CN104316058A - Interacting multiple model adopted WSN-INS combined navigation method for mobile robot - Google Patents
Interacting multiple model adopted WSN-INS combined navigation method for mobile robot Download PDFInfo
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- CN104316058A CN104316058A CN201410614772.4A CN201410614772A CN104316058A CN 104316058 A CN104316058 A CN 104316058A CN 201410614772 A CN201410614772 A CN 201410614772A CN 104316058 A CN104316058 A CN 104316058A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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Abstract
The invention belongs to the field of multi-sensor data fusion, and discloses an interacting multiple model adopted WSN-INS combined navigation method for a mobile robot. The interacting multiple model adopted WSN-INS combined navigation method is characterized in that two error models are used to perform modeling on the movement of the indoor mobile robot, and optimum estimation to the navigation parameter error of the mobile robot is obtained through interaction fusion of two groups of filters; the two groups of filters respectively perform modeling on the movement of the mobile robot in the state of a constant speed and in the state of uniform acceleration, the first group of filters take the position error and the speed error of the mobile robot in the two directions as state variables of the system; the second group of filters take the position error, the speed error and the acceleration error of the mobile robot in the two directions as state variables of the system; the two groups of filters take the position error, obtained through measuring resolving of the INS and WSN, of the mobile robot in the two directions as the observed quantity. According to the method, through fusion of smoothing results of the two groups of filters, the accuracy in the navigation parameter of the mobile robot is obviously improved.
Description
Technical field
The present invention relates to a kind of mobile robot WSN/INS Combinated navigation method adopting Interactive Multiple-Model (Interacting Multiple Model, IMM), belong to Fusion field.
Background technology
GPS (Global positioning systems, GPS) and inertial navigation system (Inertial navigation system, INS) are one of current most widely used navigational system.Wherein GPS can provide accurately, has the navigation information of continual and steady navigation accuracy, but under the environment such as urban district, mine, tunnel indoor, skyscraper is intensive, gps signal losing lock, can not position.Although INS have complete autonomous, movable information comprehensively, in short-term, high-precision advantage, although can realize independent navigation, error accumulates in time, will cause navigation accuracy degradation during long boat under service condition.Therefore, INS can only be short-term compensatory to the compensation of GPS navigation information, and the navigation accuracy of GPS/INS integrated navigation system the most conventional at present depends on the navigation accuracy of GPS, when the long-time losing lock of GPS, integrated navigation system cannot provide high-precision navigation information.
In recent years, wireless sensor network (Wireless Sensors Network, WSN) shows very large potentiality with the feature of its low cost, low-power consumption and low system complexity in short distance positioning field.WSN is without gps signal area, namely time so-called " blind area ", as under the environment such as urban district, mine, tunnel indoor, skyscraper is intensive, unknown node location provides possibility.But the communication technology adopted due to WSN is generally short-distance wireless communication technology (as ZigBee, WIFI etc.), if therefore thought the target following location of long distance, needs a large amount of network nodes jointly to complete, which increased the network burden of WSN.In addition, WSN can only provide position and velocity information, can not provide comprehensive movable information.
In order to obtain navigation information stable for a long time under GPS long losing lock environment, many scholars propose WSN location technology to be incorporated in the INS system of low cost, build WSN/INS integrated navigation system, as Southeast China University Y.Xu, although this array mode well solves the problem that long distance objective is followed the tracks of and navigator cost is high under the closed environment of underground, but due to existing low cost INS technology, make information that INS systematic survey obtains (as course angle, acceleration information) accuracy reduces greatly, add the error accumulation phenomenon of INS system itself, low cost INS technology is made to be difficult to provide stable navigation information.
Summary of the invention
Goal of the invention: in order to solve the situation of the cumulative errors that low cost INS occurs, the present invention proposes a kind of mobile robot WSN/INS Combinated navigation method adopting Interactive Multiple-Model (IMM), by using two kinds of motion models, modeling is carried out to the motion of indoor mobile robot, merge the filter result of two groups of wave filters, to improve the precision of Mobile Robotics Navigation parameter.
Technical scheme: the present invention adopts following technical scheme for solving its technical matters:
Adopt a mobile robot WSN/INS Combinated navigation method for Interactive Multiple-Model, comprise the following steps:
(1) at the uniform velocity with the INS resolution error in even acceleration two kinds of situations, modeling is being carried out to mobile robot, and then obtaining the error model under two states, be designated as model 1 and model 2 respectively;
(2) the mobile robot position in the two directions obtained is resolved in INS and WSN measurement and do difference, difference is input in IMM wave filter as the observation vector of Data Fusion Filtering device, IMM wave filter is estimated respectively by the state of model 1 and model 2 couples of mobile robots, the navigational parameter error of the mobile robot estimated by two kinds of error models is weighted and merges with the optimal estimation obtaining mobile robot's navigational parameter error under current motion state, the optimum navigation calculation error finally exporting current time mobile robot is estimated, and the navigational parameter that INS resolves is compensated, the optimum navigational parameter obtaining current time mobile robot is estimated.
Wherein, the INS resolution error model that model 1 is mobile robot at the uniform velocity situation, its state equation is:
In formula, δ x
k, δ y
kbe respectively k moment mobile robot site error in the two directions, δ v
x,k, δ v
y,kbe respectively k moment mobile robot velocity error in the two directions, δ x
k+1, δ y
k+1be respectively k+1 moment mobile robot site error in the two directions, δ v
x, k+1, δ v
y, k+1be respectively k+1 moment mobile robot velocity error in the two directions, T is the filtering cycle of wave filter, W=[ω
1ω
2]
tbe zero-mean, variance matrix is the gaussian random sequence of Q, and the acceleration noise of supposition on two coordinate directions is separate and have identical variance
The observation equation of model 1 is:
In formula,
be respectively k moment INS and resolve the mobile robot position in the two directions obtained,
be respectively k moment WSN and resolve the mobile robot position in the two directions obtained, V=[ν
x,kν
y,k]
tfor zero-mean, covariance matrix are the white noise of R, and uncorrelated with W,
be respectively the variance of the observation noise in both direction;
Model 2 is the INS resolution error model of mobile robot in even acceleration situation, and its state equation is:
In formula, δ a
x,k, δ a
y,kbe respectively k moment mobile robot acceleration error in the two directions, δ a
x, k+1, δ a
y, k+1be respectively k+1 moment mobile robot acceleration error in the two directions;
The observation equation of model 2 is:
Coordinate system described in above-mentioned steps may be defined as: with the barycenter of mobile robot for initial point, to point to local east orientation direction for x-axis, to point to local north orientation direction for y-axis, to point to local sky to direction for z-axis.
Beneficial effect of the present invention is as follows:
1, the requirement of the locating and orienting of low precision in ground urban transportation, long and narrow tunnel, small intelligent robot etc. can be met.
2, in order to solve the situation of the cumulative errors that low cost INS occurs, the present invention proposes a kind of mobile robot WSN/INS Combinated navigation method adopting Interactive Multiple-Model, solving parameter and model uncertainty problem preferably by adopting the Kalman filter of two parallel computations.The present invention uses two kinds of models to carry out modeling to the error of mobile robot, with the error model of matching shift robot under different motion state, the navigational parameter error of the mobile robot estimated by two models is weighted the optimal estimation of merging to obtain mobile robot's navigational parameter under current motion state, the optimum navigation calculation error finally exporting current time mobile robot is estimated, and the navigational parameter that INS resolves is compensated, the optimum navigational parameter obtaining current time mobile robot is estimated.The method, by the filter result of fusion two groups of wave filters, makes the precision of Mobile Robotics Navigation parameter be significantly improved.
Accompanying drawing explanation
Fig. 1 is the system schematic of the mobile robot WSN/INS Combinated navigation method for adopting Interactive Multiple-Model (IMM).
Fig. 2 is the schematic diagram of the mobile robot WSN/INS Combinated navigation method for adopting Interactive Multiple-Model (IMM).
Fig. 3 is the system flowchart of the mobile robot WSN/INS Combinated navigation method for adopting Interactive Multiple-Model (IMM).
Embodiment
Below in conjunction with accompanying drawing, the invention is described in further details.
As shown in Figure 1, a kind of system of the interactive multimode mode filter for WSN/INS integrated navigation, comprises WSN system, INS navigation module, central data processing module composition.Above-mentioned module is arranged on respectively with reference to (RN) node section and unknown (BN) node two parts.Wherein, reference mode part is made up of the wireless network receiver module (comprising ultrasound measurement module and time synchronized module) of WSN system; The wireless network main control module of WSN system, INS navigation module and central data processing module are arranged in unknown node jointly.
As shown in Figure 2, mobile robot (east orientation and the north orientation) position in the two directions obtained is resolved in INS and WSN measurement and does difference, difference is input in IMM wave filter as the observation vector of Data Fusion Filtering device, IMM wave filter is estimated respectively by the state of model 1 and model 2 couples of mobile robots, and the Mobile Robotics Navigation parameter error estimated by two kinds of error models carries out the optimal estimation merging to obtain mobile robot's navigational parameter error under current motion state.The optimum navigation calculation error finally exporting current time mobile robot is estimated, and compensates the navigational parameter that INS resolves, and the optimum navigational parameter obtaining current time mobile robot is estimated.
Wherein, the INS resolution error model that model 1 is mobile robot at the uniform velocity situation, its state equation is:
In formula, δ x
k, δ y
kbe respectively the site error of k moment mobile robot on east orientation and north orientation, δ v
x,k, δ v
y,kbe respectively the velocity error of k moment mobile robot on east orientation and north orientation, T is the filtering cycle of wave filter, W=[ω
1ω
2]
tbe zero-mean, variance matrix is the gaussian random sequence of Q, and the acceleration noise of supposition on two coordinate directions is separate and have identical variance
The observation equation of model 1 is:
In formula,
be respectively k moment INS and resolve the position of mobile robot on east orientation and north orientation obtained,
be respectively k moment WSN and resolve the position of mobile robot on east orientation and north orientation obtained, V=[ν
x,kν
y,k]
tfor zero-mean, covariance matrix are the white noise of R, and uncorrelated with W;
Model 2 is the INS resolution error model of mobile robot in even acceleration situation, and its state equation is:
In formula, δ a
x,k, δ a
y,kbe respectively the acceleration error of k moment mobile robot on east orientation and north orientation;
The observation equation of model 2 is:
A kind of working-flow of the interactive multimode mode filter for WSN/INS integrated navigation is as shown in Figure 3: in the process of moveable robot movement, and the angular velocity recorded by self-contained inertial sensor on the one hand and acceleration information carry out attitude, speed and location updating; On the other hand, mobile robot measures the distance of self and the known ultrasound wave node of surrounding with fixed cycle T by self-contained ultrasonic sensor, utilizes least-squares algorithm to resolve to obtain the current location of mobile robot; Mobile robot (east orientation and the north orientation) position in the two directions obtained is resolved in INS and WSN measurement and does difference, difference is input in IMM wave filter as the observation vector of Data Fusion Filtering device, the optimum navigation calculation error that IMM wave filter exports current time mobile robot is estimated, and the navigational parameter that INS resolves is compensated, the optimum navigational parameter obtaining current time mobile robot is estimated.
Claims (3)
1. adopt a mobile robot WSN/INS Combinated navigation method for Interactive Multiple-Model (IMM), it is characterized in that comprising the following steps:
(1) at the uniform velocity with the INS resolution error in even acceleration two kinds of situations, modeling is being carried out to mobile robot, and then obtaining the error model under two states, be designated as model 1 and model 2 respectively;
(2) the mobile robot position in the two directions obtained is resolved in INS and WSN measurement and do difference, difference is input in IMM wave filter as the observation vector of Data Fusion Filtering device, IMM wave filter is estimated respectively by the state of model 1 and model 2 couples of mobile robots, the navigational parameter error of the mobile robot estimated by two kinds of error models is weighted and merges with the optimal estimation obtaining mobile robot's navigational parameter error under current motion state, the optimum navigation calculation error finally exporting current time mobile robot is estimated, and the navigational parameter that INS resolves is compensated, the optimum navigational parameter obtaining current time mobile robot is estimated.
2. the mobile robot WSN/INS Combinated navigation method of employing Interactive Multiple-Model according to claim 1, is characterized in that, the INS resolution error model that described model 1 is mobile robot at the uniform velocity situation, and its state equation is:
In formula 1, δ x
k, δ y
kbe respectively k moment mobile robot site error in the two directions, δ v
x,k, δ v
y,kbe respectively k moment mobile robot velocity error in the two directions, δ x
k+1, δ y
k+1be respectively k+1 moment mobile robot site error in the two directions, δ v
x, k+1, δ v
y, k+1be respectively k+1 moment mobile robot velocity error in the two directions, T is the filtering cycle of wave filter, W=[ω
1ω
2]
tbe zero-mean, variance matrix is the gaussian random sequence of Q, and supposition acceleration noise is in the two directions separate and have identical variance
The observation equation of model 1 is:
In formula 2,
be respectively k moment INS and resolve the mobile robot position in the two directions obtained,
be respectively k moment WSN and resolve the mobile robot position in the two directions obtained, V=[ν
x,kν
y,k]
tfor zero-mean, covariance matrix are the white noise of R, and uncorrelated with W,
be respectively the variance of the observation noise in both direction.
3. the mobile robot WSN/INS Combinated navigation method of employing Interactive Multiple-Model according to claim 1, is characterized in that, described model 2 is the INS resolution error model of mobile robot in even acceleration situation, and its state equation is:
In formula 3, δ x
k, δ y
kbe respectively k moment mobile robot site error in the two directions, δ v
x,k, δ v
y,kbe respectively k moment mobile robot velocity error in the two directions, δ a
x,k, δ a
y,kbe respectively k moment mobile robot acceleration error in the two directions, δ x
k+1, δ y
k+1be respectively k+1 moment mobile robot site error in the two directions, δ v
x, k+1, δ v
y, k+1be respectively k+1 moment mobile robot velocity error in the two directions, δ a
x, k+1, δ a
y, k+1be respectively k+1 moment mobile robot acceleration error in the two directions, T is the filtering cycle of wave filter, W=[ω
1ω
2]
tbe zero-mean, variance matrix is the gaussian random sequence of Q, and supposition acceleration noise is in the two directions separate and have identical variance
The observation equation of model 2 is:
In formula 4,
be respectively k moment INS and resolve the mobile robot position in the two directions obtained,
be respectively k moment WSN and resolve the mobile robot position in the two directions obtained, V=[ν
x,kν
y,k]
tfor zero-mean, covariance matrix are the white noise of R, and uncorrelated with W,
2 variances being respectively the observation noise in both direction.
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