• Journal of Institute of Control, Robotics and Systems
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• v.10 no.6
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• pp.529-536
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• 2004

Autonomous navigation of an indoor mobile robot using the global ultrasonic system is presented in this paper. Since the trajectory error of the dead-reckoning navigation grows with time and distance, the autonomous navigation of a mobile robot requires to localize the current position of the robot, so that to compensate the trajectory error. The global ultrasonic system consisting of four ultrasonic generators fixed at a priori known positions in the work space and two receivers on the mobile robot has the similar structure with the well-known satellite GPS(Global Positioning System), and it is useful for the self-localization of an indoor mobile robot. The EKF(Extended Kalman Filter) algorithm for the self-localization is proposed and the autonomous navigation based on the self-localization is verified by experiments.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.9
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• pp.875-882
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• 2007

In order for a vehicle to follow a predetermined trajectory accurately, its position must be estimated accurately and reliably. In this thesis, we propose trajectory tracking control methods for unmanned vehicle and a positioning system using ultrasonic wave. The positioning problem is an important part of control problem for unmanned navigation of a vehicle. Dead Reckoning is widely used for positioning of vehicle. However this method has problems because it accumulates estimation errors. We propose a new method to increase the accuracy of position estimation using the Ultrasonic Satellite System (USAT). It is shown that we will be able to estimate the position of vehicle precisely, in which errors are not accumulated. And proposed trajectory tracking control methods include both a new path planning method and a lateral control method for vehicle. The experimental results show that the proposed methods enables exact vehicle trajectory tracking even under various environmental factors.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1807-1808
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• 2008

When it is used for autonomous mobile robots by using dead-reckoning system, odometry system with encorder is the simplest method as well as well-known in the industry. However, odometry system is reflected slide, friction and mechanical errors of wheels when operating the position estimation. And also in order to minimize errors of direction angle which is the most important factor that it is designed the controller in controlling kinematics and quadratic curve, PID that came into the values of sensor fusion with encorder and gyroscope sensor. After designing, the autonomous mobile robot is producted practically and inspected how it works.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.1323-1324
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• 2015

최근, 다수의 이동 로봇으로 구성된 무선 네트워크 기반 군집 로봇 시스템을 제한적인 환경을 벗어나 다양하고 동적인 환경에서 운용하기 위한 연구가 진행 중이다. 특히, 다수 로봇의 위치를 측정하기 위해 실내 환경에 기반 시설 없이 로봇에 장착된 센서들에 의해 위치를 추정하는 방법이 필요하다. 이를 위해, 본 논문에서는 로봇간의 상대적인 정보를 통해 다수 이동 로봇의 거리를 추정하는 방법을 제안한다. 제안한 방법은 먼저, 다수 이동 로봇의 거리를 추정하기 위해 무선 신호를 기반으로 하는 RSSI 방법을 이용하여 다수 이동 로봇의 거리를 추정한다. 그 다음, 추정된 거리와 추측 항법(Dead Reckoning)을 융합하여 이동 중인 로봇의 거리를 추정하는 방법을 제안한다. 마지막으로, 시뮬레이션 도구를 이용하여 응용 가능성을 증명한다.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.10a
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• pp.143-144
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• 2016

본 논문은 보행자의 각 발에 부착된 2개의 IMU(Inertial Measurement Unit) 정보를 융합하여 위치 추적 성능을 향상시키는 보행자 추측 항법 알고리즘을 제안하였다. 센서내의 방향드리프트로 인해 IMU기반 보행자 위치추적은 시간이 지남에 따라 성능이 크게 저하된다. 제안하는 알고리즘은 방향 드리프트로 인해 각 발의 이동경로가 발산하는 점에 착안하여, 보폭이 일정 값을 초과할 시 이를 보정하고 사용자의 위치를 계산한다. 실험을 통해 제안하는 알고리즘이 방향 드리프트를 효과적으로 감소시키는 것을 확인하였다.

• Proceedings of the Korean Information Science Society Conference
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• 2000.04a
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• pp.101-103
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• 2000

본 논문에서는 GPS(Global Positioning System)와 추측 항법 시스템인 DR(Dead Reckoning)을 혼합 구성한 수신보드의 운영을 위한 Embedded 운영체제를 설계하고 구현 하였다. 이 운영체제는 실시간으로 인공위성으로부터 수신되어지는 Raw Measurement 획득, GPS 항법, 자세 결정, 통합항법, 위성 추적을 수행하는 태스크들을 우선순위 기반으로 처리하는 선점형(Preemptive) 스케쥴링 방식을 채택한 실시간 운영체제 이다. 본 논문에서는 자세 결정용 GPS와 DR 센서를 이용한 통합시스템보드를 위한 실시간 운영체제의 개발 환경, 운영체제의 구조와 개발 내용에 대해 언급하였다.

• ETRI Journal
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• v.28 no.2
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• pp.131-144
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• 2006

This work demonstrates a dead-reckoning (DR) scheme for a low-cost land navigation system and a DR/GPS system design using the sigma point Kalman filter (SPKF). T hrough an observability analysis and some simulations, it is shown that the performances of a stand-alone DR system and DR/GPS system can be improved by employing the proposed DR scheme and SPKF. By using the designed DR scheme and filter, the stand-alone DR system does not have any undetectable errors occurring on the curve trajectory. And the DR/GPS system can provide a stable and seamless navigational solution even in the case where the initial heading estimation error is large, such as 160 degrees, or when the GPS signal is unavailable due to tunnels, buildings, and so on. Simulation results indicate a satisfactory performance of the proposed system.

• International Journal of Control, Automation, and Systems
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• v.2 no.4
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• pp.485-493
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• 2004

Open-loop position estimation methods are commonly used in mobile robot applications. Their strength lies in the speed and simplicity with which an estimated position is determined. However, these methods can lead to inaccurate or unreliable estimates. Two position estimation methods are developed in this paper, one using a single optical flow sensor and a second using two optical sensors. The first method can accurately estimate position under ideal conditions and also when wheel slip perpendicular to the axis of the wheel occurs. The second method can accurately estimate position even when wheel slip parallel to the axis of the wheel occurs. Location of the sensors is investigated in order to minimize errors caused by inaccurate sensor readings. Finally, a method is implemented and tested using a potential field based navigation scheme. Estimates of position were found to be as accurate as dead-reckoning in ideal conditions and much more accurate in cases where wheel slip occurs.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2018.11a
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• pp.275-277
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• 2018

선박 및 해상교통관제에 있어서 교통 혼잡구역에 대한 선박교통밀도 예측은 선박충돌사고 예방에 중요하다. 선박 교통밀도 예측정보는 사전에 진입하는 선박들에게 속력조정, 우회항로 이용 등 사전 조치가 가능하다. 본 연구에서는 해상 선박교통상황을 딥러닝 네트워크에 학습한 주의구역 선박교통류 예측 모델(Ship Traffic Extraction Network, STENet)을 제안하여 주의구역의 선박교통류 예측을 수행하고자 한다. STENet 모델 학습을 위해 여수해역 AIS 데이터를 전처리하고, 생성된 입력(해상교통상황)-출력(주의구역 교통밀도) 쌍 데이터를 적용하여 STENet 모델을 학습하였다. 학습된 모델을 이용하여 선박교통류 예측을 한 결과, 중기예측은 표준 절대 오차(mean absolute error)가 0.4-0.5척이 였으며, 장기예측은 0.7-0.8척의 오차로 기존의 Dead Reckoning에 의한 방법보다 50% 이상 교통밀도 예측성능이 향상 되었다.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.833-836
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• 1995

The Position Sensitive Detector(PSD) is an useful which can be used to measurement the position of an incidence light in detail and in real-time. In this paper, light sources, to be predefinded positions, are used as landmarks and the 2-D PSD signals are used to compensate the position of a running mobile robot. To induce the position compensation algorithm, first, we inspect the error factor, make the error model, and evaluate the error covariance matrix between the real position and estimated position in dead reckoning system. Next we obtain an optimal position compensation algorithm to update the estimated position using extended Kalman filler by the relation of the external light position and it's PSD signal. Through the simulation of navigating a robot the effectiveness of the proposed method is confirmed.