• IEMEK Journal of Embedded Systems and Applications
• /
• v.12 no.4
• /
• pp.213-221
• /
• 2017

Utilizing the existing polishing robot prevents unrestricted change of direction, driving, and identification of driving pathway. To overcome this barrier, driving mechaism has been designed with Omniwheels with encoders and RSSI method of beacon system has been utilized to identify the driving path by position recognition. Due to the wheel characteristics, the Omniwheel mobile robot generates greater slip than the conventional mobile robot, which reduces its driving accuracy. Therefore, to improve the driving accuracy, the localization is conducted through the fusion of encoder and RSSI of beacon data to compensate for the errors caused by Dead Reckoning and inaccuracy of sensors. Finally, the localization accuracies of the proposed and conventional indoor localization method are compared to show effectiveness of the proposed driving control for a polishing robot.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.186-186
• /
• 2000

In the GPS/DR integrated system, the GPS position(or velocity) is used to compensate the DR output and to calibrate errors in the DR sensor. This synergistic relationship ensures that the calibrated DR accuracy can be maintained even when the GPS signal is blocked. Because of the observability problem, however, the DR sensors are not sufficiently calibrated when the vehicle speed is low. This problem can be solved if we use a multi-antenna GPS receiver for attitude determination instead of conventional one. This paper designs a two-antenna GPS receiver integrated with DR sensors. The proposed integration system has three remarkable features. First, the DR sensor can be calibrated regardless of the vehicle speed with the aid of two-antenna GPS receiver. Secondly, the search space of integer ambiguities in GPS carrier-phase measurements is reduced to a part of the surface of the sphere using DR heading. Thirdly, the detection resolution of cycle-slips in GPS carrier-phase measurements is improved with the aid of DR heading. From the experimental result, it is shown that the search grace is drastically reduced to about 3120 of the non-aided case and the cycle-slips of 1 or half cycle can be detected.

• Journal of the Institute of Electronics Engineers of Korea SC
• /
• v.46 no.2
• /
• pp.28-35
• /
• 2009

Positioning technology which a part technology of Mobile Robot is an essential technology to locate the position of Robot and navigate to wanted position. The Robot that based on wheel drive uses Odometry position. technology. But when using Odometry positioning technology, it's hard to find out constant error value because a slip phenomenon occurs as the Robot runs. In this paper, we present the way to minimize positioning error by using Odometry and Inertial sensor. Also, the way to reduce error with Inertial sensor on SLAM using image will be shown, too.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.80-80
• /
• 2000

The GPS can provide accurate position information on the earth. But GPS receiver can't give position information inside buildings. DR(Dead-Reckoning) or INS(Inertial Navigation System) gives position information continuously indoors as well as outdoors, because they do not depend on the external navigation information. But in general, the inertial sensors severely suffer from their drift errors, the error of these navigation system increases with time. GPS and DR sensors can be integrated together with Kalman filter to overcome these problems. In this paper, we developed a personal navigation system which can be carried by person, using GPS and electronic pedometer. The person's footstep is detected by an accelerometer installed in vertical direction and the direction of movement is sensed by gyroscope and magnetic compass. In this case the step size is varying with person and changing with circumstance, so determining step size is the problem. In order to calculate the step size of detected footstep, the neural network method is used. The teaming pattern of the neural network is determined by human walking pattern data provided by 3-axis accelerometer and gyroscope. We can calculate person's location with displacement and heading from this information. And this neural network method that calculates step size gives more improved position information better than fixed step size.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• v.12 no.8
• /
• pp.3657-3682
• /
• 2018

This paper presents a Bluetooth and pedestrian dead reckoning (PDR)-based indoor fusion localization approach (BtPDR) using smartphones. A Bluetooth and PDR-based indoor fusion localization approach can localize the initial position of a smartphone with the received signal strength (RSS) of Bluetooth. While a smartphone is moving, BtPDR can track its position by fusing the localization results of PDR and Bluetooth RSS. In addition, BtPDR can adaptively modify the parameters of PDR. The contributions of BtPDR include: a Bluetooth RSS-based Probabilistic Voting (BRPV) localization mechanism, a probabilistic voting-based Bluetooth RSS and PDR fusion method, and a heuristic search approach for reducing the complexity of BRPV. The experiment results in a real scene show that the average positioning error is < 2m, which is considered adequate for indoor location-based service applications. Moreover, compared to the traditional PDR method, BtPDR improves the location accuracy by 42.6%, on average. Compared to state-of-the-art Wireless Local Area Network (WLAN) fingerprint + PDR-based fusion indoor localization approaches, BtPDR has better positioning accuracy and does not need the same offline workload as a fingerprint algorithm.

• Journal of Institute of Control, Robotics and Systems
• /
• v.22 no.11
• /
• pp.967-976
• /
• 2016

This paper presents a 3D carrier-smoothed GNSS/DR (Global Navigation Satellite System/Dead Reckoning) integrated system for precise ground-vehicle trajectory estimation. For precise DR navigation on sloping roads, the AHRS (Attitude Heading Reference System) methodology is employed. By combining the integrated carrier phase of GNSS and DR sensor measurements, a vehicle trajectory with an accuracy of less than 20cm is obtained even when cycle slip or change of visibility occur. In order to supplement the weak GNSS environment with DR successfully, the DR sensor is precisely compensated for using GNSS Doppler measurements when GNSS visibility is good. By integrating a multi-GNSS receiver with low-cost IMU, a precise 3D navigation system for land vehicles is proposed in this paper. For real-time implementation, a decoupled Kalman filter is employed in the integrated system. Through field experiments, the performance of the proposed system is verified in various road environments, including sloping roads, good-visibility areas, high multi-path areas, and under-ground parking areas.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.20 no.4
• /
• pp.65-71
• /
• 2020

In order to accurately track the trajectory of the smartphone indoors and outdoors, the WiFi Fingerprint method and the Pedestrian Dead Reckoning method are fused. The former can estimate the absolute position, but an error occurs randomly from the actual position, and the latter continuously estimates the position, but there are accumulated errors as it moves. In this paper, the model and Kalman Filter equation to fuse the estimated position data of the two methods were established, and optimal system parameters were derived. According to covariance value of the system noise and measurement noise the estimation accuracy is analyzed. Using the measured data and simulation, it was confirmed that the improved performance was obtained by complementing the two methods.

• Journal of Ocean Engineering and Technology
• /
• v.24 no.3
• /
• pp.46-51
• /
• 2010

This paper describes the implementation of a precise underwater navigation solution using a multi-sensor fusion technique based on USBL, DVL, and IMU measurements. To implement this precise underwater navigation solution, three strategies are chosen. The first involves heading alignment angle identification to enhance the performance of a standalone dead-reckoning algorithm. In the second, the absolute position is found quickly to prevent the accumulation of integration error. The third one is the introduction of an effective outlier rejection algorithm. The performance of the developed algorithm was verified with experimental data acquired by the deep-sea ROV, Hemire, in the East-sea during a survey of a methane gas seepage area at a 1,500 m depth.

• Journal of Navigation and Port Research
• /
• v.33 no.2
• /
• pp.99-104
• /
• 2009

The Kinematic GPS is well known to provide a quite good accuracy of positioning within an level. Although kinematic GPS assures high precision measurement on the basis of an appreciable distance between a reference station and an observational point, it has measurable distance restriction within 20 km from a reference station on land. Therefore, it is necessary to make out a simple and low-cost method to obtain accurate positioning information without distance restriction In this paper, the velocity integration method to get the precise velocity information of a ship is explained. The experimental results of Zig-zag maneuver and Williamson turn as the ship's maneuvering test, and other experimental results of ship's movement during leaving and entering the port with low speed were shown. From the experimental results, ship's course, speed and position are compared with those obtained by kinematic-GPS, velocity integration method and dead reckoning position using Gyro-compass and Doppler-log.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.1
• /
• pp.41-46
• /
• 2006

Ubiquitous location based services, offer helpful services anytime and anywhere by using real-time location information of objects based on ubiquitous network. Particularly, autonomous mobile robots can be a solution for various applications related to ubiquitous location based services, e.g. in hospitals, for cleaning, at airports or railway stations. However, a meaningful and still unsolved problem for most applications is to develop a robust and cheap positioning system. A typical example of position measurements is dead reckoning that is well known for providing a good short-term accuracy, being inexpensive and allowing very high sampling rates. However, the measurement always has some accumulated errors because the fundamental idea of dead reckoning is the integration of incremental motion information over time. The other hand, a localization system using RFID offers absolute position of robots regardless of elapsed time. We construct an absolute positioning system based on RFID and investigate how localization technique can be enhanced by RFID through experiment to measure the location of a mobile robot. Tags are placed on the floor at 5cm intervals in the shape of square in an arbitrary space and the accuracy of position measurement is investigated . To reduce the error and the variation of error, a weighting function based on Gaussian function is used. Different weighting values are applied to position data of tags since weighting values follow Gaussian function.