CN110824423B - Multi-unmanned vehicle collaborative navigation positioning method and system - Google Patents
Multi-unmanned vehicle collaborative navigation positioning method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for collaborative navigation and positioning of multiple unmanned vehicles. The method comprises the following steps: acquiring a Bluetooth signal carrying position information sent by surrounding unmanned vehicles; analyzing signal strength, source angle and position information from the Bluetooth signal; the source angle is a deflection angle of a connecting line of the peripheral unmanned vehicle and the current unmanned vehicle relative to the course of the current unmanned vehicle; calculating the deflection angle of a connecting line between the peripheral unmanned vehicle and the current unmanned vehicle according to the source angle to obtain a source signal deflection angle; and updating the position of the current unmanned vehicle by adopting a Kalman filtering algorithm according to the signal intensity, the source signal deflection angle and the position information to obtain a positioning result. The multi-unmanned vehicle collaborative navigation positioning method and system can improve the precision of collaborative navigation.
Description
Technical Field
The invention relates to the field of navigation positioning, in particular to a multi-unmanned vehicle collaborative navigation positioning method and system.
Background
In the 70's of the 20 th century, physical scientists harken a synergy theory that indicated that if subsystems within a system could coordinate with each other, a synergistic effect of "1 +1> 2" could be produced. Therefore, the collaborative navigation technology is concerned by many researchers as a new research direction, and the information interaction among the multi-agent sensors is utilized through the collaborative navigation technology to realize the information collaboration of the multi-agent sensors, so that the navigation positioning precision of the multi-agent sensors is improved, the errors of the navigation sensors are made up, the possible faults are identified, isolated and recovered, and the task execution capacity of the multi-agent sensors is guaranteed. At present, the collaborative navigation technology is rapidly developed and widely applied to the military field, such as the united combat of various military species and the swarm combat. Meanwhile, the technology is also developing to civilization, such as collaborative light show of unmanned aerial vehicle clusters, unmanned aerial vehicle express delivery service, multi-unmanned vehicle detection search and the like.
Formal research on collaborative navigation began at the end of the 20 th century and its application platforms were quite extensive, including underwater unmanned vehicles, ground unmanned vehicles, satellites, and so on. In the field of navigation, most of the existing navigation and Positioning sensors rely on assistance of a wireless Positioning System such as a satellite navigation System (e.g., GPS (Global Positioning System), Beidou), and satellite signals are highly likely to be unavailable or unreliable due to environmental time-varying property, dynamics and randomness. And the collaborative navigation effectively guarantees the robustness and accuracy of navigation through information collaboration and sharing among multiple sensors. Due to the many advantages of collaborative navigation, the extensive attention of researchers has been gained.
The conventional unmanned vehicle cooperative navigation system is implemented based on ranging information, that is, the cooperative navigation system has a set of corresponding distance measurement systems (currently, the distance measurement systems are generally radio-based measurement systems, such as Ultra Wide Band (UWB), Dedicated Short Range Communications (DSRC), Zigbee (Zigbee), and the like), and can measure the relative distance information between the unmanned vehicles in real time, and the distance information is fused with the own navigation information, so as to achieve the purpose of improving the navigation positioning accuracy. However, with the development of clustering, the number of unmanned vehicles participating in clustering is increasing, and the requirements on formation maintenance, collision avoidance and the like are increasing, so that the precision requirement on collaborative navigation is increasing, and a collaborative navigation system based on distance measurement information can not meet the requirements gradually.
Disclosure of Invention
The invention aims to provide a method and a system for collaborative navigation positioning of multiple unmanned vehicles, which improve the precision of collaborative navigation.
In order to achieve the purpose, the invention provides the following scheme:
a multi-unmanned vehicle collaborative navigation positioning method comprises the following steps:
acquiring a Bluetooth signal carrying position information sent by surrounding unmanned vehicles;
analyzing signal strength, source angle and the position information from the Bluetooth signal; the source angle is a deflection angle of a connecting line of the peripheral unmanned vehicle and the current unmanned vehicle relative to the course of the current unmanned vehicle;
calculating the deflection angle of a connecting line between the peripheral unmanned vehicle and the current unmanned vehicle according to the source angle to obtain a source signal deflection angle;
and updating the position of the current unmanned vehicle by adopting a Kalman filtering algorithm according to the signal intensity, the source signal deflection angle and the position information to obtain a positioning result.
Optionally, the calculating, according to the source angle, a deflection angle of a connection line between a peripheral unmanned vehicle and a current unmanned vehicle to obtain a source signal deflection angle specifically includes:
acquiring a course angle of the current unmanned vehicle; the course angle of the current unmanned vehicle is an included angle of the head direction of the current unmanned vehicle relative to a preset direction;
and calculating the source signal deflection angle according to the course angle of the current unmanned vehicle and the source angle.
Optionally, the current position of the unmanned vehicle is updated by using a kalman filter algorithm according to the signal strength, the source signal deflection angle and the position information, so as to obtain a positioning result, where the method specifically includes:
screening values of which the signal intensity respectively meets a first intensity interval and a second intensity interval from the signal intensities to obtain a first signal intensity and a second signal intensity; the first intensity interval is an intensity range in which the signal intensity and the transmission distance have a linear relation; the second intensity interval is a signal intensity range after the transmission distance exceeds a preset distance; the minimum value of the first intensity interval is greater than or equal to the maximum value of the second intensity interval;
establishing a Kalman filtering model by taking the first signal intensity, the position information and the source signal deflection angle as observed quantities and taking a position estimation value of the current unmanned vehicle as a state variable;
based on the Kalman filtering model, performing filtering processing by adopting an extended Kalman filtering algorithm to obtain a preliminary estimated position of the current unmanned vehicle;
judging whether the distances from the preliminary estimated position to the surrounding unmanned vehicles corresponding to the second signal strengths are all larger than the preset distance to obtain a judgment result;
if the judgment result shows that the position is the current unmanned vehicle, determining the initial estimated position as a positioning result of the current unmanned vehicle;
and if the judgment result shows that the unmanned vehicles are not located, performing secondary planning by taking the fact that the distances from the current position of the unmanned vehicle to the surrounding unmanned vehicles corresponding to the second signal strengths are all larger than the preset distance as a limiting condition, and determining the result of the secondary planning as the positioning result of the current unmanned vehicle.
Optionally, performing secondary planning by using the limit condition that the distance from the current position of the unmanned vehicle to the surrounding unmanned vehicles corresponding to the second signal strengths is greater than the preset distance, and determining that the result of the secondary planning is the positioning result of the current unmanned vehicle specifically includes:
to be provided with
As a performance index for quadratic programming to
Performing secondary planning as a limiting condition of the secondary planning, determining an output result which enables the performance index to be minimum, and obtaining a positioning result of the current unmanned vehicle; wherein J (x) is a quadratic performance index; x is an independent variable of the quadratic programming,
for preliminary position estimation, Pk is
A corresponding error covariance matrix; x (1) and x (2) are x-axis coordinates and y-axis coordinates of the unmanned vehicle position contained in the independent variable x;
x-axis coordinates of surrounding unmanned vehicles corresponding to the n second signal intensities at the current moment k respectively,
and d, respectively representing the y-axis coordinate of the peripheral unmanned vehicle corresponding to the n second signal intensities at the current moment k, wherein dmax is a preset distance.
A multi-unmanned vehicle collaborative navigation positioning system comprises:
the acquisition module is used for acquiring Bluetooth signals carrying position information and sent by surrounding unmanned vehicles;
the analysis module is used for analyzing the signal intensity, the source angle and the position information from the Bluetooth signal; the source angle is a deflection angle of a connecting line of the peripheral unmanned vehicle and the current unmanned vehicle relative to the course of the current unmanned vehicle;
the angle conversion module is used for calculating the deflection angle of a connecting line between the peripheral unmanned vehicle and the current unmanned vehicle according to the source angle to obtain a source signal deflection angle;
and the filtering module is used for updating the position of the current unmanned vehicle by adopting a Kalman filtering algorithm according to the signal intensity, the source signal deflection angle and the position information to obtain a positioning result.
Optionally, the angle conversion module includes:
the course angle acquisition unit is used for acquiring the course angle of the current unmanned vehicle; the course angle of the current unmanned vehicle is an included angle of the head direction of the current unmanned vehicle relative to a preset direction;
and the deflection angle calculation unit is used for calculating the source signal deflection angle according to the current course angle of the unmanned vehicle and the source angle.
Optionally, the filtering module includes:
the signal intensity screening unit is used for screening values of which the signal intensity respectively meets a first intensity interval and a second intensity interval from the signal intensities to obtain a first signal intensity and a second signal intensity; the first intensity interval is an intensity range in which the signal intensity and the transmission distance have a linear relation; the second intensity interval is a signal intensity range after the transmission distance exceeds a preset distance; the minimum value of the first intensity interval is greater than or equal to the maximum value of the second intensity interval;
the filter model establishing unit is used for establishing a Kalman filter model by taking the first signal intensity, the position information and the source signal deflection angle as observed quantities and taking a position estimation value of the current unmanned vehicle as a state variable;
the filtering unit is used for performing filtering processing by adopting an extended Kalman filtering algorithm based on the Kalman filtering model to obtain a preliminary estimated position of the current unmanned vehicle;
the judging unit is used for judging whether the distances from the preliminary estimated position to the surrounding unmanned vehicles corresponding to the second signal strengths are all larger than the preset distance to obtain a judgment result;
a first judgment result execution unit, configured to determine that the preliminary estimated position is a positioning result of the current unmanned vehicle if the judgment result indicates yes;
and the second judgment result execution unit is used for performing secondary planning by taking the distance from the current unmanned vehicle to the surrounding unmanned vehicles corresponding to the second signal strengths as a limiting condition if the judgment result indicates no, and determining the result of the secondary planning as the positioning result of the current unmanned vehicle.
Optionally, the second determination result executing unit includes:
a quadratic programming subunit for
As a performance index for quadratic programming to
Performing secondary planning as a limiting condition of the secondary planning, determining an output result which enables the performance index to be minimum, and obtaining a positioning result of the current unmanned vehicle; wherein J (x) is a quadratic performance index; x is an independent variable of the quadratic programming,
for preliminary position estimation, Pk is
A corresponding error covariance matrix; x (1) and x (2) are x-axis coordinates and y-axis coordinates of the unmanned vehicle position contained in the independent variable x;
x-axis coordinates of surrounding unmanned vehicles corresponding to the n second signal intensities at the current moment k respectively,
and d, respectively representing the y-axis coordinate of the peripheral unmanned vehicle corresponding to the n second signal intensities at the current moment k, wherein dmax is a preset distance.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects: the method and the system for the collaborative navigation and positioning of the multiple unmanned vehicles introduce the signal source angle information of the surrounding unmanned vehicles as the navigation and positioning factor, thereby improving the navigation and positioning precision. Meanwhile, in the navigation positioning process, the signal intensity sensitive to the distance and the signal intensity with the distance exceeding the preset distance are screened out, the signal intensity sensitive to the distance is used for Kalman filtering, and the signal intensity with the distance exceeding the preset distance is used for quadratic programming, so that Kalman filtering is performed by using data with high confidence level, the navigation positioning precision is improved, and the navigation positioning precision is further improved through quadratic programming.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a schematic flow chart of the cooperative navigation positioning of the present invention;
fig. 2 is a flowchart of a method of a multi-unmanned vehicle collaborative navigation positioning method according to embodiment 1 of the present invention;
FIG. 3 is a schematic view of an angular relationship;
FIG. 4 is a flow chart of the collaborative navigation algorithm of the present invention;
FIG. 5 is a diagram showing a variation relationship between a relative distance of Bluetooth and an RSSI value;
fig. 6 is a graph showing a division result obtained by dividing the curve of fig. 5.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
FIG. 1 is a schematic flow chart of the collaborative navigation positioning according to the present invention.
Referring to fig. 1, the core concept of the present invention is briefly introduced as follows: all install bluetooth module (bluetooth module contains the transmit-receive function, and in order to avoid sheltering from, installs bluetooth module at unmanned vehicle top as far as possible) on whole unmanned vehicle, in every bluetooth communication cycle, externally send self current positioning result, receive the positioning result that unmanned vehicle (with self unmanned vehicle existence communication relation) sent around the while, wherein, to unmanned vehicle self, the bluetooth information that receives contains three usable information: 1. position information of surrounding unmanned vehicles (obtained from the received bluetooth information content); 2. the Signal Strength (RSSI) of the currently Received bluetooth Signal; 3. the source angle of the currently received bluetooth signal. The three parts of information are used as information input and provided for the self-adaptive collaborative navigation algorithm of the scheme to carry out collaborative navigation operation, so that the aim of improving the navigation positioning precision of the user is fulfilled. The algorithm core idea of the self-adaptive collaborative navigation algorithm is as follows: the filtering algorithm performs filtering strategy adaptive selection according to the received RSSI value, and is divided into the following three conditions: 1, the RSSI value is overlarge; the RSSI value is located in a linear region; and 3, the RSSI value is too small. For the three situations, different strategies are adopted for processing respectively in a self-adaptive mode, and the specific strategies are as follows: 1. when the RSSI value is too large: because the relative distance between the unmanned vehicles has a theoretical minimum value (the vehicles cannot collide), and the RSSI value is in inverse proportion to the relative distance, namely the RSSI value has a theoretical maximum value, if the RSSI value is larger than the theoretical maximum value, the RSSI value is ignored as error information, and for the Bluetooth information, only the position information and the source angle are used for filtering fusion calculation; 2. when the RSSI value is in the linear region: in the functional relationship between the RSSI value and the relative distance, there is a section in which the RSSI value and the relative distance are approximately linear, and the RSSI value is very sensitive to the change of the distance, when the RSSI value is in the section, the RSSI value is used as the observation information to participate in the filtering calculation, that is: for the Bluetooth information, filtering and fusing position information, the source angle of the received Bluetooth signal and the RSSI value of the received Bluetooth signal; 3. when the RSSI value is too small: in the functional relationship between the RSSI value and the relative distance, when the distance is large, the change of the distance has very limited influence on the RSSI, namely the RSSI value is not sensitive to the change of the relative distance any more, and the estimation of the distance by using the RSSI value is very inaccurate, so that the RSSI value cannot be used as observation information to participate in filtering calculation, and in this case, inequality constraint information is constructed by using the RSSI value; namely: for the Bluetooth information, filtering fusion is carried out by using position information and the source angle of the received Bluetooth signal, a fusion result is used as an initial value, an inequality constraint is used as a limiting condition, and a quadratic programming algorithm is utilized to further seek an optimal fusion result. By the filtering fusion strategy of the self-adaptive selection, the RSSI value can be introduced into the collaborative navigation algorithm under the condition of high RSSI confidence coefficient, so that the aim of improving the navigation positioning precision is fulfilled; and when the confidence of the RSSI value is insufficient, the RSSI value is isolated outside the collaborative navigation algorithm, so that the RSSI value with low confidence is prevented from influencing the navigation positioning precision. In addition, due to the fact that the measurement information of the relative angle is introduced into the scheme, compared with the traditional collaborative navigation algorithm based on the ranging information only, the collaborative navigation positioning accuracy is further improved.
Example 1:
fig. 2 is a flowchart of a method for collaborative navigation and positioning of multiple unmanned vehicles according to embodiment 1 of the present invention.
Referring to fig. 2, the method for collaborative navigation and positioning of multiple unmanned vehicles includes:
step 101: and acquiring Bluetooth signals carrying position information sent by surrounding unmanned vehicles.
Step 102: analyzing signal strength, source angle and the position information from the Bluetooth signal; the source angle is a deflection angle of a connecting line of the surrounding unmanned vehicle and the current unmanned vehicle relative to the course of the current unmanned vehicle.
Step 103: and calculating the deflection angle of a connecting line between the peripheral unmanned vehicle and the current unmanned vehicle according to the source angle to obtain a source signal deflection angle.
Step 104: and updating the position of the current unmanned vehicle by adopting a Kalman filtering algorithm according to the signal intensity, the source signal deflection angle and the position information to obtain a positioning result.
As an optional implementation manner, step 102 specifically includes:
acquiring a course angle of the current unmanned vehicle; and the course angle of the current unmanned vehicle is an included angle of the head direction of the current unmanned vehicle relative to a preset direction.
And calculating the source signal deflection angle according to the course angle of the current unmanned vehicle and the source angle.
As an optional implementation manner, step 104 specifically includes:
screening values of which the signal intensity respectively meets a first intensity interval and a second intensity interval from the signal intensities to obtain a first signal intensity and a second signal intensity; the first intensity interval is an intensity range in which the signal intensity and the transmission distance have a linear relation; the second intensity interval is a signal intensity range after the transmission distance exceeds a preset distance; the minimum value of the first intensity interval is greater than or equal to the maximum value of the second intensity interval.
And establishing a Kalman filtering model by taking the first signal intensity, the position information and the source signal deflection angle as observed quantities and taking a position estimation value of the current unmanned vehicle as a state variable.
And based on the Kalman filtering model, performing filtering processing by adopting an extended Kalman filtering algorithm to obtain the initial estimated position of the current unmanned vehicle.
And judging whether the distances from the preliminary estimated position to the surrounding unmanned vehicles corresponding to the second signal strengths are all larger than the preset distance to obtain a judgment result.
And if the judgment result shows that the position is the current unmanned vehicle, determining the initial estimated position as the positioning result of the current unmanned vehicle.
And if the judgment result shows that the unmanned vehicles are not located, performing secondary planning by taking the fact that the distances from the current position of the unmanned vehicle to the surrounding unmanned vehicles corresponding to the second signal strengths are all larger than the preset distance as a limiting condition, and determining the result of the secondary planning as the positioning result of the current unmanned vehicle.
As an optional implementation, the quadratic programming process specifically includes:
to be provided with
As a performance index for quadratic programming to
Performing secondary planning as a limiting condition of the secondary planning, determining an output result which enables the performance index to be minimum, and obtaining a positioning result of the current unmanned vehicle; wherein J (x) is a quadratic performance index; x is an independent variable of the quadratic programming,
for preliminary position estimation, Pk is
Corresponding errorA difference covariance matrix; x (1) and x (2) are x-axis coordinates and y-axis coordinates of the unmanned vehicle position contained in the independent variable x;
x-axis coordinates of surrounding unmanned vehicles corresponding to the n second signal intensities at the current moment k respectively,
and d, respectively representing the y-axis coordinate of the peripheral unmanned vehicle corresponding to the n second signal intensities at the current moment k, wherein dmax is a preset distance.
Example 2:
in this embodiment 2, the unmanned vehicles participating in the collaborative navigation are all equipped with bluetooth transmission modules having transceiving functions, and simultaneously, the unmanned vehicles all have autonomous navigation capabilities (for example, equipped with MEMS gyroscopes, MEMS accelerometers, magnetic compasses, code discs, etc.).
All unmanned vehicles have the following functions: the system comprises a Bluetooth communication capability and an autonomous navigation capability, wherein the Bluetooth communication is used for information transmission between the unmanned vehicles, calculation of relative angles between the unmanned vehicles and measurement of Bluetooth RSSI values between the unmanned vehicles, and the autonomous navigation is used for autonomous updating of self poses of the unmanned vehicles. In addition, in order to suppress the influence of communication error/noise/multipath effect as much as possible, the bluetooth transmission module should be disposed on the top of the unmanned vehicle as much as possible, so that the unmanned vehicle can communicate with other unmanned vehicles with as little signal blocking/diffraction as possible.
The initial position of the unmanned vehicle is set manually or provided by other navigation systems, and the GPS may be used as one of the options, but not the only option, and other navigation devices may be used to give the initial position of the unmanned vehicle, such as UWB navigation systems.
The autonomous navigation means that the unmanned vehicle autonomously updates navigation information (mainly position information) of the unmanned vehicle by using navigation equipment loaded by the unmanned vehicle on the premise of not receiving information of other unmanned vehicles.
In this example 2, five types of unmanned vehicles a, b 1-bl (totally one unmanned vehicle under the same type) and c 1-cm (totally m unmanned vehicles under the same type) are definedUnmanned vehicles), d 1-dn (n unmanned vehicles under the same category), e 1-ep (p unmanned vehicles under the same category), wherein the unmanned vehicle a is an unmanned vehicle waiting for positioning, the unmanned vehicle a can perform data communication with the unmanned vehicles b 1-bl, c 1-cm and d 1-dn through Bluetooth communication, and cannot perform data communication with the unmanned vehicles e 1-ep, and the Bluetooth signal intensities of the unmanned vehicles b 1-bl, c 1-cm and d 1-dn which can be received on the unmanned vehicle a are respectively: RSSIb1～RSSIbl、RSSIc1～RSSIcm、RSSId1～RSSIdnThe Bluetooth signal source angles of the unmanned vehicles b 1-bl, c 1-cm and d 1-dn which can be received by the unmanned vehicle a are respectively as follows:
in addition, since the unmanned vehicle a can perform data communication with the unmanned vehicles b1 to bl, c1 to cm, and d1 to dn, the information that can be used is: and the position information of the unmanned vehicle at the moment k: position of unmanned vehicle a at time k
Position information of unmanned vehicles b 1-bl at time k
Position information of unmanned vehicle c 1-cm at time k
Position information of unmanned vehicles d 1-dn at time k
Bluetooth signal strength information: bluetooth signal strength information RSSI of unmanned vehicles b 1-bl at time kb1(k)～RSSIbl(k) And the RSSI of the Bluetooth signal strength information of the unmanned vehicle c 1-cm at the moment kc1(k)～RSSIcm(k) And the Bluetooth signal strength information RSSI of the unmanned vehicles d 1-dn at the moment kd1(k)～RSSIdn(k) (ii) a Bluetooth signal source angle information: bluetooth signal source angle of unmanned vehicles b 1-bl at time k
Bluetooth signal source angle of unmanned vehicle c 1-cm at time k
Bluetooth signal source angle of unmanned vehicles d 1-dn at time k
Are all the coordinates of the X axis,
are all Y-axis coordinates.
The present invention is directed to an unmanned vehicle, which focuses only on the position in the horizontal direction, and therefore does not need to consider its height information. The Bluetooth signal strength information and the Bluetooth signal source angle information are provided by a Bluetooth related device bottom chip.
The invention needs to compare the source angle information of the Bluetooth signal:
further processing will be described by taking the unmanned vehicle b1 as an example. FIG. 3 is a schematic view of an angular relationship. Defining the heading angle of the unmanned vehicle a at the moment k as psia(k) (the heading angle is defined as follows: firstly, the relevant personnel completes the setting of the X axis and the Y axis of the navigation coordinate system (the X axis and the Y axis can be determined according to the specific using environment, can be the east direction and the north direction in geography, can also be the direction along a certain direction under the indoor environment, as long as the X axis and the Y axis are ensured to be orthogonal), the heading angle is defined as the included angle between the advancing direction of the current unmanned vehicle and the positive direction of the Y axis, the positive deviation of the positive direction of the Y axis to the positive direction of the X axis is positive, and the range of the heading angle is-180 degrees), then the geometric relation between the unmanned vehicle a and the unmanned vehicle b1 is taken as an example for explanation, wherein the dotted line with an arrow is taken as an exampleRepresenting the advancing direction of the unmanned vehicle, and the dot-dash line represents the connecting line of the centers of the two vehicles, so that the included angle theta of the connecting line of the centers of the two vehicles can be obtained according to the geometric relationship in the figureb1(k) Satisfies the following conditions:
by the same method, θ can be obtainedb1(k)～θbl(k)、θc1(k)～θcm(k)、θd1(k)～θdn(k)。
And a filtering fusion stage: and each unmanned vehicle self-adaptively determines which filtering strategy is adopted for data fusion by using the received Bluetooth RSSI information, and performs data fusion calculation according to the selected filtering strategy to obtain a final collaborative navigation result.
FIG. 4 is a flow chart of the collaborative navigation algorithm of the present invention.
Referring to fig. 4, a collaborative navigation algorithm executed by the unmanned vehicle a is taken as an example for explanation. First, the approximate simplified functional relationship between the strength (magnitude of RSSI value) and the distance of the bluetooth signal is described as follows:
D＝d0+n0lg(RSSI)
wherein D represents a relative distance, D0N0 is a parameter related to information such as bluetooth model, transmission power, etc., and needs to be adjusted according to different devices and different use environments. lg () represents the base-10 logarithm. In embodiment 2, fig. 5 is a graph illustrating a relationship between a relative distance of bluetooth and a variation of RSSI value.
Fig. 6 is a graph showing a division result obtained by dividing the curve of fig. 5. Wherein the meaning of each interval is as follows: the first interval (0 m-0.4 m) divided from left to right is an infeasible interval, and because no unmanned vehicles are unlikely to overlap or collide with each other, the relative distance between the unmanned vehicles is unlikely to be too small, namely, the Bluetooth RSSI value cannot reach the interval; the second interval (0.4 m-4 m) divided from left to right is a linear interval (i.e. the first intensity interval), in which the RSSI value is very sensitive to the change of the distance, the relative distance can be accurately calculated by the RSSI, and the confidence of the distance calculation result is higherHigh; a third interval (4 m-12 m) divided from left to right is an insensitive interval (namely, a second intensity interval), in which the RSSI value is no longer sensitive to the change of the distance, and even if the distance changes greatly, the change of the corresponding RSSI value is no longer obvious, and if the distance is estimated by using the RSSI value, the confidence coefficient of the distance is low, so that the estimation is no longer suitable for estimating the distance by using the RSSI value; however, the RSSI value in this interval is still inversely proportional to the distance, so that when the RSSI is detected in this interval, it is ensured that the relative distance is always greater than a certain value (defined as the preset distance d)max)。
According to the above description, the RSSI value can be divided into three corresponding intervals: the infeasible area: [0, RSSI1]The linear region: [ RSSI2,RSSI3]And a insensitive area: [ RSSI4,-120]The unit of the parameters is dbm, and the following conditions are satisfied: -120 < RSSI4≤RSSI3＜RSSI2≤RSSI1< 0, wherein RSSI1,RSSI2,RSSI3,RSSI4The parameter value can be adjusted according to the actual performance and the specific application condition of the Bluetooth.
After the division value of the interval is determined, the subsequent filtering calculation can be further performed. Suppose that: RSSIb1(k)～RSSIbl(k) RSSI in infeasible regionc1(k)～RSSIcm(k) In the linear region, RSSId1(k)～RSSIdn(k) And if the data are located in the insensitive region, respectively processing the data in the following modes:
due to RSSIb1(k)～RSSIbl(k) In the infeasible region, the RSSIb1(k)～RSSIbl(k) Not participating in filtering calculation, using only thetab1(k)～θbl(k) And
and participating in filtering calculation.
Due to RSSIc1(k)～RSSIcm(k) In the linear region, theta is usedc1(k)～θcm(k)、RSSIc1(k)～RSSIcm(k) And
and participating in filtering calculation.
Due to RSSId1(k)～RSSIdn(k) In the insensitive region, there must be:
it is true that, among other things,
representing the relative distance between the unmanned vehicle a and the unmanned vehicles d 1-dn; then theta is usedd1(k)～θdn(k) And
participating in filtering calculation, after obtaining filtering result, to
And further performing quadratic programming as a constraint condition to obtain a final fusion result.
For the above case, the corresponding filtering model is constructed as follows:
and (3) state transition model:
wherein, k represents the system time,
is the position estimate of the unmanned vehicle a at time k,
the position increment, which represents the time k, provided by the autonomous navigation system equipped by the unmanned vehicle a, is a known quantity,
representing the system process noise at time k.
Wherein p isx(k) And py(k) And x-axis coordinates and y-axis coordinates respectively representing the estimated position of the unmanned vehicle a at the time k. Δ px(k) And Δ py(k) And respectively representing the x-axis coordinate increment and the y-axis coordinate increment of the estimated position of the unmanned vehicle a at the moment k. w is ax(k) And wy(k) Respectively representing the x-axis component noise and the y-axis component noise at time k.
The observation equation is:
wherein v (k) represents the measurement noise at time k,
represents the observed value at time k, and tan represents the tangent value obtained.
For convenience of further description below, the above model is defined as:
X(k)＝ΦX(k-1)+U(k)+W(k)
Z(k)＝h(X(k))+v(k)
wherein, W (k) and v (k) are white Gaussian noises which are not related to each other, and satisfy: the covariance matrix of W (k) is Q, and the covariance matrix of v (k) is R.
Aiming at the model, an Extended Kalman Filter (EKF) algorithm is adopted for filtering, and the specific calculation flow is as follows:
Pk,k-1＝ΦPk-1ΦT+Q
Kk＝Pk,k-1HT[HPk,k-1HT+R]-1
Pk＝[I-KkH]Pk,k-1
wherein,
representing the filtering result of step k-1 (i.e. time k-1),
one-step prediction of the representative state, Pk,k-1Representing the one-step prediction error variance, KkRepresenting the filter gain, PkRepresents the k-th step filtering error variance, Pk-1Representing the filtering error variance of the k-1 step; i represents the identity matrix of the corresponding dimension, H represents the H function in
The jacobian matrix of (a). Is obtained by
(k-th step filter estimation result) and PkOn the basis of (the k filtering estimation result error covariance matrix), whether the k filtering estimation result error covariance matrix completely meets the requirement is further detected
Wherein
And
respectively represent
If the above formula is satisfied, the first element and the second element of (1) will
Outputting a final collaborative navigation result, if the above formula is not satisfied, further optimizing by utilizing quadratic programming, wherein the specific flow is as follows:
defining the following quadratic performance index:
wherein x represents an independent variable of quadratic programming, and the limiting conditions to be met are as follows:
constructing the quadratic programming model, and setting the initial value of x as
And (3) starting quadratic programming operation, finding by using a quadratic programming algorithm, and obtaining the value of x of the minimum value of the performance index J (x) on the premise of meeting the limiting condition: x is the number ofminThen xminAnd outputting as a final collaborative navigation result.
Example 3:
this embodiment 3 provides a multi-unmanned vehicle collaborative navigation positioning system, including:
the acquisition module is used for acquiring Bluetooth signals carrying position information and sent by surrounding unmanned vehicles;
the analysis module is used for analyzing the signal intensity, the source angle and the position information from the Bluetooth signal; the source angle is a deflection angle of a connecting line of the peripheral unmanned vehicle and the current unmanned vehicle relative to the course of the current unmanned vehicle;
the angle conversion module is used for calculating the deflection angle of a connecting line between the peripheral unmanned vehicle and the current unmanned vehicle according to the source angle to obtain a source signal deflection angle;
and the filtering module is used for updating the position of the current unmanned vehicle by adopting a Kalman filtering algorithm according to the signal intensity, the source signal deflection angle and the position information to obtain a positioning result.
Optionally, the angle conversion module includes:
the course angle acquisition unit is used for acquiring the course angle of the current unmanned vehicle; the course angle of the current unmanned vehicle is an included angle of the head direction of the current unmanned vehicle relative to a preset direction;
and the deflection angle calculation unit is used for calculating the source signal deflection angle according to the current course angle of the unmanned vehicle and the source angle.
Optionally, the filtering module includes:
the signal intensity screening unit is used for screening values of which the signal intensity respectively meets a first intensity interval and a second intensity interval from the signal intensities to obtain a first signal intensity and a second signal intensity; the first intensity interval is an intensity range in which the signal intensity and the transmission distance have a linear relation; the second intensity interval is a signal intensity range after the transmission distance exceeds a preset distance; the minimum value of the first intensity interval is greater than or equal to the maximum value of the second intensity interval;
the filter model establishing unit is used for establishing a Kalman filter model by taking the first signal intensity, the position information and the source signal deflection angle as observed quantities and taking a position estimation value of the current unmanned vehicle as a state variable;
the filtering unit is used for performing filtering processing by adopting an extended Kalman filtering algorithm based on the Kalman filtering model to obtain a preliminary estimated position of the current unmanned vehicle;
the judging unit is used for judging whether the distances from the preliminary estimated position to the surrounding unmanned vehicles corresponding to the second signal strengths are all larger than the preset distance to obtain a judgment result;
a first judgment result execution unit, configured to determine that the preliminary estimated position is a positioning result of the current unmanned vehicle if the judgment result indicates yes;
and the second judgment result execution unit is used for performing secondary planning by taking the distance from the current unmanned vehicle to the surrounding unmanned vehicles corresponding to the second signal strengths as a limiting condition if the judgment result indicates no, and determining the result of the secondary planning as the positioning result of the current unmanned vehicle.
Optionally, the second determination result executing unit includes:
a quadratic programming subunit for
As a performance index for quadratic programming to
Performing secondary planning as a limiting condition of the secondary planning, determining an output result which enables the performance index to be minimum, and obtaining a positioning result of the current unmanned vehicle; wherein J (x) is a quadratic performance index; x is an independent variable of the quadratic programming,
for preliminary position estimation, Pk is
A corresponding error covariance matrix; x (1) and x (2) are x-axis coordinates and y-axis coordinates of the unmanned vehicle position contained in the independent variable x;
x-axis coordinates of surrounding unmanned vehicles corresponding to the n second signal intensities at the current moment k respectively,
and d, respectively representing the y-axis coordinate of the peripheral unmanned vehicle corresponding to the n second signal intensities at the current moment k, wherein dmax is a preset distance.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
1. on the basis of the traditional collaborative navigation algorithm only based on the ranging information, the invention further introduces the relative angle measurement information, expands the application range of the original collaborative navigation algorithm and simultaneously improves the precision of the traditional collaborative navigation algorithm.
2. According to the method, different filtering fusion strategies are selected in a self-adaptive mode according to the RSSI value, so that the RSSI is introduced into a collaborative navigation algorithm under the condition that the RSSI confidence coefficient is high, and the purpose of improving the navigation positioning accuracy is achieved; and when the RSSI confidence coefficient is insufficient, the RSSI is isolated outside the collaborative navigation algorithm, so that the RSSI with low confidence coefficient is prevented from influencing the navigation positioning precision.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (6)
1. A multi-unmanned vehicle collaborative navigation positioning method is characterized by comprising the following steps:
acquiring a Bluetooth signal carrying position information sent by surrounding unmanned vehicles;
analyzing signal strength, source angle and the position information from the Bluetooth signal; the source angle is a deflection angle of a connecting line of the peripheral unmanned vehicle and the current unmanned vehicle relative to the course of the current unmanned vehicle;
calculating the deflection angle of a connecting line between the peripheral unmanned vehicle and the current unmanned vehicle according to the source angle to obtain a source signal deflection angle;
updating the position of the current unmanned vehicle by adopting a Kalman filtering algorithm according to the signal intensity, the source signal deflection angle and the position information to obtain a positioning result;
according to the signal intensity, the source signal deflection angle and the position information, updating the position of the current unmanned vehicle by adopting a Kalman filtering algorithm to obtain a positioning result, and specifically comprising the following steps:
screening values of which the signal intensity respectively meets a first intensity interval and a second intensity interval from the signal intensities to obtain a first signal intensity and a second signal intensity; the first intensity interval is an intensity range in which the signal intensity and the transmission distance have a linear relation; the second intensity interval is a signal intensity range after the transmission distance exceeds a preset distance; the minimum value of the first intensity interval is greater than or equal to the maximum value of the second intensity interval;
establishing a Kalman filtering model by taking the first signal intensity, the position information and the source signal deflection angle as observed quantities and taking a position estimation value of the current unmanned vehicle as a state variable;
based on the Kalman filtering model, performing filtering processing by adopting an extended Kalman filtering algorithm to obtain a preliminary estimated position of the current unmanned vehicle;
judging whether the distances from the preliminary estimated position to the surrounding unmanned vehicles corresponding to the second signal strengths are all larger than the preset distance to obtain a judgment result;
if the judgment result shows that the position is the current unmanned vehicle, determining the initial estimated position as a positioning result of the current unmanned vehicle;
and if the judgment result shows that the unmanned vehicles are not located, performing secondary planning by taking the fact that the distances from the current position of the unmanned vehicle to the surrounding unmanned vehicles corresponding to the second signal strengths are all larger than the preset distance as a limiting condition, and determining the result of the secondary planning as the positioning result of the current unmanned vehicle.
2. The method according to claim 1, wherein the calculating a deflection angle of a connection line between the peripheral unmanned vehicle and the current unmanned vehicle according to the source angle to obtain a source signal deflection angle specifically comprises:
acquiring a course angle of the current unmanned vehicle; the course angle of the current unmanned vehicle is an included angle of the head direction of the current unmanned vehicle relative to a preset direction;
and calculating the source signal deflection angle according to the course angle of the current unmanned vehicle and the source angle.
3. The method according to claim 1, wherein performing secondary planning with the limiting condition that the distance from the current position of the unmanned vehicle to the surrounding unmanned vehicles corresponding to each second signal strength is greater than the preset distance, and determining that the result of the secondary planning is the positioning result of the current unmanned vehicle specifically comprises:
to be provided with
As a performance index for quadratic programming to
Performing secondary planning as a limiting condition of the secondary planning, determining an output result which enables the performance index to be minimum, and obtaining a positioning result of the current unmanned vehicle; wherein J (x) is a quadratic performance index; x is an independent variable of the quadratic programming,
for preliminary estimation of position, PkIs composed of
A corresponding error covariance matrix; x (1) and x (2) are x-axis coordinates and y-axis coordinates of the unmanned vehicle position contained in the independent variable x;
x-axis coordinates of surrounding unmanned vehicles corresponding to the n second signal intensities at the current moment k respectively,
y-axis coordinate, d, of the surrounding unmanned vehicle at the current time k corresponding to the n second signal intensities respectivelymaxIs a preset distance.
4. A multi-unmanned vehicle collaborative navigation positioning system is characterized by comprising:
the acquisition module is used for acquiring Bluetooth signals carrying position information and sent by surrounding unmanned vehicles;
the analysis module is used for analyzing the signal intensity, the source angle and the position information from the Bluetooth signal; the source angle is a deflection angle of a connecting line of the peripheral unmanned vehicle and the current unmanned vehicle relative to the course of the current unmanned vehicle;
the angle conversion module is used for calculating the deflection angle of a connecting line between the peripheral unmanned vehicle and the current unmanned vehicle according to the source angle to obtain a source signal deflection angle;
the filtering module is used for updating the position of the current unmanned vehicle by adopting a Kalman filtering algorithm according to the signal intensity, the source signal deflection angle and the position information to obtain a positioning result;
the filtering module includes:
the signal intensity screening unit is used for screening values of which the signal intensity respectively meets a first intensity interval and a second intensity interval from the signal intensities to obtain a first signal intensity and a second signal intensity; the first intensity interval is an intensity range in which the signal intensity and the transmission distance have a linear relation; the second intensity interval is a signal intensity range after the transmission distance exceeds a preset distance; the minimum value of the first intensity interval is greater than or equal to the maximum value of the second intensity interval;
the filter model establishing unit is used for establishing a Kalman filter model by taking the first signal intensity, the position information and the source signal deflection angle as observed quantities and taking a position estimation value of the current unmanned vehicle as a state variable;
the filtering unit is used for performing filtering processing by adopting an extended Kalman filtering algorithm based on the Kalman filtering model to obtain a preliminary estimated position of the current unmanned vehicle;
the judging unit is used for judging whether the distances from the preliminary estimated position to the surrounding unmanned vehicles corresponding to the second signal strengths are all larger than the preset distance to obtain a judgment result;
a first judgment result execution unit, configured to determine that the preliminary estimated position is a positioning result of the current unmanned vehicle if the judgment result indicates yes;
and the second judgment result execution unit is used for performing secondary planning by taking the distance from the current unmanned vehicle to the surrounding unmanned vehicles corresponding to the second signal strengths as a limiting condition if the judgment result indicates no, and determining the result of the secondary planning as the positioning result of the current unmanned vehicle.
5. The multi-unmanned-vehicle cooperative navigation and positioning system of claim 4, wherein the angle conversion module comprises:
the course angle acquisition unit is used for acquiring the course angle of the current unmanned vehicle; the course angle of the current unmanned vehicle is an included angle of the head direction of the current unmanned vehicle relative to a preset direction;
and the deflection angle calculation unit is used for calculating the source signal deflection angle according to the current course angle of the unmanned vehicle and the source angle.
6. The multi-unmanned-vehicle cooperative navigation and positioning system according to claim 4, wherein the second determination result execution unit comprises:
a quadratic programming subunit for
As a performance index for quadratic programming to
Performing secondary planning as a limiting condition of the secondary planning, determining an output result which enables the performance index to be minimum, and obtaining a positioning result of the current unmanned vehicle; wherein J (x) is a quadratic performance index; x is an independent variable of the quadratic programming,
for preliminary estimation of position, PkIs composed of
A corresponding error covariance matrix; x (1) and x (2) are x-axis coordinates and y-axis coordinates of the unmanned vehicle position contained in the independent variable x;
x-axis coordinates of surrounding unmanned vehicles corresponding to the n second signal intensities at the current moment k respectively,
y-axis coordinate, d, of the surrounding unmanned vehicle at the current time k corresponding to the n second signal intensities respectivelymaxIs a preset distance.
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