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Multi-robot cooperative location algorithm based on square root cubature Kalman filtering

A Kalman filtering and multi-robot technology, applied in instruments, navigation computing tools, measuring devices, etc., can solve problems such as increased calculations, achieve the effects of improving accuracy, reducing computational complexity, and shortening time-consuming algorithms

Inactive Publication Date: 2017-01-11
ANHUI UNIVERSITY OF TECHNOLOGY AND SCIENCE
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Problems solved by technology

This algorithm has better real-time performance, but the algorithm is prone to error accumulation, and its calculation amount will increase sharply with the increase of the map

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  • Multi-robot cooperative location algorithm based on square root cubature Kalman filtering
  • Multi-robot cooperative location algorithm based on square root cubature Kalman filtering
  • Multi-robot cooperative location algorithm based on square root cubature Kalman filtering

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Embodiment Construction

[0058] In order to make the technical means, creative features, goals and effects achieved by the present invention easy to understand, the present invention will be further described below in conjunction with specific embodiments.

[0059] According to one embodiment of the present invention, as figure 1 As shown, the whole algorithm is divided into two steps: prediction and update. Firstly, the motion equation and observation equation of the robot are established, and the relative orientation is used as the measured value to further obtain the dynamic model of multi-robot cooperative positioning. The prediction stage includes: calculating the volume point set; propagating the volume point through the state equation; pre-estimating the robot pose state and predicting the square root factor. The update stage includes: calculating volume point set; calculating Kalman gain; calculating pose information and square root factor; updating state vector, covariance matrix and pose in...

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Abstract

The invention discloses a multi-robot cooperative location algorithm based on square root cubature Kalman filtering (SR-CKF) and belongs to the field of robot cooperative location. The whole algorithm includes two steps: predication and updating. Firstly, a robot motion equation and a predication equation are established and a relative azimuth is used as a measured value; furthermore, a multi-robot cooperative location dynamic model is obtained. A predication phase comprises: calculating a cubature point set; transmitting cubature points through a state equation; pre-estimating a robot pose state and predicating a square root factor. An updating phase comprises: calculating a cubature point set; calculating Kalman gains; calculating pose information and a square root factor; updating a state vector, a covariance matrix and the pose information. According to the embodiment of the invention, a target state mean value and the square root factor of the covariance matrix are directly transmitted in an updating process and the complexity of calculation is reduced; the symmetry and the semi-positive definitiveness of the covariance matrix are ensured, and the numerical value precision and the stability are improved.

Description

technical field [0001] The invention relates to the field of robot cooperative positioning, and relates to a multi-robot cooperative positioning algorithm based on square root volumetric Kalman filtering. Background technique [0002] As people's research fields continue to expand, extending from land and ocean to deep sea and even unknown outer space, multi-robot collaborative systems are playing an increasingly important role in military, aerospace, service, industry and other fields. Being able to perceive, model and determine its own position on the unknown and complex external environment information is the premise and basis for the robot's autonomous navigation. Multi-robot co-localization refers to mutual observation between multiple robots, independent of the external environment, by sharing environmental information, and realizing the determination of their respective pose information in a common environment. [0003] At present, there are many methods to solve mul...

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Application Information

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IPC IPC(8): G01C21/20G01C21/00
CPCG01C21/20G01C21/005
Inventor 陈孟元李朕阳郎朗
Owner ANHUI UNIVERSITY OF TECHNOLOGY AND SCIENCE
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