• Journal of Positioning, Navigation, and Timing
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• v.9 no.1
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• pp.33-42
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• 2020

In Integration Approach (IA)-based Pedestrian Dead Reckoning (PDR), it is important to detect the exact zero-velocity of the foot with an Inertial Measurement Unit (IMU). By detecting zero-velocity during the stance phase of the foot touching the ground and executing Zero-velocity UPdaTe (ZUPT) at the exact time, stable navigation information can be provided by the PDR. When the pace is fast, however, it is not easy to accurately detect the zero-velocity because of the small stance phase interval and the large signal variance of the corresponding interval. Incorrect zero-velcity detection greatly causes navigation errors of IA-based PDR. In this paper, we propose a method to detect the zero-velocity stably even at high speed by novel buffering of IMU's output data and signal processing of the buffer. And we design a PDR based on this. By analyzing the performance of the proposed Zero-Velocity Detection (ZVD) algorithm and ZVD-based PDR through experiemnts, we confirm that the proposed method can provide accurate navigation information of pedestrians such as firefighters in the indoor space.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.3
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• pp.73-82
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• 1992

An industrial robot, installed real manufacturing processes an element of the system autmation, can be considered as an uncertain system due to dynamic uncertainties in inertial parameters and varying payloads. Most difficuties in controlling a robot manipulator are caused by the fact that the dynamic equations describing the motions of the manipulator are inherently nonlinear and heavily coupled effects between joints and associated links. Existing robot conrol systems have constant predefined gains and do not cover the complex dynamic interactions between manipulator joints. As a result, the manipulator is severly limited in range of application, speed of operation and variation of payload. The proposed controller is operated by adjusting its gains based on the response of the manipulator in such a way that the manipulator closely matches the reference model trajectories defined by the desinger. The proposed manipulator studied has two loops, an inner loop of model reference adaptive controller and an outer loop of state feedback controller with integral action to guarantee the stability of the adaptive scheme. This adaptation algorithm is based on the hyperstailiy approach with an improved Lyapunov function. The coupling among joints and the nonlinearity in the dynamic equation are explicitly considered. The designed manipulator controller shows good tracking performance in practical working environment, various load variations and parameter uncertainties.

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.5
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• pp.490-495
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• 2015

As flying robots are becoming popular, there are increased needs to use themforsuch purposes as parcel delivery, serving in restaurants, and stage performances. To control flying robots such as quad copters, localization is essential. In order to properly position flying robots, many techniques are in development, including IR (infra-red)-based systemswhich catch markers on a flying robot in order that it can position itself. However, this technique demonstrates only short coverage. Furthermore, localization from inertial sensors diverges as time passes. For this reason, this paper suggests a TWR (two-way ranging) based positioning technique. Despite the weaknesses in currently available TWR system, this paper suggests a self-positioning and outlier detection technique in order to provide reliable position information with a faster update rate. The self-positioning system sends a shorter message which reduces wireless traffic. By detecting and removing outlier measurements, a positioning result with better accuracy is acquired. Finally, this paper shows that the suggesting system detects outlierssequentially from less than half the number of anchors in localization system according to the degree of outlier in measurement and the noise level. By performing an outlier algorithm, better positioning accuracy is acquired as shown in the experimental result.

• Journal of Ocean Engineering and Technology
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• v.19 no.2 s.63
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• pp.74-81
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• 2005

A 3-D localization method of an autonomous underwater vehicle (AUV) has been developed, which can solve the limitations oj the conventional localization, such as LBL or SBL that reduces the flexibility and availability of the AUV. The system is composed of a mother ship (small unmanned marine prober) on the surface of the water and an unmanned underwater vehicle in the water. The mother ship is equipped with a digital compass and a GPS for position information, and an extended Kalman filter is used for position estimation. For the localization of the AUV, we used only non-inertial sensors, such as a digital compass, a pressure sensor, a clinometer, and ultrasonic sensors. From the orientation and velocity information, a priori position of the AUV is estimated by applying the dead reckoning method. Based on the extended Kalman filter algorithm, a posteriori position of the AUV is, then, updated by using the distance between the AUV and a mother ship on the surface of the water, together with the depth information from the pressure sensor.

• International Journal of Naval Architecture and Ocean Engineering
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• v.11 no.2
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• pp.679-687
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• 2019

This paper presents the autonomous swimming technology developed for an Autonomous Underwater Vehicle (AUV) operating in the underwater jacket structure environment. To prevent the position divergence of the inertial navigation system constructed for the primary navigation solution for the vehicle, we've developed kinds of marker-recognition based underwater localization methods using both of optical and acoustic cameras. However, these two methods all require the artificial markers to be located near to the cameras mounted on the vehicle. Therefore, in the case of the vehicle far away from the structure where the markers are usually mounted on, we may need alternative position-aiding solution to guarantee the navigation accuracy. For this purpose, we develop a sonar image processing based underwater localization method using a Forward Looking Sonar (FLS) mounted in front of the vehicle. The primary purpose of this FLS is to detect the obstacles in front of the vehicle. According to the detected obstacle(s), we apply an Occupancy Grid Map (OGM) based path planning algorithm to derive an obstacle collision-free reference path. Experimental studies are carried out in the water tank and also in the Pohang Yeongilman port sea environment to demonstrate the effectiveness of the proposed autonomous swimming technology.

• Journal of the Korea Institute of Military Science and Technology
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• v.15 no.2
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• pp.169-176
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• 2012

GPS(Global Positioning System) is widely used for the position estimation of an aerial vehicle. However, GPS may not be available due to hostile jamming or strategic reasons. A vision-based position estimation method can be effective if GPS does not work properly. In mountainous areas without any man-made landmark, a horizon is a good feature for estimating the position of an aerial vehicle. In this paper, we present a new method to estimate the position of the aerial vehicle equipped with a forward-looking infrared camera. It is assumed that INS(Inertial Navigation System) provides the attitudes of an aerial vehicle and a camera. The horizon extracted from an infrared image is compared with horizon models generated from DEM(Digital Elevation Map). Because of a narrow field of view of the camera, two images with a different camera view are utilized to estimate a position. The algorithm is tested using real infrared images acquired on the ground. The experimental results show that the method can be used for estimating the position of an aerial vehicle.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.491-494
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• 2006

Kalman filtering (KF) is hard to be applied to the SINS (Strap-down Inertial Navigation System)/RDSS (Radio Determination Satellite Service) integrated navigation system directly because the time delay of RDSS positioning in active mode is random. BP (Back-Propagation) Neuron computing as a powerful technology of Artificial Neural Network (ANN), is appropriate to solve nonlinear problems such as the random time delay of RDSS without prior knowledge about the mathematical process involved. The new algorithm betakes a BP neural network (BPNN) and velocity feedback to aid KF in order to overcome the time delay of RDSS positioning. Once the BP neural network was trained and converged, the new approach will work well for SINS/RDSS integrated navigation. Dynamic vehicle experiments were performed to evaluate the performance of the system. The experiment results demonstrate that the horizontal positioning accuracy of the new approach is 40.62 m (1 ${\sigma}$), which is better than velocity-feedback-based KF. The experimental results also show that the horizontal positioning error of the navigation system is almost linear to the positioning interval of RDSS within 5 minutes. The approach and its anti-jamming analysis will be helpful to the applications of SINS/RDSS integrated systems.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.377-382
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• 2006

Precision approach and landing of aircraft in a remote landing zone autonomously present several challenges. Firstly, the exact location, orientation and elevation of the landing zone are not always known; secondly, the accuracy of the navigation solution is not always sufficient for this type of precision maneuver if there is no DGPS availability within close proximity. This paper explores an alternative approach for estimating the navigation parameters of the aircraft to the landing area using only time-differenced GPS carrier phase measurement and range measurements from a vision system. Distinct ground landmarks are marked before the landing zone. The positions of these landmarks are extracted from the vision system then the ranges relative to these locations are used as measurements for the extended Kalman filter (EKF) in addition to the precise time-differenced GPS carrier phase measurements. The performance of this navigation algorithm is demonstrated using simulation.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.10
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• pp.992-997
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• 2009

This paper focuses on the calculation of the missile angular velocity under the reduced sensor condition and its verification using the Flight Motion Simulator(FMS). The missile angular velocity is usually measured by the body gyroscopes, but we assume that the inertial sensors on the missile body are in the absence of pitch and yaw gyroscopes. Under this reduced sensor condition, this paper shows the missile angular velocity can be calculated by using the gimbal seeker gyroscope, the roll body gyroscope, the gimbal angle and its rate. The FMS experiment was carried out to verify the proposed algorithm.

• Journal of the Korea Institute of Military Science and Technology
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• v.2 no.2
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• pp.186-196
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• 1999

The CPS provides data with long-term stability independent of passed time and the INS provides high-rate data with short-term stability. By integrating these complementary systems, a highly accurate navigation system can be achieved. In this paper, a loosely-coupled GPS/INS integration system is designed. It is a simple structure and is easy to implement and preserves independent navigation capability of GPS and INS. The integration system consists of a NCU, an IMU, a GPS receiver, and a monitoring system. The navigation algorithm in the NCU is designed under the multi-tasking environment based on a real-time kernel system and the monitoring system is designed using the Visual C++. The integrated Kalman filter is designed as a feedback formed 15-state filter, in which the states are position errors, velocity errors, attitude errors and sensor bias errors. The van test result shows that the integrated system provides more accurate navigation solution then the inertial or the GPS-alone navigation system.