전파를 이용한 실내 위치인식 기술은 현재 다양한 환경에서 연구되고 있다. 그 중 철골구조로 이루어진 선박은 전파의 반사에 의해 수신율은 높지만 레인징 오차가 크게 발생한다. 이러한 환경에서 발생하는 위치측정 오차를 줄이기 위하여 본 연구에서는 IEEE 802.15.4a의 CSS 기반으로 변형 이변측위와 초전센서를 이용한 선내 위치인식보정 알고리즘을 제안한다. 제안한 시스템은 선내 복도와 같은 좁은 통로에서 CSS의 특성분석을 통하여 이동노드와 고정노드 사이의 적합한 수신거리를 추정하여 고정노드의 수를 줄이고 또한 전파의 반사와 회절에 의한 레인징 오차가 크게 변동하는 코너영역에서 제안한 변형 이변측위기법과 초전센서를 이용하여 이동구간을 추적하여 위치를 인식하였다. 실험결과 제안한 알고리즘이 일반적인 방법 대비 86.2 %의 선내 위치인식 정확도와 효율이 향상됨을 확인하였다.

도로기하구조정보는 도로의 안전성평가 및 도로의 유지관리를 위한 필수적인 요소이다. 본 연구에서는 GPS(Global Positioning System)/INS(Inertial Navigation System)센서가 탑재된 조사차량을 이용하여 기하구조정보를 수집하였으며, 수집된 차량의 자세정보 중 평면선형과 관련된 Roll, Heading 자료를 이용하여 직선, 원곡선, 완화곡선을 구분하는 알고리즘을 개발하였다. 본 연구에서는 평면선형 인식 이전에 전처리 과정으로 이동평균법을 통하여 자료를 평활화함으로써 원시자료의 이상치를 제거하여 평면선형 인식의 신뢰성을 제고하였다. 유전알고리즘(GA, Genetic Algorithm)을 이용하여 분류정확도(CCR, Correct Classification Rate)를 최대로 하는 알고리즘 파라미터를 설정한 결과 100%의 분류정확도를 보였다. 설정된 파라미터를 이용하여 고속도로와 국도 주행자료를 이용하여 알고리즘을 평가한 결과 90.48%와 88.24%의 분류정확도를 보여, 제안된 평면선형인식 알고리즘은 현장에서 적용 시 높은 신뢰도를 가지는 정보를 제공 가능한 것으로 분석되었다. 본 연구에서 개발한 평면선형인식 알고리즘은 조사차량에 GPS/INS센서의 소프트웨어로 탑재되어 도로 및 교통기술자에게 도로기하구조정보를 보다 용이하게 수집하고 분석할 수 있는 환경을 제공하는데 기여할 것으로 기대된다.

The purpose of our sensor system is to transparentize the large hydraulic manipulators of a six-ton dual arm excavator from the operator camera view. Almost 40% of the camera view is blocked by the manipulators. In other words, the operator loses 40% of visual information which might be useful for many manipulator control scenarios such as clearing debris on a disaster site. The proposed method is based on a 3D reconstruction technology. By overlaying the camera image from front top of the cabin with the point cloud data from RGB-D (red, green, blue and depth) cameras placed at the outer side of each manipulator, the manipulator-free camera image can be obtained. Two additional algorithms are proposed to further enhance the productivity of dual arm excavators. First, a color correction algorithm is proposed to cope with the different color distribution of the RGB and RGB-D sensors used on the system. Also, the edge overlay algorithm is proposed. Although the manipulators often limit the operator’s view, the visual feedback of the manipulator’s configurations or states may be useful to the operator. Thus, the overlay algorithm is proposed to show the edge of the manipulators on the camera image. The experimental results show that the proposed transparentization algorithm helps the operator get information about the environment and objects around the excavator.

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 an algorithm that estimates the location of lunar rover using IMU and vision system instead of the dead-reckoning method using IMU and encoder, which is difficult to estimate the exact distance due to the accumulated error and slip. First, in the lunar environment, magnetic fields are not uniform, unlike the Earth, so only acceleration and gyro sensor data were used for the localization. These data were applied to extended kalman filter to estimate Roll, Pitch, Yaw Euler angles of the exploration rover. Also, the lunar module has special color which can not be seen in the lunar environment. Therefore, the lunar module were correctly recognized by applying the HSV color filter to the stereo image taken by lunar rover. Then, the distance between the exploration rover and the lunar module was estimated through SIFT feature point matching algorithm and geometry. Finally, the estimated Euler angles and distances were used to estimate the current position of the rover from the lunar module. The performance of the proposed algorithm was been compared to the conventional algorithm to show the superiority of the proposed algorithm.

This paper presents a vision-based relative pose estimation algorithm and its validation through both numerical and hardware experiments. The algorithm and the hardware system were simultaneously designed considering actual experimental conditions. Two estimation techniques were utilized to estimate relative pose; one was a nonlinear least square method for initial estimation, and the other was an extended Kalman Filter for subsequent on-line estimation. A measurement model of the vision sensor and equations of motion including nonlinear perturbations were utilized in the estimation process. Numerical simulations were performed and analyzed for both the autonomous docking and formation flying scenarios. A configuration of LED-based beacons was designed to avoid measurement singularity, and its structural information was implemented in the estimation algorithm. The proposed algorithm was verified again in the experimental environment by using the Autonomous Spacecraft Test Environment for Rendezvous In proXimity (ASTERIX) facility. Additionally, a laser distance meter was added to the estimation algorithm to improve the relative position estimation accuracy. Throughout this study, the performance required for autonomous docking could be presented by confirming the change in estimation accuracy with respect to the level of measurement error. In addition, hardware experiments confirmed the effectiveness of the suggested algorithm and its applicability to actual tasks in the real world.

This paper describes efficient flight control algorithms for building a reconfigurable ad-hoc wireless sensor networks between nodes on the ground and airborne nodes mounted on autonomous vehicles to increase the operational range of an aerial robot or the communication connectivity. Two autonomous flight control algorithms based on adaptive gradient climbing approach are developed to steer the aerial vehicles to reach optimal locations for the maximum communication throughputs in the airborne sensor networks. The first autonomous vehicle control algorithm is presented for seeking the source of a scalar signal by directly using the extremum-seeking based forward surge control approach with no position information of the aerial vehicle. The second flight control algorithm is developed with the angular rate command by integrating an adaptive gradient climbing technique which uses an on-line gradient estimator to identify the derivative of a performance cost function. They incorporate the network performance into the feedback path to mitigate interference and noise. A communication propagation model is used to predict the link quality of the communication connectivity between distributed nodes. Simulation study is conducted to evaluate the effectiveness of the proposed reconfigurable airborne wireless networking control algorithms.