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Improved particle filter-based mobile robot positioning method

A mobile robot and positioning method technology, applied in machine learning, instruments, measuring devices, etc., can solve problems such as poor particles, a large number of particles, and a large amount of calculation

Active Publication Date: 2014-01-01
DEEPBLUE ROBOTICS (SHANGHAI) CO LTD
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  • Application Information

AI Technical Summary

Problems solved by technology

Among the two existing simultaneous positioning and mapping methods, the method based on Kalman filter has a large amount of calculation and has the disadvantage that the error must obey the Gaussian distribution. Therefore, the research on mobile robot positioning and mapping at home and abroad is mainly aimed at particle filter method
The standard pf-slam will have the problem of particle poverty and the need for a large number of particles. Particle poverty means that in the particle filter algorithm, as the particles are updated and iterated, the weights of many particles will become smaller, even after resampling. , there will be a phenomenon of particle unity

Method used

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  • Improved particle filter-based mobile robot positioning method

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Embodiment Construction

[0026] A non-limiting embodiment is given below in conjunction with the accompanying drawings to further illustrate the present invention.

[0027] (1) Mobile robots generally use an odometer to calculate the moving distance. The working principle of the odometer is to calculate the moving distance by using the arc turned by the photoelectric encoder disc installed on the wheel within a certain period of time. Assuming that the radius of the wheel is r, the wheelbase of the two wheels is l, the output of the photoelectric encoder disc is n times / △t, and the resolution is p lines / rev, the formula for calculating the moving distance of the mobile robot is d=2πrn / p(rad), according to According to this formula, from the time t to the time t+1 of the mobile robot, the moving distance of the two wheels is d respectively due to the differential drive 1 and d 2 , through the above parameters, the displacement increment, rotation angle increment and motion radius of the robot can be o...

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Abstract

The invention provides an improved particle filter-based mobile robot positioning method. The improved particle filter-based mobile robot positioning method comprises the following steps: establishing a motion equation and a road sign calculation equation of a robot; optimizing a particle set by using a multi-agent particle swarm optimization algorithm, wherein the obtained optimal value is estimation of a pose; estimating an environmental road sign by using Kalman filtering algorithm; updating and normalizing the weight and resampling. The positioning method is accurate in positioning and easy to implement; the pose estimation and the environmental road sign estimation of the mobile robot are more accurate in a simulation process of the mobile robot.

Description

technical field [0001] The invention relates to the technical field of mobile robots and their simultaneous positioning and mapping, and specifically provides a method for improving positioning errors of mobile robots. Background technique [0002] Mobile robots are intelligent and independent, and can replace human tasks in various unknown or dangerous environments, and these unknown or dangerous environments are often full of various uncertain factors, so mobile robots must have autonomous navigation capabilities The key to exploring unknown and dangerous environments for mobile robots is to establish a local environmental map. Data fusion is performed on all established local environmental maps to finally construct a global environmental map. The positioning technology of mobile robots is the basis for mobile robots to establish local environmental maps. The foundation can also be said to be the foundation of other research on mobile robots. Only with the precise positio...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/00G01C21/20
CPCG01C21/20G06N20/00
Inventor 唐贤伦蒋波杰庄陵虞继敏张毅张鹏李洋
Owner DEEPBLUE ROBOTICS (SHANGHAI) CO LTD
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