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Location and environment modeling method of intelligent movable robot

A technology of mobile robots and modeling methods, which is applied in the directions of instruments, surveying and navigation, measuring devices, etc., and can solve the problems of increasing errors and large errors.

Inactive Publication Date: 2012-10-03
CHONGQING UNIV OF POSTS & TELECOMM
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Therefore, when the environment in which the robot is located is relatively unfamiliar, the robot will face this dilemma: that is, in order to build an environmental map, the robot needs to know clearly where it is at each moment, and for precise positioning, move The robot needs to have a high accuracy map
[0006] The Extended Kalman Filter (EKF), which is the most widely used in SLAM, performs Taylor series expansion on the nonlinear function during the linearization process, ignoring the second order and above to obtain an approximate linear model, but the The algorithm ignores the high-order terms of the Taylor expansion, and the error generated by linearization will be relatively large
Therefore, the EKF applied to SLAM at this time will increase the error

Method used

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  • Location and environment modeling method of intelligent movable robot
  • Location and environment modeling method of intelligent movable robot
  • Location and environment modeling method of intelligent movable robot

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Experimental program
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Effect test

Embodiment 1

[0060] figure 1 It is the schematic diagram of MIEKF algorithm; figure 2 is the estimated error effect of EKF / IEKF / MIEKF on the X-axis; image 3 is the estimated error effect of EKF / IEKF / MIEKF on the Y axis; Figure 4 is the estimation error effect of EKF / IEKF / MIEKF on the direction angle; Figure 5 update graph for EKF cycle; Image 6 It is a schematic diagram of SLAM; Figure 7 is the robot motion model, Figure 8 It is a flowchart of the MIEKF algorithm, as shown in the figure: a positioning and environment modeling method for an intelligent mobile robot provided by an embodiment of the present invention includes the following steps:

[0061] S1: Form a modified iterative extended Kalman filter algorithm, and determine the number of iterations;

[0062] S2: Establish the motion model x of the mobile robot k ;

[0063] S3: Establish the observation model z of the mobile robot k ;

[0064] S4: Initialize the prior state variables of the mobile robot prior covaria...

Embodiment 2

[0097] The positioning and environment modeling method of an intelligent mobile robot provided by the present invention is based on the most widely used extended Kalman filter algorithm in the field of simultaneous positioning and environment modeling of mobile robots, and the algorithm is improved so that the algorithm The performance has been greatly improved, which can better meet the application in SLAM. It also provides strong technical support for mobile robots to navigate autonomously in unknown environments and complete complex intelligent tasks.

[0098] The principle of the positioning and environment modeling method of the intelligent mobile robot is described in detail below:

[0099] First of all, the extended Kalman filter algorithm is to expand the nonlinear function by Taylor series, and ignore the second order and above order, so as to obtain an approximate linear model, thereby skipping the influence of linearization. After the approximate linear model is ob...

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Abstract

The invention discloses a location and environment modeling method of an intelligent movable robot, and the method comprises the steps of firstly forming correction iteration expanded Kalman filtering algorithm and determining a number of iterations, then establishing a movement model and an observation model of the movable robot, initializing the status of the movable robot, calculating a position jacobian matrix, controlling and inputting the jacobian matrix to calculate, observing the jacobian matrix and the like; and finally solving a Kalman gain matrix, updating a status estimation equation and a covariance matrix by resolving Kalman gain matrix, and repeating partial steps. The method is centralized on the expanded Kalman filter algorithm which is widely used in the simultaneous location and environment modeling field of the movable robot, and the algorithm is improved, so that the performance of the algorithm is greatly improved, and the algorithm can better meet the application in the SLAM (simultaneous location and mapping). The method also provides powerful technical support for the autonomous navigation and completion of complicated intelligent tasks of the movable robot in an unknown environment.

Description

technical field [0001] The invention relates to the field of automatic control, in particular to a positioning and environment modeling method of a mobile robot. Background technique [0002] Mobile robots are formed by the combination of sensor technology, control technology, information processing technology, machining technology, electronic technology, computing technology and other technologies. On the basis of these technologies, mobile robots mainly study the following contents: machine structure, multi-robot system, architecture, path planning, artificial intelligence, navigation and positioning, multi-sensor information fusion technology, human-computer interaction, feedback stabilization and tracking control. In the above research content, there is a technology that is indispensable in the practical application of mobile robots, and this technology is the research of navigation and positioning. Mobile robots, as the name implies, are not fixed in a certain place, b...

Claims

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Application Information

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IPC IPC(8): G01C21/00
Inventor 张毅罗元谢颖徐晓东唐贤伦李敏蔡军胡章芳
Owner CHONGQING UNIV OF POSTS & TELECOMM
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