 제11권11호
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• pp.321-326
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• 2013

GPS는 실내에서 수신률이 떨어지는 특성을 가지고 있다. 이러한 단점을 극복하기 위해 AP의 RSS를 이용한 측위기술에 대한 연구와 개발이 이루어지고 있다. 측위기술과 더불어 신호 세기만을 가지고 장애물까지 판단할 수 있다면 활용도와 효율성 측면에서 이를 응용한 서비스를 하는 입장에서도 별다른 구축비용이 들지 않는다는 장점이 있다. 본 논문에서는 RSS(Received Signal Strength)를 이용하여 장애물을 판단하는 방법을 제시한다.

• 제어로봇시스템학회논문지
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• 제5권2호
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• pp.228-236
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• 1999

In this paper, we propose visual-based self-localization and obstacle detection algorithms for indoor mobile robots. The algorithms do not require calibration, and can be worked with only single image by using the projective invariant relationship between natural landmarks. We predefine a risk zone without obstacles for a robot, and update the image of the risk zone, which will be used to detect obstacles inside the zone by comparing the averaging image with the current image of a new risk zone. The positions of the robot and the obstacles are determined by relative positioning. The method does not require the prior information for positioning robot. The robustness and feasibility of our algorithms have been demonstrated through experiments in hallway environments.

• 로봇학회논문지
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• 제14권3호
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• pp.170-178
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• 2019

This paper proposes a parking space detection method for autonomous parking by using the Around View Monitor (AVM) image and Light Detection and Ranging (LIDAR) sensor fusion. This method consists of removing obstacles except for the parking line, detecting the parking line, and template matching method to detect the parking space location information in the parking lot. In order to remove the obstacles, we correct and converge LIDAR information considering the distortion phenomenon in AVM image. Based on the assumption that the obstacles are removed, the line filter that reflects the thickness of the parking line and the improved radon transformation are applied to detect the parking line clearly. The parking space location information is detected by applying template matching with the modified parking space template and the detected parking lines are used to return location information of parking space. Finally, we propose a novel parking space detection system that returns relative distance and relative angle from the current vehicle to the parking space.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.871-875
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• 2003

To detect and avoid obstacles is one of the important tasks of mobile navigation. In a real environment, when a mobile robot encounters dynamic obstacles, it is required to simultaneously detect and avoid obstacles for its body safely. In previous vision system, mobile robot has used it as either a passive sensor or an active sensor. This paper proposes a new obstacle detection algorithm that uses a stereo camera as both a passive sensor and an active sensor. Our system estimates the distances from obstacles by both passive-correspondence and active-correspondence using laser slit. The system operates in three steps. First, a far-off obstacle is detected by the disparity from stereo correspondence. Next, a close obstacle is acquired from laser slit beam projected in the same stereo image. Finally, we implement obstacle avoidance algorithm, adopting the modified Dynamic Window Approach (DWA), by using the acquired the obstacle's distance.

• 대한전자공학회논문지SP
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• 제37권3호
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• pp.57-69
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• 2000

본 논문에서는 주행차량에 장착된 CCD 카메라를 통하여 획득되어진 영상으로부터 moving window를 사용하여 차선을 인식하고 장애물을 감지하는 방법을 제안한다 입력되는 동영상을 실시간에 처리하기 위해서는 하드웨어적으로 상당히 많은 제약을 초래한다. 이러한 문제점을 극복하고 영상을 사용하여 실시간에 차선 인식 및 장애물을 감지하기 위해, 도로조건과 차량상태에 바탕을 둔 최적의 window 크기를 결정하고 그 window 영상만을 처리하여 차선 인식 및 장애물 감지를 실시간에 가능하게 하는 기법을 제안한다 영상의 각 프레임에 대하여 moving window는 칼만필터에 의해 정확성이 향상된 예측방향으로 옮겨진다. 제안된 알고리즘의 효용성을 고속도로 주행영상을 사용한 실험을 통해 보여준다

• ETRI Journal
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• 제45권2호
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• pp.329-337
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• 2023

For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.

• 대한기계학회논문집A
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• 제21권10호
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• pp.1551-1560
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• 1997

This study is devoted to the detection of the 3-dimensional point obstacles on the plane by using accumulated scan line images. The proposed accumulating only one scan line allow to process image at real time. And the change of motion of the feature in image is small because of the short time between image frames, so it does not take much time to track features. To obtain recursive optimal obstacles position and robot motion along to the motion of camera, Kalman filter algorithm is used. After using Kalman filter in case of the fixed environment, 3-dimensional obstacles point map is obtained. The position and motion of moving obstacles can also be obtained by pre-segmentation. Finally, to solve the stereo ambiguity problem from multiple matches, the camera motion is actively used to discard mis-matched features. To get relative distance of obstacles from camera, parallel stereo camera setup is used. In order to evaluate the proposed algorithm, experiments are carried out by a small test vehicle.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2009년도 정보 및 제어 심포지움 논문집
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• pp.372-374
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• 2009

In this research, an obstacle avoidance method is proposed. The common usage of a robot is indoor and the obstacles to the indoor robot is studied. The accurate detection of direction after overcoming the obstacles is necessary for performance of autonomous navigation and mission project. The sensors such as Laser, Ultrasound, PSD can be used to measure the obstacles. In this research, a PSD sensor is used to detect obstacles. It detects the height and width of obstacles located on the floor. Before measuring the obstacles, a calibration of the sensor was done and it produced a better accuracy. We have plotted an error graph using data obtained from the repeated experiments. The graph is fitted to a polynomial curve. The polynomial equation is used for the robot navigation. And in this research, a model of the error of the direction of the robot after overcoming obstacles was obtained also. The prototype of the obstacle and the error of the direction after overcoming the obstacles are modelled using a neural networks. The input of the neural network composed with the height of the obstacles, the speed of robot, the direction of wheels and the error of the direction. To implement the suggested algorithm, we set up a robot which is operated by a notebook computer. Experiment showed the suggested algorithm performed well.