CN116953751A - GNSS-based robot co-location system and method - Google Patents
GNSS-based robot co-location system and method Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/13—Receivers
- G01S19/35—Constructional details or hardware or software details of the signal processing chain
- G01S19/37—Hardware or software details of the signal processing chain
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/393—Trajectory determination or predictive tracking, e.g. Kalman filtering
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Abstract
The invention provides a robot co-location system and a method based on GNSS. The center server dynamically selects the anchor node robot and the user node robot; constructing an extended Kalman filter of each robot; predicting, measuring and updating by using an extended Kalman filter of each robot to obtain the position of each robot at the next moment; the measurement update includes ionosphere-free combined measurement and double difference measurement; and determining whether each user node robot receives double difference measurement update or not by using the corresponding covariance matrix according to the positions before and after double difference measurement at the next moment of each user node robot through the static game model. The user node robot benefits from the high-precision position information of the anchor node robot, the high-precision position information is transmitted through double-difference measurement, and the robust module formed by the random model and the static game model can effectively detect the measurement rough difference and weaken the influence of the measurement rough difference.
Description
Technical Field
The invention belongs to the technical field of robot positioning, and particularly relates to a robot co-positioning system and method based on GNSS.
Background
Automated robots have been widely used in many modern intelligent systems, such as delivery, rescue and patrol. Accurate and consistent positioning is critical in order to increase the efficiency and redundancy of autonomous mobile robots. In order to solve the problem of outdoor global positioning, the global navigation satellite system (Gobal Navigation Satellite System, GNSS) plays an important role, wherein the main GNSS positioning technologies can be divided into two groups, single point positioning and relative positioning depending on nearby base stations. Nowadays, accurate point positioning (Precise Point Positioning, PPP) is receiving increasing attention due to its advantages in terms of global availability and privacy protection. However, the convergence of PPP takes several minutes and the accuracy of PPP may be degraded in GNSS challenged scenarios.
With the development of communication technology, robots can share information with others. Information sharing between robots makes it possible to improve the positioning performance of the robots. By utilizing the advantages of robot trunking communication, the existing GNSS-based co-location method comprises two types of relative location and absolute location, wherein the first type adopts differential pseudo-range and carrier phase observation to obtain the accurate relative position between robots, however, the relative location method does not consider the global position of the robots in the co-location process. The second type of absolute positioning method uses double-difference pseudo-range measurement to improve the absolute positioning accuracy of the clustered network, however, the positioning consistency is still not guaranteed due to the influence of the change of the surrounding environment. The existing robot GNSS cooperative positioning method has the problems of low positioning precision and low global consistency.
Disclosure of Invention
In order to solve the technical problems, the invention provides a GNSS-based robot co-location system and a GNSS-based robot co-location method.
The technical scheme of the system of the invention is a GNSS-based robot co-location system, comprising: a center server, a plurality of robots;
the central server is connected with each robot in sequence;
the central server sequentially calculates the track of the uncertainty matrix of each robot at the current moment, and determines an anchor node robot and a user node robot; the central server builds an extended Kalman filter of each robot; predicting by using an extended Kalman filter of each robot to obtain a predicted state vector and a corresponding covariance matrix of each robot at the next moment; measuring and updating through an extended Kalman filter of each robot to obtain the position of each robot at the next moment; updating by using an extended Kalman filter of each robot to obtain a covariance matrix of a state vector of each robot at the next moment; and calculating gain parameters of the static game model through the static game model according to the positions before and after double difference measurement at the next moment of each user node robot and the corresponding covariance matrix, and judging through the gain parameters to determine whether each user node robot receives double difference measurement update.
The technical scheme of the method is a GNSS-based robot co-positioning method, which specifically comprises the following steps:
step 1: each robot wirelessly transmits the position of the current moment, the uncertainty matrix of the current moment and the GNSS original observation value of the current moment to a central server;
step 2: the central server sequentially calculates the track of the uncertainty matrix of each robot at the current moment, selects the robot with the smallest track of the uncertainty matrix at the current moment from all robots as an anchor node robot, and takes the rest robots as a plurality of user node robots;
step 3: the method comprises the steps that a central server builds an extended Kalman filter of each robot, and a state vector of the current moment of each robot and a covariance matrix of the state vector of the current moment of each robot are obtained; predicting by using an extended Kalman filter of each robot to obtain a predicted state vector of each robot at the next moment; transferring the covariance matrix of the state vector of each robot at the current moment by using an extended Kalman filter of each robot to obtain the covariance matrix of the predicted state vector of each robot at the next moment;
step 4: the central server carries out measurement updating on the GNSS original observed value of each robot at the current moment through an extended Kalman filter of each robot to obtain the position of each robot at the next moment; updating the covariance matrix of the predicted state vector of each robot at the next moment by using an extended Kalman filter of each robot to obtain the covariance matrix of the state vector of each robot at the next moment;
step 5: the center server checks the covariance matrix of the state vector of each robot at the next moment through a random model to obtain a covariance matrix after random check of the state vector of each robot at the next moment; after the random model is checked, the central server calculates gain parameters of a static game model through the static game model according to the positions before and after double difference measurement at the next moment of each user node robot and the corresponding covariance matrix, and determines whether each user node robot receives double difference measurement update or not through judgment of the gain parameters;
preferably, in step 4, the central server updates the GNSS raw observations of each robot at the current time by using an extended kalman filter of each robot, and the specific process is as follows:
the central server combines GNSS original observation values of each robot at the current moment into ionosphere-free combined observation values, and uses the ionosphere-free combined observation values to measure and update each robot through an extended Kalman filter to obtain the position of the robot before double-difference measurement; the center server calculates GNSS original observation values of the anchor node robot and each user node robot at the current moment through a double difference model to obtain a virtual position of each user node robot, and measures and updates each user node robot through an extended Kalman filter by using the virtual position to obtain a double difference measured position of the user robot;
preferably, in step 5, the positions before and after the double difference measurement at the next moment of each robot and the corresponding covariance matrix are checked by the static game model, and the specific process is as follows:
wherein g k,t+1 The gain parameter of the kth user node robot at the (t+1) th moment is represented, min represents the minimum value, (x) k,t+1 ,y k,t+1 ,z k,t+1 ) T Representing the coordinates of the kth user node robot in the X-axis direction, the Y-axis direction and the Z-axis direction under the geocentric ground system before double difference measurement at the (t+1) th moment,representing the coordinates of the double difference measured in the X-axis direction, Y-axis direction and Z-axis direction under the earth-centered earth-fixed system, (a) k,t+1 ,b k,t+1 ,c k,t+1 ) T Eigenvalue vector of covariance matrix representing X-axis direction, Y-axis direction and Z-axis direction before double difference measurement at time t+1 of kth user node robot in geocentric earth system,>characteristic value vectors of covariance matrixes of X-axis direction, Y-axis direction and Z-axis direction of the kth user node robot under the earth center ground system after double-difference measurement at the (t+1) th moment are shown;
preferably, in step 5, the determination of the gain parameter is used to determine whether each user node robot accepts the update of the double differential measurement, which is specifically as follows:
if g k,t+1 And if the game reaches unique Nash equilibrium, the method receives double-difference measurement update, and the position of the kth user node robot at the t+1 time is as follows:
if g k,t+1 And if the gaming does not reach the unique Nash equilibrium, rejecting the update of the double differential measurement, wherein the position of the kth user node robot at the t+1 time is as follows: (x) k,t+1 ,y k,t+1 ,z k,t+1 ) T ；
One or more technical schemes provided by the invention have at least the following technical effects or advantages:
if the robot cluster is converged to a high-precision positioning result after running for a period of time, the absolute position of the newly added robot benefits from the high-precision position reference of the cluster after the collaborative positioning method is applied, and the precision and consistency of the positioning of the robot cluster are obviously improved. This is because the user node robot benefits from high precision position information of the anchor node robot, which is propagated by the position constraint of the double differential measurement. Although the double difference measurement is easily affected by multipath errors, the two-stage robust module can effectively detect inconsistent double difference measurement and eliminate the influence thereof, so that obvious performance gain can be obtained.
In addition, the method is less influenced by the interval of information sharing among robots. When each robot communicates with other robots for a certain time interval, the whole positioning precision is good and stable. This is because in the method of the present invention, each robot can maintain a high-precision positioning within a period of time, and meanwhile, GNSS information fusion between robots can improve positioning robustness against possible errors, and selecting an appropriate communication interval can reduce communication cost and improve positioning accuracy of a cluster in consideration of trade-off between positioning accuracy and communication cost.
Drawings
Fig. 1: the method of the embodiment of the invention is a flow chart.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In particular, the method according to the technical solution of the present invention may be implemented by those skilled in the art using computer software technology to implement an automatic operation flow, and a system apparatus for implementing the method, such as a computer readable storage medium storing a corresponding computer program according to the technical solution of the present invention, and a computer device including the operation of the corresponding computer program, should also fall within the protection scope of the present invention.
The following describes a technical scheme of an embodiment of the present invention with reference to fig. 1 as a system and a method for co-locating a robot based on GNSS.
The technical scheme of the system of the embodiment of the invention is a GNSS-based robot co-location system, which comprises: a center server, a plurality of robots;
the central server is connected with each robot in sequence;
the central server sequentially calculates the track of the uncertainty matrix of each robot at the current moment, and determines an anchor node robot and a user node robot; the central server builds an extended Kalman filter of each robot; predicting by using an extended Kalman filter of each robot to obtain a predicted state vector and a corresponding covariance matrix of each robot at the next moment; measuring and updating through an extended Kalman filter of each robot to obtain the position of each robot at the next moment; updating by using an extended Kalman filter of each robot to obtain a covariance matrix of a state vector of each robot at the next moment; and calculating gain parameters of the static game model through the static game model according to the positions before and after double difference measurement at the next moment of each user node robot and the corresponding covariance matrix, and judging through the gain parameters to determine whether each user node robot receives double difference measurement update.
The central server is a four-wheel robot, the model of the GNSS receiver is U-blox F9P, and the model of the central processor is AMD Ryzen 5 4500U; the robot is a four-wheel robot, the model of the GNSS receiver is U-blox F9P, and the model of the central processor is ARM Cortex-A75;
all robots autonomously acquire GNSS measurement values through respective GNSS receivers, share respective positioning information through a communication network, then select a robot with the smallest uncertainty as an anchor node robot, the rest robots as user node robots and share the GNSS measurement values of the anchor node robots in the network, all robots update the respective positioning information through GNSS ionosphere-free combined measurement and calculation, meanwhile, the user node robots calculate the own virtual position through a GNSS double-difference measurement equation constructed between the user node robots and the anchor node robots, update the positioning information through virtual position calculation, and all robots reject rough differences in measurement through a random model-based test method, and meanwhile, the user robots also determine whether to accept virtual position update through a static game model-based test method. The above process is not dependent on a central server all the time, only point-to-point communication is needed, and the GNSS ionosphere-free combined measurement can enable each robot to obtain independent positioning information, the GNSS double-difference combined measurement can enable the positioning result accuracy and consistency of the user node robot to be improved, the measurement rough difference is removed by the checking method based on the random model and the static game model, and the reliability of positioning information is improved. The technical problem of low GNSS co-location precision and consistency in the prior art is solved.
The technical scheme adopted by the method of the embodiment of the invention is a GNSS-based robot co-location method, as shown in fig. 1, and comprises the following specific steps:
step 1: each robot wirelessly transmits the position of the current moment, the uncertainty matrix of the current moment and the GNSS original observation value of the current moment to a central server;
step 2: the central server sequentially calculates the track of the uncertainty matrix of each robot at the current moment, selects the robot with the smallest track of the uncertainty matrix at the current moment from all robots as an anchor node robot, and takes the rest robots as a plurality of user node robots;
step 3: the method comprises the steps that a central server builds an extended Kalman filter of each robot, and a state vector of the current moment of each robot and a covariance matrix of the state vector of the current moment of each robot are obtained; predicting by using an extended Kalman filter of each robot to obtain a predicted state vector of each robot at the next moment; transferring the covariance matrix of the state vector of each robot at the current moment by using an extended Kalman filter of each robot to obtain the covariance matrix of the predicted state vector of each robot at the next moment;
step 4: the central server carries out measurement updating on the GNSS original observed value of each robot at the current moment through an extended Kalman filter of each robot to obtain the position of each robot at the next moment; updating the covariance matrix of the predicted state vector of each robot at the next moment by using an extended Kalman filter of each robot to obtain the covariance matrix of the state vector of each robot at the next moment;
and 4, the central server performs measurement updating on the GNSS original observation value of each robot at the current moment through an extended Kalman filter of each robot, and the specific process is as follows:
the central server combines GNSS original observation values of each robot at the current moment into ionosphere-free combined observation values, and uses the ionosphere-free combined observation values to measure and update each robot through an extended Kalman filter to obtain the position of the robot before double-difference measurement; the center server calculates GNSS original observation values of the anchor node robot and each user node robot at the current moment through a double difference model to obtain a virtual position of each user node robot, and measures and updates each user node robot through an extended Kalman filter by using the virtual position to obtain a double difference measured position of the user robot;
step 5: the center server checks the covariance matrix of the state vector of each robot at the next moment through a random model to obtain a covariance matrix after random check of the state vector of each robot at the next moment; after the random model is checked, the central server calculates gain parameters of a static game model through the static game model according to the positions before and after double difference measurement at the next moment of each user node robot and the corresponding covariance matrix, and determines whether each user node robot receives double difference measurement update or not through judgment of the gain parameters;
and 5, checking the positions before and after double difference measurement and the corresponding covariance matrix of each robot at the next moment through a static game model, wherein the specific process is as follows:
wherein g k,t+1 The gain parameter of the kth user node robot at the (t+1) th moment is represented, min represents the minimum value, (x) k,t+1 ,y k,t+1 ,z k,t+1 ) T Representing the coordinates of the kth user node robot in the X-axis direction, the Y-axis direction and the Z-axis direction under the geocentric ground system before double difference measurement at the (t+1) th moment,representing the coordinates of the double difference measured in the X-axis direction, Y-axis direction and Z-axis direction under the earth-centered earth-fixed system, (a) k,t+1 ,b k,t+1 ,c k,t+1 ) T Eigenvalue vector of covariance matrix representing X-axis direction, Y-axis direction and Z-axis direction before double difference measurement at time t+1 of kth user node robot in geocentric earth system,>characteristic value vectors of covariance matrixes of X-axis direction, Y-axis direction and Z-axis direction of the kth user node robot under the earth center ground system after double-difference measurement at the (t+1) th moment are shown;
step 5, determining whether each user node robot accepts the update of the double differential measurement through the judgment of the gain parameters, which is specifically as follows:
if g k,t+1 And if the game reaches unique Nash equilibrium, the method receives double-difference measurement update, and the position of the kth user node robot at the t+1 time is as follows:
if g k,t+1 And if the gaming does not reach the unique Nash equilibrium, rejecting the update of the double differential measurement, wherein the position of the kth user node robot at the t+1 time is as follows: (x) k,t+1 ,y k,t+1 ,z k,t+1 ) T ；
It should be understood that parts of the specification not specifically set forth herein are all prior art.
It should be understood that the foregoing description of the preferred embodiments is not intended to limit the scope of the invention, but rather to limit the scope of the claims, and that those skilled in the art can make substitutions or modifications without departing from the scope of the invention as set forth in the appended claims.
Claims (6)
1. A GNSS based robotic co-location system, comprising: a center server, a plurality of robots;
the central server is connected with each robot in sequence;
the central server sequentially calculates the track of the uncertainty matrix of each robot at the current moment, and determines an anchor node robot and a user node robot; the central server builds an extended Kalman filter of each robot; predicting by using an extended Kalman filter of each robot to obtain a predicted state vector and a corresponding covariance matrix of each robot at the next moment; measuring and updating through an extended Kalman filter of each robot to obtain the position of each robot at the next moment; updating by using an extended Kalman filter of each robot to obtain a covariance matrix of a state vector of each robot at the next moment; and calculating gain parameters of the static game model through the static game model according to the positions before and after double difference measurement at the next moment of each user node robot and the corresponding covariance matrix, and judging through the gain parameters to determine whether each user node robot receives double difference measurement update.
2. A GNSS based robot co-location method applied to the GNSS based robot co-location system of claim 1, comprising the steps of:
step 1: each robot wirelessly transmits the position of the current moment, the uncertainty matrix of the current moment and the GNSS original observation value of the current moment to a central server;
step 2: the central server sequentially calculates the track of the uncertainty matrix of each robot at the current moment, selects the robot with the smallest track of the uncertainty matrix at the current moment from all robots as an anchor node robot, and takes the rest robots as a plurality of user node robots;
step 3: the method comprises the steps that a central server builds an extended Kalman filter of each robot, and a state vector of the current moment of each robot and a covariance matrix of the state vector of the current moment of each robot are obtained; calculating through an extended Kalman filter to obtain a predicted state vector of each robot at the next moment and a covariance matrix of the predicted state vector;
step 4: the central server carries out measurement updating on the GNSS original observed value of each robot at the current moment through an extended Kalman filter of each robot to obtain the position of each robot at the next moment; updating the covariance matrix of the predicted state vector of each robot at the next moment by using an extended Kalman filter of each robot to obtain the covariance matrix of the state vector of each robot at the next moment;
step 5: the center server checks the covariance matrix of the state vector of each robot at the next moment through a random model to obtain a covariance matrix after random check of the state vector of each robot at the next moment; after the random model is checked, the central server calculates gain parameters of the static game model through the static game model according to the positions before and after double difference measurement at the next moment of each user node robot and the corresponding covariance matrix, and whether each user node robot receives double difference measurement update is determined through judgment of the gain parameters.
3. The GNSS based robotic co-location method of claim 2, wherein:
the calculation by the extended kalman filter in the step 3 is specifically as follows:
predicting by using an extended Kalman filter of each robot to obtain a predicted state vector of each robot at the next moment; transferring the covariance matrix of the state vector of each robot at the current moment by using an extended Kalman filter of each robot to obtain the covariance matrix of the predicted state vector of each robot at the next moment.
4. A method of co-locating a GNSS based robot as claimed in claim 3 wherein:
and 4, the central server performs measurement updating on the GNSS original observation value of each robot at the current moment through an extended Kalman filter of each robot, and the specific process is as follows:
the central server combines GNSS original observation values of each robot at the current moment into ionosphere-free combined observation values, and uses the ionosphere-free combined observation values to measure and update each robot through an extended Kalman filter to obtain the position of the robot before double-difference measurement; the center server calculates GNSS original observation values of the anchor node robots and the current moment of each user node robot through a double difference model to obtain virtual positions of each user node robot, and measures and updates each user node robot through an extended Kalman filter by using the virtual positions to obtain positions of the user robots after double difference measurement.
5. The method of GNSS based robotic co-localization of claim 4, wherein:
and 5, checking the positions before and after double difference measurement and the corresponding covariance matrix of each robot at the next moment through a static game model, wherein the specific process is as follows:
wherein g k,t+1 The gain parameter of the kth user node robot at the (t+1) th moment is represented, min represents the minimum value, (x) k,t+1 ,y k,t+1 ,z k,t+1 ) T Representing the coordinates of the kth user node robot in the X-axis direction, the Y-axis direction and the Z-axis direction under the geocentric ground system before double difference measurement at the (t+1) th moment,representing the coordinates of the double difference measured in the X-axis direction, Y-axis direction and Z-axis direction under the earth-centered earth-fixed system, (a) k,t+1 ,b k,t+1 ,c k,t+1 ) T Eigenvalue vector of covariance matrix representing X-axis direction, Y-axis direction and Z-axis direction before double difference measurement at time t+1 of kth user node robot in geocentric earth system,>and the eigenvalue vector of the covariance matrix of the kth user node robot in the X-axis direction, the Y-axis direction and the Z-axis direction under the geocentric ground system after double difference measurement at the (t+1) th moment is shown.
6. The GNSS based robotic co-location method of claim 5, wherein:
step 5, determining whether each user node robot accepts the update of the double differential measurement through the judgment of the gain parameters, which is specifically as follows:
if g k,t+1 And if the game reaches unique Nash equilibrium, the method receives double-difference measurement update, and the position of the kth user node robot at the t+1 time is as follows:
if g k,t+1 And if the gaming does not reach the unique Nash equilibrium, rejecting the update of the double differential measurement, wherein the position of the kth user node robot at the t+1 time is as follows: (x) k,t+1 ,y k,t+1 ,z k,t+1 ) T 。
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