PURPOSES : This study verifies the appropriateness of the observed traffic volume using car navigation traffic volume data. METHODS : In this study, we developed an annual average daily traffic (AADT) estimation model that can verify the total amount of traffic by using navigation traffic volume data. In addition, a method to verify the appropriateness of the observed traffic volume was developed using time-based navigation traffic volume data that can check the characteristics of traffic volume at each point. RESULTS : As a result of the analysis of this study, it was found that 674 of the 697 short-duration survey spots of the freeways were appropriate and that 23 spots needed to be revised. CONCLUSIONS : As a result of the analysis of this study, it was found that there was a strong positive correlation between the observed traffic volume and the car navigation traffic volume. Thus, the appropriateness of the observed traffic was determined by verifying the total amount of observed traffic and the observed traffic volume by time.

In order to determine home delivery service routes, conditions specified for each parcel such as customer-assigned delivery times and parcel contents should be considered, so the conventional route search algorithms have some limits to be applied for home delivery services. In this study, a multi-purpose route searching algorithm is suggested in order to deal with every requirement of deliveries that vary in delivery distances, contents and appointed times. A simulation study to verify the performances of the system with example data of Seoul and Kyungki provinces shows that it significantly improves the customer satisfaction and the productivity of delivery businesses.

In this paper, we propose a modified ORB-SLAM (Oriented FAST and Rotated BRIEF Simultaneous Localization And Mapping) for precise indoor navigation of a mobile robot. The exact posture and position estimation by the ORB-SLAM is not possible all the times for the indoor navigation of a mobile robot when there are not enough features in the environment. To overcome this shortcoming, additional IMU (Inertial Measurement Unit) and encoder sensors were installed and utilized to calibrate the ORB-SLAM. By fusing the global information acquired by the SLAM and the dynamic local location information of the IMU and the encoder sensors, the mobile robot can be obtained the precise navigation information in the indoor environment with few feature points. The superiority of the modified ORB-SLAM was verified to compared with the conventional algorithm by the real experiments of a mobile robot navigation in a corridor environment.

This paper describes an alignment algorithm that estimates the initial heading angle of AUVs (Autonomous Underwater Vehicle) for starting navigation in a sea area. In the basic dead reckoning system, the initial orientation of the vehicle is very important. In particular, the initial heading value is an essential factor in determining the performance of the entire navigation system. However, the heading angle of AUVs cannot be measured accurately because the DCS (Digital Compass) corrupted by surrounding magnetic field in pointing true north direction of the absolute global coordinate system (not the same to magnetic north direction). Therefore, we constructed an experimental constraint and designed an algorithm based on extended Kalman filter using only inertial navigation sensors and a GPS (Global Positioning System) receiver basically. The value of sensor covariance was selected by comparing the navigation results with the reference data. The proposed filter estimates the initial heading angle of AUVs for navigation in a sea area and reflects sampling characteristics of each sensor. Finally, we verify the performance of the filter through experiments.

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

최근 항공기, 자동차, 선박을 포함하여 다양한 분야에서 무인시스템에 관한 연구 개발이 이루어지고 있다. 우리나라에서도 IT 기술의 발달과 함께 무인시스템에 관한 연구가 활발히 진행되고 있지만 아직 개발 실적은 미미한 수준이다. 이 연구에서는 바지(barge)선형의 초소형자율 무인선박(USV)을 개발하고자 하였다. 자율항법 알고리즘 개발에 GPS센서의 위치 정보를 기반으로 대권항법 계산식을 적용하였으며, 프로그래밍은 NI사의 LabVIEW 8.2를 이용하였다. 조타제어는 펄스진폭변조 방식으로 하였다. 또한, 엔진시스템은 전동모터 및 전자 변속기로 구성하였고, 엔진시스템 냉각방식으로 DC모터펌프를 이용한 해수 직접냉각방식을 채용하였다. 무인선박을 자체 설계 제작하고, 해상실험을 통해 자율운항 알고리즘의 유효성을 검증하였다.

In this paper, an INS compensation algorithm is proposed using the accelerometer from IMU. First, we denote the basic INS algorithm and show that how to compensate the position error when low cost IMU is used. Second, considering the ship's characteristic and ocean environments, we consider with a drift as a periodic external environment change which is affected with exact position. To develop the compensation algorithm, we use a repetitive method to reduce the external environment changes. Lastly, we verify the proposed algorithm through the experiments, where the acceleration sensor is used to acquire real data.

In this paper, a tracking algorithm for autonomous navigation of automated guided vehicles (AGVs) operating in container terminals is presented. The developed navigation algorithm takes the form of a federated information filter used to detect other AGVs and avoid obstacles using fused information from multiple sensors. Being equivalent to the Kalman filter (KF) algebraically, the information filter is extended to N-sensor distributed dynamic systems. In multi-sensor environments, the information-based filter is easier to decentralize, initialize, and fuse than a KF-based filter. It is proved that the information state and the information matrix of the suggested filter, which are weighted in terms of an information sharing factor, are equal to those of a centralized information filter under the regular conditions. Numerical examples using Monte Carlo simulation are provided to compare the centralized information filter and the proposed one.

In this paper, the equations calculating GDOP are induced in Hyperboic, and Spherical Navigation System, respectively, The GDOP diagram shows that the DGOP in the inner region of Beacons is similar each other, but the GDOP of Hyperboic Navigation System is much larger than that of Spherical Navigation System due to GDOP in the outer region of Beacons. The authors propose the algorithm estimating the pulse starting time using the Hyperboic Navigation System, and prove that if Navigation use the Spherical Navigation System by adopting the proposed Algorithm -in this case, "Pseudo Sperical Navigation System" - in the outer region where GDOP is becoming large, the position errors can be reduced.e reduced.