VCKF-based multi-robot collaborative navigating and positioning method

A technology of collaborative navigation and positioning method, applied in navigation calculation tools, complex mathematical operations, etc., can solve problems such as uncertainty and nonlinearity, and achieve the effect of improving positioning accuracy, accuracy and adaptability

Active Publication Date: 2018-03-27
HARBIN ENG UNIV
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Problems solved by technology

The present invention uses VCE to improve CKF, and proposes an improved collaborative navigation and positioning method, which can simultaneously solve the nonl

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  • VCKF-based multi-robot collaborative navigating and positioning method
  • VCKF-based multi-robot collaborative navigating and positioning method
  • VCKF-based multi-robot collaborative navigating and positioning method

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[0021] The present invention will be described in detail below in conjunction with specific implementation cases.

[0022] The present invention is a coordinated navigation and positioning algorithm for multiple mobile robots based on CKF and VCE methods. figure 1 The algorithm flow diagram shown, and its specific implementation is as follows:

[0023] Step 1: First, build a reasonable working environment according to the tasks of the multi-mobile robot, determine the fixed landmark points in the surrounding environment of the multi-mobile robot, and measure the position of each fixed landmark point in the working environment and the initial position of each mobile robot And posture information;

[0024] Step 2: Construct a state vector including the position and posture of multiple robots:

[0025] X=[X 1 X 2 … X n ] T

[0026] Where X i =[x i ,y i ,θ i ] T Represents the pose of the i-th robot, which can be expressed as:

[0027]

[0028] Construct a measurement vector including dis...

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Abstract

The invention discloses a VCKF-based multi-robot collaborative navigating and positioning method. The VCKF-based multi-robot collaborative navigating and positioning method comprises the following steps: determining an initial value of a collaborative navigating system according to a working environment of a plurality of moving robots; establishing non-linear system equations of the collaborativenavigating system of the plurality of moving robots; performing time updating on the collaborative navigating system of the plurality of moving robots according to a CKF filtering frame; observing a fixed waypoint and other robots in the working environment by the plurality of moving robots in real time, and acquiring relative distance and azimuth angles to serve as observation information; completing measurement updating of the collaborative navigating system of the plurality of moving robots by utilizing a VCKF algorithm by utilizing the observed measurement information and system equations;updating position information of the plurality of moving robots; completing high-precision collaborative navigating and positioning of the plurality of moving robots. According to the VCKF-based multi-robot collaborative navigating and positioning method, VCE (Variance Component Estimation)-based non-linear filter CKF is utilized, and variance matrix of process noises and measurement noises of asystem can be estimated in real time, so that a non-linear problem can be effectively solved, and the positioning precision and adaptability of the system are improved.

Description

technical field [0001] The invention relates to the field of robot navigation and positioning, in particular to a VCKF-based multi-robot cooperative navigation and positioning method. Background technique [0002] Because multi-mobile robots have many advantages that single robots do not have, such as more complex tasks and higher work efficiency, multi-mobile robots have gradually become a research hotspot in the field of robotics. However, robot cooperative navigation is the premise and guarantee for its safe and efficient completion of tasks, so a high-precision multi-mobile robot cooperative navigation algorithm is needed. [0003] Due to the nonlinear characteristics of the actual system, it is necessary to use nonlinear filters in the cooperative navigation of multiple mobile robots. CKF applies nonlinear transformation to 2n points with the same weight, so as to calculate the approximate Gaussian distribution of the system and complete the filtering process. Compare...

Claims

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Application Information

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IPC IPC(8): G01C21/20G06F17/12G06F17/16
CPCG01C21/20G06F17/12G06F17/16
Inventor 孙骞刁鸣李一兵王秋滢
Owner HARBIN ENG UNIV
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