TP212 97021226光栅编码器输出脉冲抖动的去除=Clearing theoutput pulse jitters of the optical grating encoders[刊,中]/李华,廖铭,齐美彬(安徽工学院.安徽,合肥(230069))//电子技术.—1995,(12).—8—9在电机定位状态下,由于机床...TP212 97021226光栅编码器输出脉冲抖动的去除=Clearing theoutput pulse jitters of the optical grating encoders[刊,中]/李华,廖铭,齐美彬(安徽工学院.安徽,合肥(230069))//电子技术.—1995,(12).—8—9在电机定位状态下,由于机床的振动,造成光栅编码器有抖动脉冲输出。该文介绍了电路能去除抖动脉冲而又不影响编码器的正常工作。图5(方舟)展开更多
The task of simultaneous localization and mapping （SLAM） is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. ...The task of simultaneous localization and mapping （SLAM） is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. However, there are two obvious limitations in FastSLAM 2.0, one is the linear approximations of nonlinear functions which would cause the filter inconsistent and the other is the ＂particle depletion＂ phenomenon. A kind of PSO ＆ Hjj-based FastSLAM 2.0 algorithm is proposed. For maintaining the estimation accuracy, H~ filter is used instead of EKF for overcoming the inaccuracy caused by the linear approximations of nonlinear functions. The unreasonable proposal distribution of particle greatly influences the pose state estimation of robot. A new sampling strategy based on PSO （particle swarm optimization） is presented to solve the ＂particle depletion＂ phenomenon and improve the accuracy of pose state estimation. The proposed approach overcomes the obvious drawbacks of standard FastSLAM 2.0 algorithm and enhances the robustness and efficiency in the parts of consistency of filter and accuracy of state estimation in SLAM. Simulation results demonstrate the superiority of the proposed approach.展开更多
文摘TP212 97021226光栅编码器输出脉冲抖动的去除=Clearing theoutput pulse jitters of the optical grating encoders[刊,中]/李华,廖铭,齐美彬(安徽工学院.安徽,合肥(230069))//电子技术.—1995,(12).—8—9在电机定位状态下,由于机床的振动,造成光栅编码器有抖动脉冲输出。该文介绍了电路能去除抖动脉冲而又不影响编码器的正常工作。图5(方舟)
基金Project(ZR2011FM005)supported by the Natural Science Foundation of Shandong Province,China
文摘The task of simultaneous localization and mapping （SLAM） is to build environmental map and locate the position of mobile robot at the same time. FastSLAM 2.0 is one of powerful techniques to solve the SLAM problem. However, there are two obvious limitations in FastSLAM 2.0, one is the linear approximations of nonlinear functions which would cause the filter inconsistent and the other is the ＂particle depletion＂ phenomenon. A kind of PSO ＆ Hjj-based FastSLAM 2.0 algorithm is proposed. For maintaining the estimation accuracy, H~ filter is used instead of EKF for overcoming the inaccuracy caused by the linear approximations of nonlinear functions. The unreasonable proposal distribution of particle greatly influences the pose state estimation of robot. A new sampling strategy based on PSO （particle swarm optimization） is presented to solve the ＂particle depletion＂ phenomenon and improve the accuracy of pose state estimation. The proposed approach overcomes the obvious drawbacks of standard FastSLAM 2.0 algorithm and enhances the robustness and efficiency in the parts of consistency of filter and accuracy of state estimation in SLAM. Simulation results demonstrate the superiority of the proposed approach.
