Mechanical and electrical equipment is widely used in various links of the manufacturing industry and is also the key to the implementation of modern industrial technology. Winding is the core component of transformer...
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Traditionally, the unavoidable interaction between tour guides and tourists during extraordinary circumstances, particularly COVID-19, significantly increases the risk of disease transmission. To address this concern,...
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In order to get a better mapping effect and use it in robot navigation, this paper first established the kinematic model and dynamic model of the wheeled mobile robot used in the experimental research, and determined ...
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In order to get a better mapping effect and use it in robot navigation, this paper first established the kinematic model and dynamic model of the wheeled mobile robot used in the experimental research, and determined ...
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ISBN:
(数字)9781728198880
ISBN:
(纸本)9781728198897
In order to get a better mapping effect and use it in robot navigation, this paper first established the kinematic model and dynamic model of the wheeled mobile robot used in the experimental research, and determined the relationship between the relevant parameters of the mobile robot experimental equipment. After comparing and analyzing the advantages and disadvantages of different SLAM(Simultaneous Localization and Mapping) algorithms such as HectorSLAM, LagoSLAM, Cartographer, and GMapping, Monte Carlo adaptive positioning and laser mapping(GMapping) algorithms were selected for better results. And aiming at the problem that the traditional GMapping algorithm has no loopback and low accuracy, a solution of increase the closed-loop detection link to improvement is proposed. This GMapping algorithm is implemented through the ROS operating system. This algorithm improves the accuracy of mapping by changing the internal scanning registration algorithm. Finally, the improved algorithm and robot autonomous navigation technology are combined in experiments, and the accuracy of the improved algorithm's mapping is verified through many experiments.
This note is concerned with the H-infinity deconvolution filtering problem for linear time-varying discretetime systems described by state space models, The H-infinity deconvolution filter is derived by proposing a ne...
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This note is concerned with the H-infinity deconvolution filtering problem for linear time-varying discretetime systems described by state space models, The H-infinity deconvolution filter is derived by proposing a new approach in Krein space. With the new approach, it is clearly shown that the central deconvolution filter in an H-infinity setting is the same as the one in an H2 setting associated with one constructed stochastic state-space model. This insight allows us to calculate the complicated H-infinity deconvolution filter in an intuitive and simple way. The deconvolution filter is calculated by performing Riccati equation with the same order as that of the original system.
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