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.
