CN103206954A - Multi-sensor information fusion method for mobile robot based on UKF (Unscented Kalman Filter) - Google Patents
Multi-sensor information fusion method for mobile robot based on UKF (Unscented Kalman Filter) Download PDFInfo
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Abstract
The invention provides a multi-sensor information fusion method for a mobile robot based on a UKF (Unscented Kalman Filter). The method comprises the following processing steps that milemeter locating information and electronic compass azimuth information of the mobile robot are filtered to obtain robot local rough azimuth information L; WLAN (Wireless Local Area Network) locating information is processed by adopting a trilateration locating method to obtain a robot preliminary overall locating coordinate W; the W and the L are subjected to coordinate transformation to obtain overall azimuth information WL; RFID (Radio Frequency Identification) locating information is processed by adopting the trilateration locating method to obtain locating information, and the locating information is subjected to data layer filtering to obtain overall locating information R; the ultrasonic locating information is filtered to obtain barrier local azimuth information Z; and the W, the L, the R and the Z are subjected to total information fusion to obtain robot and barrier overall accurate azimuth information. According to the method, the independent locating accuracy of the mobile robot can be improved significantly, and the anti-interference independent locating capability and the navigation capability of the mobile robot can be improved.
Description
(1) technical field
The invention belongs to the autonomous location of mobile robot and navigation field, particularly the method based on the multi-sensor information fusion of UKF algorithm in the autonomous location navigation of a kind of mobile robot's multisensor.
(2) background technology
Concerning the mobile robot, locating module is the important component part of whole navigational system.For position and the attitude that makes the main control module know that robot is current, must take measures to provide enough accurate position and directional information.Therefore, for any practical mobile robot, accurate, reliable location technology is necessary condition precedent.
Multi-sensor information fusion technology is to improve one of the core of the autonomous bearing accuracy of mobile robot and navigation performance and basic technology, is forward position and the focus of the autonomous location of present mobile robot and navigation field research.This technology is just obtaining application more and more widely in concrete practices such as the perception of mobile robot's ambient intelligence, autonomous location.Wherein, the UKF filtering algorithm is also adopted by people gradually with its good filtering accuracy and high calculating speed.
UKF(Unscented Kalman Filter), the Chinese lexical or textual analysis is harmless Kalman filtering, be that probability density distribution to nonlinear function is similar to, approach the posterior probability density of state with a series of definite samples, rather than nonlinear function is similar to, do not need differentiate to calculate the Jacobian matrix.UKF does not have linearization to ignore higher order term, so the computational accuracy of nonlinear Distribution statistic is higher.Based on above-mentioned advantage, UKF is widely used in a plurality of fields such as navigation, target following, signal processing and neural network learning.Therefore the present invention will utilize the nonlinear data of the mobile robot's of UKF various kinds of sensors to carry out data fusion, reach the purpose that improves the localization for Mobile Robot precision.
(3) summary of the invention
The object of the present invention is to provide a kind ofly can significantly improve the autonomous bearing accuracy of mobile robot, strengthen the mobile robot's multiple sensor information amalgamation method based on UKF of the anti-interference autonomous location of mobile robot and homing capability.
The object of the present invention is achieved like this: it comprises following treatment step: mobile robot's odometer locating information and electronic compass azimuth information are carried out filtering by UKF (A) filtering algorithm obtain robot part azimuth information L slightly just; Adopt three limit localization methods processing to obtain the preliminary overall elements of a fix W of robot the locating information of WLAN (wireless local area network) WLAN; W and L are carried out coordinate transform obtain further overall azimuth information WL of robot; Adopt three limit localization methods to handle the RFID locating information and obtain locating information, and carry out data Layer filtering with UKF (B) filtering algorithm and obtain accurate relatively more overall locating information R, the ultrasound wave locating information is obtained the local azimuth information Z of barrier by the filtering of UKF (A) filtering algorithm, W, L, R, Z carry out the total information fusion by UKF (B) filtering algorithm and obtain robot and the accurate azimuth information of the barrier overall situation, realize the autonomous accurate location of mobile robot and map perception.
The present invention also has some technical characterictics like this:
1, described UKF (A) filtering algorithm utilizes the iterative computation of computing machine to realize progressively optimal estimation, and concrete steps are: at first filtering algorithm carries out initialization, comprises operations such as definition and initialization matrix; The second, determine system's dimension according to system state equation and observation equation, and calculate Sigma point and weight coefficient thereof; The 3rd, the Sigma point and the weight coefficient that obtain are changed, and computing time renewal equation; The 4th, according to the sensing data calculating observation vector renewal equation that obtains; The 5th, the calculation of filtered gain; The 6th, calculate covariance matrix; The 7th, current optimal State Estimation is exported and returned the calculating Sigma point stage and restart iterative computation, know system's location end; UKF (B) filtering algorithm and the difference of UKF (A) filtering algorithm are that in UKF (A) the filtering algorithm initialization procedure, state variable is that (s), wherein, x, y are robot current time coordinate for x, y, and s is the robot corner; And the init state variable of UKF (B) (7) be (x, y, X, Y), x wherein, y is robot current time coordinate, X, Y are the traveling speed of robot.
2, the described step that W and L are carried out coordinate transform realizes that for W and L are carried out the premultiplication translation matrix coordinate translation obtains further overall azimuth information WL of robot.
The invention has the advantages that: the mobile robot's multiple sensor information amalgamation method based on UKF provided by the invention, can significantly improve the autonomous bearing accuracy of mobile robot, strengthen the ability of the anti-interference autonomous location of mobile robot and navigation.
(4) description of drawings
Fig. 1 is schematic flow sheet of the present invention.
Fig. 2 is three limit localization method schematic diagrams.
Fig. 3 is UKF filtering algorithm calculation flow chart.
(5) embodiment
The present invention is further illustrated below in conjunction with the drawings and specific embodiments, and present embodiment comprises the following steps:
In conjunction with Fig. 1, odometer locating information and electronic compass azimuth information are carried out filtering and are obtained robot part azimuth information L slightly just in the present embodiment in UKF (A) filtering algorithm, and the WLAN locating information adopts three limit localization methods to obtain the preliminary overall elements of a fix W of robot; W and L are carried out the premultiplication translation matrix, realize that coordinate translation obtains further overall azimuth information WL of robot, adopt three limit localization methods to obtain locating information the RFID locating information, and carry out data Layer filtering with UKF (B) filtering algorithm and obtain accurate relatively more overall locating information R, the ultrasound wave locating information is carried out the filtering of UKF (A) filtering algorithm obtain the local azimuth information Z of barrier, W, L, R, Z carries out further filtering and realizes that the total information fusion obtains robot and the accurate azimuth information of the barrier overall situation, realizes the autonomous accurate location of mobile robot and map perception in UKF (B) filtering algorithm.
Fig. 2 is location, three limits ratio juris.The present invention is all effective to WLAN localization method and RFID localization method.Be example with the WLAN localization method: in rectangular coordinate system, mobile robot T at a time reads three wireless router M1, M2, M3, router is respectively d1, d2, d3 to the distance of robot, if the current coordinate of robot is (x, y), Simultaneous Equations can try to achieve robot coordinate (x, y).
Fig. 3 is UKF filtering algorithm workflow diagram.UKF (A) filtering algorithm does not have essence different with UKF (B) filtering algorithm, and unique difference is that in UKF (A) the filtering algorithm initialization procedure, state variable is that (s), wherein, x, y are robot current time coordinate for x, y, and s is the robot corner; And the init state variable of UKF (B) filtering algorithm be (x, y, X, Y), x wherein, y is robot current time coordinate, X, Y are the traveling speed of robot.It was 7 steps that the UKF filtering algorithm is divided into, and utilized the iterative computation of computing machine to realize progressively optimal estimation.Concrete steps are: at first filtering algorithm carries out initialization, comprises operations such as definition and initialization matrix; The second, determine system's dimension according to system state equation and observation equation, and calculate Sigma point and weight coefficient thereof; The 3rd, the Sigma point and the weight coefficient that obtain are changed, and computing time renewal equation; The 4th, according to the sensing data calculating observation vector renewal equation that obtains; The 5th, the calculation of filtered gain; The 6th, calculate covariance matrix; The 7th, current optimal State Estimation is exported and returned the calculating Sigma point stage and restart iterative computation, know system's location end.At this moment system optimal is estimated more and more near true value, reaches accurate location purpose, and has improved system reliability.
Claims (3)
1. mobile robot's multiple sensor information amalgamation method based on UKF is characterized in that it comprises following treatment step: mobile robot's odometer locating information and electronic compass azimuth information are carried out filtering by UKF (A) filtering algorithm obtain robot part azimuth information L slightly just; Adopt three limit localization methods processing to obtain the preliminary overall elements of a fix W of robot the locating information of WLAN (wireless local area network) WLAN; W and L are carried out coordinate transform obtain further overall azimuth information WL of robot; Adopt three limit localization methods to handle the RFID locating information and obtain locating information, and carry out data Layer filtering with UKF (B) filtering algorithm and obtain accurate relatively more overall locating information R, the ultrasound wave locating information is obtained the local azimuth information Z of barrier by the filtering of UKF (A) filtering algorithm, W, L, R, Z carry out the total information fusion by UKF (B) filtering algorithm and obtain robot and the accurate azimuth information of the barrier overall situation, realize the autonomous accurate location of mobile robot and map perception.
2. a kind of mobile robot's multiple sensor information amalgamation method based on UKF according to claim 1, it is characterized in that described UKF (A) filtering algorithm utilizes the iterative computation of computing machine to realize progressively optimal estimation, concrete steps are: at first filtering algorithm carries out initialization, comprises operations such as definition and initialization matrix; The second, determine system's dimension according to system state equation and observation equation, and calculate Sigma point and weight coefficient thereof; The 3rd, the Sigma point and the weight coefficient that obtain are changed, and computing time renewal equation; The 4th, according to the sensing data calculating observation vector renewal equation that obtains; The 5th, the calculation of filtered gain; The 6th, calculate covariance matrix; The 7th, current optimal State Estimation is exported and returned the calculating Sigma point stage and restart iterative computation, know system's location end; UKF (B) filtering algorithm and the difference of UKF (A) filtering algorithm are that in UKF (A) the filtering algorithm initialization procedure, state variable is that (s), wherein, x, y are robot current time coordinate for x, y, and s is the robot corner; And the init state variable of UKF (B) (7) be (x, y, X, Y), x wherein, y is robot current time coordinate, X, Y are the traveling speed of robot.
3. a kind of mobile robot's multiple sensor information amalgamation method based on UKF according to claim 2, it is characterized in that the described step that W and L are carried out coordinate transform for W and L are carried out the premultiplication translation matrix, realizes that coordinate translation obtains further overall azimuth information WL of robot.
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Cited By (10)
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| CN103529424A (en) * | 2013-10-23 | 2014-01-22 | 北京工商大学 | RFID (radio frequency identification) and UKF (unscented Kalman filter) based method for rapidly tracking indoor target |
| CN104035067A (en) * | 2014-06-13 | 2014-09-10 | 重庆大学 | Mobile robot automatic positioning algorithm based on wireless sensor network |
| CN104482933A (en) * | 2014-12-03 | 2015-04-01 | 北京航空航天大学 | Method for dead reckoning and WLAN (wireless local area network) integrated positioning based on particle filter |
| CN105258702A (en) * | 2015-10-06 | 2016-01-20 | 深圳力子机器人有限公司 | Global positioning method based on SLAM navigation mobile robot |
| CN106908762A (en) * | 2017-01-12 | 2017-06-30 | 浙江工业大学 | A kind of many hypothesis UKF method for tracking target for UHF rfid systems |
| CN107831405A (en) * | 2017-11-20 | 2018-03-23 | 北京国网富达科技发展有限责任公司 | A kind of infrared comprehensive detection device of distribution overhead line ultrasonic wave |
| CN110296706A (en) * | 2019-07-11 | 2019-10-01 | 北京云迹科技有限公司 | Localization method and device, robot for Indoor Robot |
| CN110780325A (en) * | 2019-08-23 | 2020-02-11 | 腾讯科技（深圳）有限公司 | Method and device for positioning moving object and electronic equipment |
| CN111136660A (en) * | 2020-02-19 | 2020-05-12 | 清华大学深圳国际研究生院 | Robot pose positioning method and system |
| CN111693044A (en) * | 2020-06-19 | 2020-09-22 | 南京晓庄学院 | Fusion positioning method |
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Cited By (17)
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| CN103529424B (en) * | 2013-10-23 | 2015-07-08 | 北京工商大学 | RFID (radio frequency identification) and UKF (unscented Kalman filter) based method for rapidly tracking indoor target |
| CN103529424A (en) * | 2013-10-23 | 2014-01-22 | 北京工商大学 | RFID (radio frequency identification) and UKF (unscented Kalman filter) based method for rapidly tracking indoor target |
| CN104035067A (en) * | 2014-06-13 | 2014-09-10 | 重庆大学 | Mobile robot automatic positioning algorithm based on wireless sensor network |
| CN104482933B (en) * | 2014-12-03 | 2017-12-29 | 北京航空航天大学 | A kind of method based on particle filter reckoning and WLAN integrated positioning |
| CN104482933A (en) * | 2014-12-03 | 2015-04-01 | 北京航空航天大学 | Method for dead reckoning and WLAN (wireless local area network) integrated positioning based on particle filter |
| CN105258702B (en) * | 2015-10-06 | 2019-05-07 | 深圳力子机器人有限公司 | A kind of global localization method based on SLAM navigator mobile robot |
| CN105258702A (en) * | 2015-10-06 | 2016-01-20 | 深圳力子机器人有限公司 | Global positioning method based on SLAM navigation mobile robot |
| CN106908762A (en) * | 2017-01-12 | 2017-06-30 | 浙江工业大学 | A kind of many hypothesis UKF method for tracking target for UHF rfid systems |
| CN106908762B (en) * | 2017-01-12 | 2019-10-22 | 浙江工业大学 | A kind of more hypothesis UKF method for tracking target for UHF-RFID system |
| CN107831405A (en) * | 2017-11-20 | 2018-03-23 | 北京国网富达科技发展有限责任公司 | A kind of infrared comprehensive detection device of distribution overhead line ultrasonic wave |
| CN107831405B (en) * | 2017-11-20 | 2024-05-28 | 北京国网富达科技发展有限责任公司 | Ultrasonic and infrared comprehensive detection device with grid overhead lines |
| CN110296706A (en) * | 2019-07-11 | 2019-10-01 | 北京云迹科技有限公司 | Localization method and device, robot for Indoor Robot |
| CN110780325A (en) * | 2019-08-23 | 2020-02-11 | 腾讯科技（深圳）有限公司 | Method and device for positioning moving object and electronic equipment |
| CN110780325B (en) * | 2019-08-23 | 2022-07-19 | 腾讯科技（深圳）有限公司 | Method and device for positioning moving object and electronic equipment |
| CN111136660A (en) * | 2020-02-19 | 2020-05-12 | 清华大学深圳国际研究生院 | Robot pose positioning method and system |
| CN111136660B (en) * | 2020-02-19 | 2021-08-03 | 清华大学深圳国际研究生院 | Robot pose positioning method and system |
| CN111693044A (en) * | 2020-06-19 | 2020-09-22 | 南京晓庄学院 | Fusion positioning method |
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