• 로봇학회논문지
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• 제5권3호
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• pp.270-277
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• 2010

Various robot platforms have been designed and developed to perform given tasks in a hazardous environment for the purpose of surveillance, reconnaissance, search and rescue, and etc. We have considered a terrain adaptive hybrid robot platform which is equipped with rapid navigation on flat floors and good performance on overcoming stairs or obstacles. Since our special consideration is posed to its flexibility for real application, we devised a design of a transformable robot structure which consists of an ordinary wheeled structure to navigate fast on flat floor and a variable tracked structure to climb stairs effectively. Especially, track arms installed in front side, rear side, and mid side are used for navigation mode transition between flatland navigation and stairs climbing. The mode transition is determined and implemented by adaptive driving mode control of mobile robot. The wheel and track hybrid mobile platform apparatus applied off-road driving mechanism for various professional service robots is verified through experiments for navigation performance in real and test-bed environment.

• 한국군사과학기술학회지
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• 제13권5호
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• pp.894-902
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• 2010

This paper presents a vision-based robust off-road terrain classification method against environmental variation. As a supervised classification algorithm, we applied a neural network classifier using wavelet features extracted from wavelet transform of an image. In order to get over an effect of overall image feature variation, we adopted environment sensors and gathered the training parameters database according to environmental conditions. The robust terrain classification algorithm against environmental variation was implemented by choosing an optimal parameter using environmental information. The proposed algorithm was embedded on a processor board under the VxWorks real-time operating system. The processor board is containing four 1GHz 7448 PowerPC CPUs. In order to implement an optimal software architecture on which a distributed parallel processing is possible, we measured and analyzed the data delivery time between the CPUs. And the performance of the present algorithm was verified, comparing classification results using the real off-road images acquired under various environmental conditions in conformity with applied classifiers and features. Experiments show the robustness of the classification results on any environmental condition.

• 제어로봇시스템학회논문지
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• 제15권4호
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• pp.372-378
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• 2009

For effective mobility control of unmanned ground vehicles in outdoor off-road environments, terrain cover classification technology using passive sensors is vital. This paper presents a novel method far terrain classification based on color and texture information of off-road images. It uses a neural network classifier and wavelet features. We exploit the wavelet mean and energy features extracted from multi-channel wavelet transformed images and also utilize the terrain class spatial coordinates of images to include additional features. By comparing the classification performance according to applied features, the experimental results show that the proposed algorithm has a promising result and potential possibilities for autonomous navigation.

• International Journal of Control, Automation, and Systems
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• 제3권4호
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• pp.509-523
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• 2005

We are developing a novel framework, PRIDE (PRediction In Dynamic Environments), to perform moving object prediction (MOP) for autonomous ground vehicles. The underlying concept is based upon a multi-resolutional, hierarchical approach which incorporates multiple prediction algorithms into a single, unifying framework. The lower levels of the framework utilize estimation-theoretic short-term predictions while the upper levels utilize a probabilistic prediction approach based on situation recognition with an underlying cost model. The estimation-theoretic short-term prediction is via an extended Kalman filter-based algorithm using sensor data to predict the future location of moving objects with an associated confidence measure. The proposed estimation-theoretic approach does not incorporate a priori knowledge such as road networks and traffic signage and assumes uninfluenced constant trajectory and is thus suited for short-term prediction in both on-road and off-road driving. In this article, we analyze the complementary role played by vehicle kinematic models in such short-term prediction of moving objects. In particular, the importance of vehicle process models and their effect on predicting the positions and orientations of moving objects for autonomous ground vehicle navigation are examined. We present results using field data obtained from different autonomous ground vehicles operating in outdoor environments.

• 한국자동차공학회논문집
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• 제15권4호
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• pp.27-32
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• 2007

Head-Up Display system makes it possible for the driver to be informed of important vehicle data such as vehicle speed, engine RPM or navigation data without taking the driver's eyes off the road. Long focal length optics, LCD with bright illumination, image generator and vehicle interface controllers are key parts of head-up display system. All these parts have been designed, developed and applied to the test vehicle. Virtual images are located about 2m ahead of the driver's eye by projecting it onto the windshield just below the driver's line of sight. Developed head-up display system shows satisfactory results for future commercialization.

• 제어로봇시스템학회논문지
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• 제13권12호
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• pp.1222-1229
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• 2007

In this paper, we propose a new obstacle negotiation method for the rescue robot. The rescue robot has a variable geometry single-tracked mechanism, so it can maximize a contact length with ground for the adaptability to off-road and pursue a stable system due to the lower center of gravity. In this research, we add the basis of autonomous navigation, driving mode control based on obstacle detection, to the robot to realize automation of mode transformation. Obstacle detection using PSD(Position Sensitive Device) infrared sensors gives active transformation of the track shape. Finally, experimental results about mentioned are presented.

• 한국군사과학기술학회지
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• 제13권4호
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• pp.683-688
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• 2010

The ability to navigate autonomously in off-road terrain is the most critical technology needed for Unmanned Ground Vehicles(UGV). In this paper, we present a method for vision-based terrain cover classification using DCT features. To classify the terrain, we acquire image from a CCD sensor, then the image is divided into fixed size of blocks. And each block transformed into DCT image then extracts features which reflect frequency band characteristics. Neural network classifier is used to classify the features. The proposed method is validated and verified through many experiments and we compare it with wavelet feature based method. The results show that the proposed method is more efficiently classify the terrain-cover than wavelet feature based one.

• 한국군사과학기술학회지
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• 제12권4호
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• pp.516-523
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• 2009

Recently, the applications of unmanned robots are increasing in various fields including surveillance and reconnaissance, planet exploration and disaster relief. To perform their missions with success, the robots should be able to evaluate terrain's characteristics quantitatively and identify traversable regions to progress toward a goal using mounted sensors. Recently, the authors have proposed techniques that extract terrain information and analyze traversability for off-road navigation of an unmanned robot. In this paper, we examine the use of 3D world models(terrain maps) to classify obstacle and safe terrain for increasing the reliability of the proposed techniques. A world model is divided into several patches and each patch is classified as belonging either to an obstacle or a non-obstacle using three types of metrics. The effectiveness of the proposed method is verified on real terrain maps.

• 대한공간정보학회지
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• 제22권3호
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• pp.79-85
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• 2014

지상무인로봇의 성공적 운용을 위해서는 작전지역에 대한 가동성 분석, 위협도 분석 등의 사전분석 결과를 반영하여 최적의 경로계획을 수행하여야 한다. 그 중에서도 가동성 분석 결과는 차량의 안전 보장과 관련성이 높으며, 특히 비포장로, 초지 등 야지 주행시 차량의 안전에 지대한 영향을 미칠 수 있다. 지형정보는 가동성 분석을 위한 특정 관심영역의 데이터 추출과 비용산출 과정에서 중추적인 역할을 한다. 본 논문에서는 새로운 기반 데이터로서 토지피복도의 활용성을 분석하고, 이를 기반으로 지상무인로봇의 주행을 위한 가동맵 생성방안을 제시한다. 시뮬레이션을 통하여 제안된 토지피복도의 단독 활용방법과 타 지형정보체계와의 혼합 활용방법이 기존 방법과 비교해 가동성 표현과 최적 경로계획에 있어 개선 효과를 보임을 확인한다.

• 한국항행학회논문지
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• 제22권6호
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• pp.602-609
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• 2018

DGPS (differential GPS) 방식의 위치해 계산 방식은 기준국 수신기와 동적 수신기와의 의사거리 보정정보 (PRC; pseudo-range correction) 실시간 통신을 통해서 위치해를 계산하는 방식을 말한다. 실제 동적으로 움직이는 수신기에서는 기준국 수신기와의 통신이 단절되어 PRC 실시간 통신이 단절되는 상황이 발생한다. 논문에서는 DGPS 방식의 위치해 계산방식에서 PRC를 받는 실시간 상황 중간에서수신기에 의사거리보정 정보전송이 끊긴 상황을 가정하여, 수신기에서 기존에 수신했던 PRC 정보를 사용하여 가상의 PRC 모델을 기계학습 알고리즘을 통해 실시간 생성하는 predict DGPS를 제안한다. predict DGPS 방식을 검증하기 위해 고정되어있는 기준국의 수신기에서 실제 PRC와 본 논문에서 제안 한가상의 PRC를 적용하여 위치해를 비교, 분석하였다. 또한 실제 도로에서 PRC 통신이 단절된 시나리오를 가정하여, predict DGPS 방식을 적용한 위치해 계산 방식이 기존 방식의 위치해 계산과 비교하여 향상된 위치해를 보여 줄수 있음을 보였다.