CN113218388B - Mobile robot positioning method and system considering variable colored measurement noise - Google Patents
Mobile robot positioning method and system considering variable colored measurement noise Download PDFInfo
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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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- G—PHYSICS
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- 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
- G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
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Abstract
The invention discloses a mobile robot positioning method and a system considering variable colored measurement noise, which comprises the following steps: taking the east and north positions and speeds of the mobile robot at the moment k as state quantities, and taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity to construct a filtering model; on the basis of expanding the finite impulse response filter, constructing m different sub-filters considering colored measurement noise according to different local filtering windows selected in an off-line stage; and fusing the constructed output of the sub-filters in an IMM mode to obtain the optimal position prediction of the mobile robot at the current moment, so as to realize the positioning of the mobile robot. The method constructs sub EFIR filters under different colored measurement noises, and fuses the outputs of the sub filters through an interactive multi-model algorithm to finally obtain the optimal position estimation of the mobile robot for the optimal UWB measurement.
Description
Technical Field
The invention relates to the technical field of combined positioning in a complex environment, in particular to a mobile robot positioning method and system considering variable colored measurement noise.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
Mobile robot navigation and positioning, as a basis for home service robots to provide high quality services to humans, currently face many challenges. Especially in indoor environment, the indoor layout, building materials and even space size of a building can influence navigation signals, and positioning accuracy is seriously influenced. Meanwhile, a relatively small platform of the home service robot facing the indoor environment also puts severe requirements on the volume, the precision and the cost of the navigation instrument. In an indoor environment, how to utilize the acquired limited information to eliminate the influence of an indoor complex navigation environment on the accuracy and the real-time performance of the acquisition of the navigation information of the service robot, and ensure the continuous and stable navigation precision of the service robot in the indoor environment has important scientific theoretical significance and practical application value.
Among the existing positioning methods, Global Navigation Satellite System (GNSS) is the most commonly used method. Although the GNSS can continuously and stably obtain the position information with high precision, the application range of the GNSS is limited by the defect that the GNSS is easily influenced by external environments such as electromagnetic interference and shielding, and particularly in some closed and environment-complex scenes such as indoor and underground roadways, GNSS signals are seriously shielded, and effective work cannot be performed.
In recent years, uwb (ultra wideband) has shown great potential in the field of short-distance local positioning due to its high positioning accuracy in a complex environment. Researchers have proposed the use of UWB-based target tracking for pedestrian navigation in GNSS-disabled environments. Although indoor positioning can be realized by the method, because the indoor environment is complicated and changeable, UWB signals are easily interfered to cause the reduction of positioning accuracy and even the unlocking; meanwhile, because the communication technology adopted by the UWB is generally a short-distance wireless communication technology, if a large-range indoor target tracking and positioning is to be completed, a large number of network nodes are required to complete together, which inevitably introduces a series of problems such as network organization structure optimization design, multi-node multi-cluster network cooperative communication, and the like. Therefore, at present, the UWB-based target tracking still faces many challenges in the field of indoor navigation, and particularly in the process of steering the mobile robot, Colored Measurement Noise (CMN) affects the position estimation accuracy of the mobile robot, but the existing technology cannot accurately predict the Colored Measurement Noise.
Disclosure of Invention
In order to solve the above problems, the present invention provides a mobile robot positioning method and system considering variable colored measurement noise, which improves a conventional Extended Finite Impulse Response (EFIR) filter to construct sub EFIR filters under different colored measurement noise in different colored noise ratios selected in an offline stage, and fuses outputs of the constructed sub EFIR filters by an interactive multi-model (IMM) method to finally obtain an optimal mobile robot position estimate at the current time.
In some embodiments, the following technical scheme is adopted:
a mobile robot positioning method taking into account variable colored measurement noise, comprising:
taking the east and north positions and speeds of the mobile robot at the moment k as state quantities, and taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity to construct a filtering model;
on the basis of expanding the finite impulse response filter, according to different local filtering windows selected in an off-line stage, m different sub-filters considering colored measurement noise are constructed, wherein m is a set value;
and fusing the constructed output of the sub-filters in an IMM mode to obtain the optimal position prediction of the mobile robot at the current moment, so as to realize the positioning of the mobile robot.
As a further improvement, the state equation of each sub-filter is specifically as follows:
wherein (x)k,yk)、(xk-1,yk-1) The positions of the east and north of the mobile robot at the moment k and the moment k-1 respectively;the speed of the mobile robot in the east direction and the north direction at the moment k and k-1 respectively; tau is a sampling period; w is akIs the system noise at time k.
As a further improvement, the observation equation of each sub-filter is specifically as follows:
wherein (x)i,yi),i∈[1,4]The position of the ith UWB reference node; v. ofkThe observed noise at the system k moment;and the observed distance between the mobile robot and the UWB reference node is measured by UWB at the time k.
As a further improvement, the number of different local filtering windows selected during the off-line phase is selected according to the requirements on the performance of the algorithm.
As a further improvement, the constructed sub-filters are fused in an IMM manner, and the specific process includes:
constructing a Markov transition probability matrix, and introducing a weight matrix on the basis of the Markov transition probability matrix;
calculating the weight of each sub-filter;
and obtaining the total output of the data fusion filter according to the weight of each sub-filter and the state vector prediction of the sub-filters.
As a further improvement, the weight of each sub-filter is calculated, and the specific process includes:
wherein the content of the first and second substances,the weighted value of the k moment obtained from the k-1 moment; i is the number of the sub-filters;is the weight value C of the current timek=Hk-γiHk-1F-1,γiIs the ratio of CMN in the ith sub-filter,for the error matrix at time k derived from time k-1, R observes the noise covariance, φk=QkγiHk-1F-1,QkAnd F is a system matrix.
As a further improvement, the total output of the data fusion filter is obtained, specifically:
wherein the content of the first and second substances,is the output of the main filter at the time k,The state of the sub-filter at time k is estimated.
In other embodiments, the following technical solutions are adopted:
a mobile robot positioning system that accounts for variable colored measurement noise, comprising:
the device is used for taking the east and north positions and speeds of the mobile robot k as state quantities, taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity, and constructing a filtering model;
means for constructing m different sub-filters considering colored measurement noise based on the extended finite impulse response filter according to different local filtering windows selected at the off-line stage;
and the device is used for fusing the outputs of the constructed sub-filters in an IMM mode to obtain the optimal position prediction of the mobile robot at the current moment and realize the positioning of the mobile robot.
In other embodiments, the following technical solutions are adopted:
a terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory is for storing a plurality of instructions adapted to be loaded by the processor and to perform the above-described mobile robot positioning method taking into account variable colored measurement noise.
In other embodiments, the following technical solutions are adopted:
a computer readable storage medium having stored therein a plurality of instructions adapted to be loaded by a processor of a terminal device and to perform the above described mobile robot positioning method taking into account variable colored measurement noise.
Compared with the prior art, the invention has the beneficial effects that:
in the invention, the proportion of colored measurement noise of the sub-EFIR filters is obtained through independent data in an off-line stage, the sub-EFIR filters under different colored measurement noise are constructed, and on the basis, the output of each sub-filter is fused through an interactive multi-model (IMM) algorithm to finally obtain the optimal position estimation of the mobile robot measured by the optimal UWB; the method can be used for realizing high-precision positioning in indoor environment.
Additional features and advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
FIG. 1 is a schematic structural diagram of a mobile robot positioning system in accordance with an embodiment of the present invention, in which variable colored measurement noise is taken into account;
fig. 2 is a flowchart of a mobile robot positioning method in consideration of variable colored measurement noise in an embodiment of the present invention.
Detailed Description
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application. As used herein, the singular forms "a", "an", and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
Example one
In one or more embodiments, disclosed is a mobile robot positioning method considering variable colored measurement noise, which is implemented based on the system shown in fig. 1, and referring to fig. 1, UWB includes a UWB reference node and a UWB positioning tag, the UWB reference node is fixed on a known coordinate in advance, and the UWB positioning tag is fixed on any mobile machine; an INS is primarily composed of an IMU secured to the foot of a target pedestrian. UWB acquires the distance information between the target pedestrian and the reference node at the current moment; the INS acquires distance information between the target pedestrian and the reference node at the current moment.
Referring to fig. 2, the mobile robot positioning method considering variable colored measurement noise specifically includes:
(1) taking the east and north positions and speeds of the mobile robot at the moment k as state quantities, and taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity to construct a filtering model;
(2) on the basis of the extended finite impulse response filter, m different sub-filters considering Colored Measurement Noise (CMN) are constructed according to different local filtering windows selected in an off-line stage, wherein m is a set value.
The number of different local filtering windows selected in the off-line stage is selected according to the requirement on the performance of the algorithm.
In this embodiment, the sub-filter is a sub-EFIR filter (sub-chefir filter) under colored measurement noise.
The output of the sub-cEFIR filter is the estimation of the sub-filter to the current time position of the mobile robot, and the input of the sub-cEFIR filter is the observation vector of each time.
The equation of state for the sub-chefir filter is:
wherein (x)k,yk)、(xk-1,yk-1) The positions of the east and north of the mobile robot at the moment k and the moment k-1 respectively;the speed of the mobile robot in the east direction and the north direction at the moment k and k-1; tau is a sampling period; w is akIs the system noise at time k.
The observed equation for the sub-chefir filter is:
wherein (x)i,yi),i∈[1,4]The position of the ith UWB reference node; v. ofkThe observed noise at the system k moment;and the observed distance between the mobile robot and the UWB reference node is measured by UWB at the time k.
The iterative algorithm for the sub-chefir filter is:
setting intermediate variables m and s:
mcE=k-NcE+1,s=mcE+McE-1
wherein N iscEIs the size of the local filter window of the sub-chefir filter; mcEIs the dimension of the state vector;
when the sampling time is more than NcEThen, setting the intermediate variable j to range from time s +1 to time k, the iteration performed by the sub-chefir filter is as follows:
zl=yl-γyl-1
Cl=Hl-γHlF-1
wherein F is a system matrix;andrespectively are intermediate quantities of state vector iteration at the time k and the time k-1;iterating the intermediate quantity for the state vector of the k moment obtained from the k-1 moment; gkGeneralized noise power gain at time k; y iskAn observation vector at the k moment;estimating a state vector for k time;to relate toA function of (a);
is an error matrix from k-1 to k times,Is the error matrix from the time k-1, Q is the system noise wkCovariance of (H)kThe observation equation at time j, RkTo observe the covariance of the noise.
Is the weight value C of the current timek=Hk-γiHk-1F-1、γiIs the ratio of CMN in the ith sub-filter,error matrix for k-time derived from k-1 time, R observed noise covariance,φk=QkγiHk-1F-1Wherein Q iskIs the covariance of the system noise at time k.
(3) Fusing the constructed output of the sub-filters in an IMM mode to obtain the optimal position estimation of the mobile robot of the UWB positioning system at the current moment;
the process of fusing the outputs of the constructed sub-filters in an IMM manner specifically comprises the following steps:
1) constructing a Markov transition probability matrix, and introducing a weight matrix on the basis;
2) calculating the weight of each sub-cEFIR filter; the specific calculation method is as follows:
wherein the content of the first and second substances,the weight value of the previous moment; i is the number of sub-chefir filters.
3) And obtaining the total output of the data fusion filter according to the weight of each sub-cEFIR filter and the state vector prediction of the sub-cEFIR filters.
The total output of the data fusion filter is specifically:
wherein the content of the first and second substances,is the output of the main filter at the time k,The state of the sub-filter at time k is estimated.
Example two
In one or more embodiments, a mobile robot positioning system is disclosed that considers variable colored measurement noise, specifically comprising:
the device is used for constructing a filtering model by taking the east and north positions and speeds of the mobile robot k as state quantities and taking the distance between the mobile robot and a UWB reference node measured by UWB as a system observed quantity;
means for constructing m different sub-filters considering colored measurement noise based on the extended finite impulse response filter according to different local filtering windows selected at the off-line stage;
and the device is used for fusing the outputs of the constructed sub-filters in an IMM mode to obtain the optimal position prediction of the mobile robot at the current moment and realize the positioning of the mobile robot.
It should be noted that specific implementation manners of the modules are already described in the first embodiment, and are not described again.
EXAMPLE III
In one or more implementations, a terminal device is disclosed that includes a server including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the mobile robot positioning method of example one that takes into account variable colored measurement noise when executing the program. For brevity, no further description is provided herein.
It should be understood that in this embodiment, the processor may be a central processing unit CPU, and the processor may also be other general purpose processors, digital signal processors DSP, application specific integrated circuits ASIC, off-the-shelf programmable gate arrays FPGA or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, and so on. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory may include both read-only memory and random access memory, and may provide instructions and data to the processor, and a portion of the memory may also include non-volatile random access memory. For example, the memory may also store device type information.
In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software.
The mobile robot positioning method considering the variable colored measurement noise in the first embodiment may be directly implemented by a hardware processor, or implemented by a combination of hardware and software modules in the processor. The software modules may be located in ram, flash, rom, prom, or eprom, registers, among other storage media as is well known in the art. The storage medium is located in a memory, and a processor reads information in the memory and completes the steps of the method in combination with hardware of the processor. To avoid repetition, it is not described in detail here.
Those of ordinary skill in the art will appreciate that the various illustrative elements, i.e., algorithm steps, described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
Although the embodiments of the present invention have been described with reference to the accompanying drawings, it is not intended to limit the scope of the invention, and it should be understood by those skilled in the art that various modifications and variations can be made without inventive changes in the technical solutions of the present invention.
Claims (8)
1. A mobile robot positioning method taking into account variable colored measurement noise, comprising:
taking the east and north positions and speeds of the mobile robot at the moment k as state quantities, and taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity to construct a filtering model;
on the basis of expanding the finite impulse response filter, according to different local filtering windows selected in an off-line stage, m different sub-filters considering colored measurement noise are constructed, wherein m is a set value;
fusing the constructed output of the sub-filters in an IMM mode to obtain the optimal position prediction of the mobile robot at the current moment, and realizing the positioning of the mobile robot;
wherein, the output of the constructed sub-filters is fused in an IMM mode, and the specific process comprises the following steps:
constructing a Markov transition probability matrix, and introducing a weight matrix on the basis;
calculating the weight of each sub-filter; the specific process comprises the following steps:
wherein the content of the first and second substances,the weighted value of the k moment obtained from the k-1 moment; i is the number of the sub-filters;is the weight value C of the current timek=Hk-γiHk-1F-1,HkIs the observation equation at time j, gammaiIs the ratio of CMN in the ith sub-filter,for the error matrix at time k derived from time k-1, R observes the noise covariance, φk=QkγiHk-1F-1,QkThe covariance of the system noise at the moment k is shown, and F is a system matrix;
and obtaining the total output of the data fusion filter according to the weight of each sub-filter and the state vector prediction of the sub-filters.
2. A method for mobile robot localization taking into account variable colored measurement noise according to claim 1, characterized in that the state equation of each sub-filter is embodied as:
wherein (x)k,yk)、(xk-1,yk-1) The positions of the east and north of the mobile robot at the moment k and the moment k-1 respectively;the speed of the mobile robot in the east direction and the north direction at the moment k and k-1 respectively; tau is a sampling period; w is akIs the system noise at time k.
3. A method for mobile robot localization taking into account variable colored measurement noise according to claim 1, characterized in that the observation equation of each sub-filter is embodied as:
4. A method for mobile robot localization taking into account variable colored measurement noise according to claim 1, characterized in that the number of different local filtering windows selected in the off-line phase is selected according to the need for algorithm performance.
5. A mobile robot localization method considering variable colored measurement noise according to claim 1, characterized by obtaining the total output of the data fusion filter, in particular:
6. A mobile robot positioning system that accounts for variable colored measurement noise, comprising:
the device is used for taking the east and north positions and speeds of the mobile robot k as state quantities, taking the distance between the mobile robot and a UWB reference node obtained by UWB measurement as a system observed quantity, and constructing a filtering model;
means for constructing m different sub-filters considering colored measurement noise based on the extended finite impulse response filter according to different local filtering windows selected at the off-line stage;
the device is used for fusing the constructed output of the sub-filters in an IMM mode to obtain the optimal position estimation of the mobile robot at the current moment and realize the positioning of the mobile robot;
wherein, the output of the constructed sub-filters is fused in an IMM mode, and the specific process comprises the following steps:
constructing a Markov transition probability matrix, and introducing a weight matrix on the basis;
calculating the weight of each sub-filter; the specific process comprises the following steps:
wherein the content of the first and second substances,the weighted value of the k moment obtained from the k-1 moment; i is the number of the sub-filters;is the weight value C of the current timek=Hk-γiHk-1F-1,HkIs the observation equation at time j, gammaiIs the ratio of CMN in the ith sub-filter,for the error matrix at time k derived from time k-1, R observes the noise covariance, φk=QkγiHk-1F-1,QkThe covariance of the system noise at the moment k is shown, and F is a system matrix;
and obtaining the total output of the data fusion filter according to the weight of each sub-filter and the state vector prediction of the sub-filters.
7. A terminal device comprising a processor and a memory, the processor being arranged to implement instructions; the memory for storing a plurality of instructions, characterized in that the instructions are adapted to be loaded by the processor and to perform the method of mobile robot positioning taking into account variable colored measurement noise according to any of claims 1-5.
8. A computer-readable storage medium, in which a plurality of instructions are stored, characterized in that said instructions are adapted to be loaded by a processor of a terminal device and to perform a mobile robot localization method taking into account a variable colored measurement noise according to any of claims 1-5.
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