• 제어로봇시스템학회논문지
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• 제14권8호
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• pp.800-808
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• 2008

This paper presents a localization algorithm of the outdoor wheeled mobile robot using the sensor fusion method based on indirect Kalman filter(IKF). The wheeled mobile robot considered with in this paper is approximated to the two wheeled mobile robot. The mobile robot has the IMU and encoder sensor for inertia positioning system and GPS. Because the IMU and encoder sensor have bias errors, divergence of the estimated position from the measured data can occur when the mobile robot moves for a long time. Because of many natural and artificial conditions (i.e. atmosphere or GPS body itself), GPS has the maximum error about $10{\sim}20m$ when the mobile robot moves for a short time. Thus, the fusion algorithm of IMU, encoder sensor and GPS is needed. For the sensor fusion algorithm, we use IKF that estimates the errors of the position of the mobile robot. IKF proposed in this paper can be used other autonomous agents (i.e. UAV, UGV) because IKF in this paper use the position errors of the mobile robot. We can show the stability of the proposed sensor fusion method, using the fact that the covariance of error state of the IKF is bounded. To evaluate the performance of proposed algorithm, simulation and experimental results of IKF for the position(x-axis position, y-axis position, and yaw angle) of the outdoor wheeled mobile robot are presented.

• 제어로봇시스템학회논문지
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• 제13권7호
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• pp.637-648
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• 2007

This paper presents a sensor fusion method based on indirect Kalman filter(IKF) for error compensation of low-cost inertial sensors and its application to the determination of attitude and position of small flying robots. First, the analysis of the measurement error characteristics to zero input is performed, focusing on the bias due to the temperature variation, to derive a simple nonlinear bias model of low-cost inertial sensors. Moreover, from the experimental results that the coefficients of this bias model possess non-deterministic (stochastic) uncertainties, the bias of low-cost inertial sensors is characterized as consisting of both deterministic and stochastic bias terms. Then, IKF is derived to improve long term stability dominated by the stochastic bias error, fusing low-cost inertial sensor measurements compensated by the deterministic bias model with non-inertial sensor measurement. In addition, in case of using intermittent non-inertial sensor measurements due to the unreliable data link, the upper and lower bounds of the state estimation error covariance matrix of discrete-time IKF are analyzed by solving stochastic algebraic Riccati equation and it is shown that they are dependant on the throughput of the data link and sampling period. To evaluate the performance of proposed method, experimental results of IKF for the attitude determination of a small flying robot are presented in comparison with that of extended Kaman filter which compensates only deterministic bias error model.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.241-244
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• 1997

A dead reckoning navigation system is developed for autonomous mobile robot localization. The navigation system was implemented by novel sensor fusion using a Kalman filter. A differential encoder and the gyroscope error models are developed for the filter. An indirect Kalman filter scheme is adopted to reduce the computational burden and to enhance the navigation system reliability. The filter mutually compensates the encoder errors and the gyroscope errors. The experimental results show that the proposed mobile . robot navigation algorithm provides the reliable position and heading angle of the mobile robot without any help of the external positioning systems.

• Journal of Ship and Ocean Technology
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• 제11권1호
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• pp.36-46
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• 2007

The maintenance of a ship is essential for safe navigation and hence regular surveys are prescribed according to the rule of classification societies. A hull inspection is generally performed by professional divers, but it takes a long time and the efficiency is low in terms of time and cost. In this research, a ROV(Remotely Operated Vehicle) named as SNU-ROV(Seoul National University-ROV) is developed to replace the conventional inspection method. In this system, the ROV is intended to be used for inspecting ship and harbor because harbor inspection is merging as a safety measure against any possible terror actions. In order to increase the efficiency of inspection, the ROV must be able to measure the exact position of damages. SNU-ROV has a positioning system based on LBL(Long Base Line). In shallow water such as harbor, however, LBL has bad DOP(Dilution of Precision) in the depth direction due to the limited depth. Thus LBL only can not locate the exact depth position. To solve the DOP problem, a pressure sensor is introduced to LBL and a complementary filter is attached by using indirect feedback Kalman filter. Thus developed positioning system is verified by simulation and experiment in towing tank.