Self-localization is a fundamental requirement for the mobile robot. Robot usually contains a large number of dif- ferent sensors, which provide the information of robot localization, and all the sensor information sh...Self-localization is a fundamental requirement for the mobile robot. Robot usually contains a large number of dif- ferent sensors, which provide the information of robot localization, and all the sensor information should be considered for the optimal location. Kalman filter is efficient to realize the information fusion. Used as an efficient sensor fusion algorithm, Kalman filter is an advanced filtering technique which can reduce errors of the position and orientation of the sensors. Kalman filter has been paied much attention to robot automation and solutions to solve uncertainties such as robot localization, navigation, following, tracking, motion control, estimation and prediction. The paper briefly describes Kalman filter theory, and establishes a simple mathematical model based on muti-sensor mobile robot. Meanwhile, Kalman filter is used in robot self-localization by simulations, and it is demonstrated by simulations that Kalman filter is effective.展开更多
In this paper,we present a method for localization of a rail autonomous pesticide spraying and sampling robot working in greenhouse using an absolute localization system.Design and implementation of the localization s...In this paper,we present a method for localization of a rail autonomous pesticide spraying and sampling robot working in greenhouse using an absolute localization system.Design and implementation of the localization system comes from the usage of beacon systems each of which is composed of an RF single receiver and an ultrasonic transmitter.The RF single receiver gets the synchronization signal from the mobile robot,and the ultrasonic transmitter sends ultrasonic signal,thus the distance from the beacon to the ultrasonic receiver can be measured.The position of a beacon in coordinate system of robot can be calculated according to distance information from the beacons to two ultrasonic receivers which are mounted on the robot.Based on the coordinate transformation,the position of a mobile robot can be calculated from the beacon＇s absolute position information in the global coordinate system.Experiments demonstrate the effectiveness of the proposed method in real world applications.展开更多
An accurate low-cost ultrasonic localization system is de- veloped for automated mobile robots in indoor environments, which is essential for automatic navigation of mobile robots with various tasks. Although ultrasen...An accurate low-cost ultrasonic localization system is de- veloped for automated mobile robots in indoor environments, which is essential for automatic navigation of mobile robots with various tasks. Although ultrasenic sensors are more cost-effective than other sensors such as Laser Range Finder （LRF） and vision, but they are inaccurate and directionally ambiguons. First, the matched filter is used to measure the distance accurately. For resolving the computational complexity of the matched filter, a new matched filter algorithm with simple compution is proposed. Then, an ultrasonic localization system is proposed which consists of three ultrasonic receivers and two or mote transmitters for improving position and orientation accuracy was developed. Finally, an extended Kalman filter is designed to estimate both the static and dynamic positions and orientations. Various simu lations and experimental results show that the proposed system is effective.展开更多
基金Research Fund for the Doctoral Program of Higher Education of China(No.20123718120007)
文摘Self-localization is a fundamental requirement for the mobile robot. Robot usually contains a large number of dif- ferent sensors, which provide the information of robot localization, and all the sensor information should be considered for the optimal location. Kalman filter is efficient to realize the information fusion. Used as an efficient sensor fusion algorithm, Kalman filter is an advanced filtering technique which can reduce errors of the position and orientation of the sensors. Kalman filter has been paied much attention to robot automation and solutions to solve uncertainties such as robot localization, navigation, following, tracking, motion control, estimation and prediction. The paper briefly describes Kalman filter theory, and establishes a simple mathematical model based on muti-sensor mobile robot. Meanwhile, Kalman filter is used in robot self-localization by simulations, and it is demonstrated by simulations that Kalman filter is effective.
基金supported by the MKE(The Ministry of Knowledge Economy),Korea,under the ITRC(Information Technology Research Center)support program supervised by the NIPA(National IT Industry Promotion Agency)(NIPA-2010-C1090-1021-0010)
文摘In this paper,we present a method for localization of a rail autonomous pesticide spraying and sampling robot working in greenhouse using an absolute localization system.Design and implementation of the localization system comes from the usage of beacon systems each of which is composed of an RF single receiver and an ultrasonic transmitter.The RF single receiver gets the synchronization signal from the mobile robot,and the ultrasonic transmitter sends ultrasonic signal,thus the distance from the beacon to the ultrasonic receiver can be measured.The position of a beacon in coordinate system of robot can be calculated according to distance information from the beacons to two ultrasonic receivers which are mounted on the robot.Based on the coordinate transformation,the position of a mobile robot can be calculated from the beacon＇s absolute position information in the global coordinate system.Experiments demonstrate the effectiveness of the proposed method in real world applications.
基金supported by the MKE(The Ministry of Knowledge Economy,Korea)the ITRC(Information Technology Research Center)support program(ⅡTA-2009-(C1090-0902-0007))
文摘An accurate low-cost ultrasonic localization system is de- veloped for automated mobile robots in indoor environments, which is essential for automatic navigation of mobile robots with various tasks. Although ultrasenic sensors are more cost-effective than other sensors such as Laser Range Finder （LRF） and vision, but they are inaccurate and directionally ambiguons. First, the matched filter is used to measure the distance accurately. For resolving the computational complexity of the matched filter, a new matched filter algorithm with simple compution is proposed. Then, an ultrasonic localization system is proposed which consists of three ultrasonic receivers and two or mote transmitters for improving position and orientation accuracy was developed. Finally, an extended Kalman filter is designed to estimate both the static and dynamic positions and orientations. Various simu lations and experimental results show that the proposed system is effective.
