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Method and device for positioning particles of mobile robot

A technology of mobile robots and positioning methods, applied in positioning, measuring devices, instruments, etc., to achieve the effects of reducing complexity, improving computing speed and accuracy, and preventing degradation

Inactive Publication Date: 2011-06-22
SHANGHAI DIANJI UNIV
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  • Abstract
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Problems solved by technology

[0007] The technical problem to be solved by the present invention is to provide a particle positioning method and device for a mobile robot, which can reduce the information loss and error increase caused by the linearization of the nonlinear model, and reduce the complexity of the particle positioning algorithm. Effectively prevent the degradation and exhaustion of particle resampling, and improve the real-time online running speed and accuracy of the algorithm

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  • Method and device for positioning particles of mobile robot
  • Method and device for positioning particles of mobile robot
  • Method and device for positioning particles of mobile robot

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Embodiment Construction

[0023] The specific implementation of the particle positioning method and device for the mobile robot provided by the present invention will be described in detail below in conjunction with the accompanying drawings.

[0024] First, a specific implementation of the particle positioning method for the mobile robot of the present invention is given.

[0025] attached figure 1 Shown is a schematic diagram of the implementation steps of a specific embodiment of the method of the present invention, including: step S11, performing landmark estimation with an unscented Kalman filter algorithm; step S12, obtaining approximate distribution location information of a plurality of sampled particles, and using Trace Kalman filter algorithm for auxiliary positioning; step S13, optimize the sampled particles based on the genetic algorithm within the node.

[0026] Referring to step S11, the landmark estimation is performed by an unscented Kalman filter algorithm.

[0027] In this step, the...

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Abstract

The invention provides a method and device for positioning particles of a mobile robot. The method for positioning particles of the mobile robot comprises the following steps of: (a) estimating road signs by an unscented Kalman filtering (UKF) algorithm; (b) obtaining approximate distribution and position information of a plurality of sampling particles, and carrying out auxiliary positioning by adopting the unscented Kalman filtering algorithm; and (c) optimizing the sampled particles based on a genetic algorithm in a node. The method for positioning particles of the mobile robot has the advantages of: (1) estimating road signs by the UKF algorithm instead of the EKF (Extended Kalman Filter) algorithm in calculation of road sign estimation, and avoiding huge deduction of a Jocabian matrix; (2) disclosing the filtering and positioning method of particles distributed according to the UKF auxiliary advice; and (3) disclosing the concept of weight value based on topological node importance, improving diversity of particles, and preventing degradation of particles.

Description

【Technical field】 [0001] The invention relates to the field of probabilistic reasoning, intelligent system and robot control, in particular to the instant positioning and map creation technology of an intelligent mobile robot in detecting an unknown environment. 【Background technique】 [0002] At present, the real-time positioning and map creation algorithms in the field of intelligent mobile robot research mainly use probability estimation algorithms, such as Kalman filter (KF), extended Kalman filter (EKF), unscented Kalman filter (UKF), maximum likelihood Estimation (MLE), particle filter (PF), Rao-Blackwellized particle filter (RBPF), Markov positioning algorithm and Gaussian filter, etc. The KF and EKF methods are classic algorithms in the field of environmental modeling. Their biggest advantage is that they can estimate the posterior probability of all elements in the map online. The meaning of this method is clear, concise and easy to implement. MLE is currently the ...

Claims

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Application Information

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IPC IPC(8): G09B29/00G06F17/15G06N3/12G01S5/00
Inventor 王海军
Owner SHANGHAI DIANJI UNIV
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