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Wheeled robot state estimation method and autonomous navigation method based on robust rank Kalman filtering

A wheeled robot and Kalman filter technology, applied in navigation, surveying and navigation, navigation calculation tools, etc., can solve problems such as inability to deal with nonlinear uncertain systems, low navigation positioning accuracy, etc., and achieve strong robustness and navigation Positioning accuracy and the effect of improving accuracy

Pending Publication Date: 2021-10-22
HENAN VOCATIONAL COLLEGE OF APPLIED TECH
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The invention provides a wheeled robot state estimation method and autonomous navigation method based on the robustness rank Kalman filter-SLAM (robust RKF-SLAM), aiming at solving the problem that the existing SLAM method cannot handle nonlinear uncertain systems, navigation Technical problems with low positioning accuracy

Method used

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  • Wheeled robot state estimation method and autonomous navigation method based on robust rank Kalman filtering
  • Wheeled robot state estimation method and autonomous navigation method based on robust rank Kalman filtering
  • Wheeled robot state estimation method and autonomous navigation method based on robust rank Kalman filtering

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Embodiment

[0064] Embodiment: the wheeled robot autonomous navigation method based on robust rank Kalman filtering-SLAM, comprises the following steps:

[0065] Step 1: Establish a corresponding navigation system model for a wheeled robot:

[0066]

[0067] in, is the position (x and y) and azimuth (θ) of the wheeled robot at time k, u k is the control quantity, z k is the observed quantity, w k-1 and v k are system noise and measurement noise, respectively;

[0068] Step 2: Establish a SLAM probability model In the formula is the map feature point at time k;

[0069] The SLAM probability model is calculated using Bayesian theory.

[0070] First predict, through the motion model of the wheeled robot and the posterior probability distribution at time k-1 to obtain the prior probability distribution at time k:

[0071]

[0072] The second is the observation update, using the observation data z of the sensor k time k Correct the prior probability distribution to obtain ...

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Abstract

The invention discloses a wheeled robot state estimation method and an autonomous navigation method based on robust rank Kalman filtering, and aims to solve the technical problems that a nonlinear uncertain system cannot be processed and the navigation positioning precision is low in the existing SLAM (Simultaneous Localization and Mapping) technology. The robust rank Kalman filtering-SLAM autonomous navigation method has no requirement for whether a system model of the wheeled robot is Gaussian distribution or not, and has higher robustness and navigation positioning precision when noise statistics of measured data is not accurate due to the influence of an unknown environment; and the positioning and mapping precision of the wheeled robot in an unknown environment can be improved.

Description

technical field [0001] The invention relates to the technical field of robot autonomous navigation, in particular to a state estimation method and an autonomous navigation method of a wheeled robot based on a robust rank Kalman filter. Background technique [0002] Wheeled robots play an important role in various areas of people's production and life, such as life assistants, assembly line production, patrolling, disaster relief, space exploration and so on. In order for a wheeled robot to move autonomously in an unknown environment, it must first perceive and identify the surrounding environment, as well as accurately locate it. The precise positioning and environment perception of the robot are the key technologies for the robot to realize autonomous navigation, and it is also a hot spot in the field of robot research. [0003] Simultaneous Localization and Mapping (SLAM) technology is the best way to solve the problem of robot navigation in an unknown environment. The S...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/00G01C21/20
CPCG01C21/3815G01C21/20
Inventor 常新中郜海超张二月任旭凯孙丽娜王嵩垚娄泰山陈沛余红奎李原厂
Owner HENAN VOCATIONAL COLLEGE OF APPLIED TECH