CN116929350B - Rapid temporary reconstruction collaborative navigation system and method based on data link ranging - Google Patents

Rapid temporary reconstruction collaborative navigation system and method based on data link ranging Download PDF

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CN116929350B
CN116929350B CN202311064485.6A CN202311064485A CN116929350B CN 116929350 B CN116929350 B CN 116929350B CN 202311064485 A CN202311064485 A CN 202311064485A CN 116929350 B CN116929350 B CN 116929350B
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unmanned
aerial
platform
ground
platforms
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CN116929350A (en
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朱建良
杨东豫
吴盘龙
马立丰
赵高鹏
王军
薄煜明
王超尘
汪进文
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Nanjing University of Science and Technology
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Nanjing University of Science and Technology
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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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations

Abstract

The invention discloses a rapid temporary reconstruction collaborative navigation system and method based on data link ranging. The system comprises an aerial unmanned subsystem, a ground unmanned subsystem and a communication data chain, wherein the aerial unmanned subsystem comprises an aerial unmanned flight network formed by a plurality of aerial unmanned platforms, and the ground unmanned subsystem comprises a ground cooperative communication network formed by a plurality of ground unmanned platforms; the communication data link is connected with each unmanned platform. The method comprises the following steps: the aerial unmanned platform carries an onboard sensor to acquire navigation positioning data and assist the unmanned platform in navigation positioning; when the aerial unmanned platform enters the communication range of the ground unmanned platform, the navigation position information of the ground unmanned platform is utilized to correct the positioning error of the aerial unmanned platform; and the relative distance between the unmanned platforms is calculated and obtained by the two-way single-way distance measurement method through the data transmitted by the communication data link of each unmanned platform. The invention can carry out high-precision collaborative navigation in a complex environment and has stronger anti-interference capability.

Description

Rapid temporary reconstruction collaborative navigation system and method based on data link ranging
Technical Field
The invention relates to the technical field of unmanned system collaborative navigation, in particular to a rapid temporary reconstruction collaborative navigation system and method based on data link ranging.
Background
Unmanned aerial vehicles and unmanned vehicles are important components of modern technology, and are continuously perfected and upgraded along with continuous development and progress of technology. The unmanned plane and the unmanned vehicle have wide application range, for example, in military aspect, can be used for tasks such as information investigation, investigation and monitoring, target identification, attack and the like, and can effectively improve the combat power and combat effect of the army; in the civil aspect, unmanned aerial vehicle and ground unmanned platform can be used for working such as taking a photograph, logistics distribution, environmental monitoring, rescue search, can save a large amount of manpower and material resources.
The traditional unmanned platform generally adopts a GNSS/INS integrated navigation method for navigation positioning, and under the condition of GNSS, relatively high precision can be obtained, and the efficiency of work completion can be effectively improved. However, when the unmanned platform enters the GNSS signal weakly or even without the GNSS signal, the error of the inertial device cannot be corrected, the positioning error will increase along with the increase of time, and when the time is longer, the positioning accuracy is poor, and the designated task cannot be completed. Compared with a single unmanned platform, the unmanned platforms can realize collaborative navigation through methods of mutual communication, ranging and the like, so that the positioning precision of the unmanned platforms is improved, and the appointed task is completed. The traditional collaborative navigation generally refers to collaborative navigation of an air unmanned platform, so that the collaborative navigation research of the air unmanned platform and a ground unmanned platform is less, and the air-ground collaborative navigation of an unmanned system still has the following problems at present: (1) in the GNSS refusing environment, the inertial navigation error of the unmanned platform is fast in divergence speed, and formation is difficult to maintain; (2) the height and the speed between the air domain and the ground domain are not matched, but the traditional geometric figure constraint method needs to convert the unmanned platform to the same height for calculation, so that larger navigation positioning errors can be generated; (3) depending on the GNSS positioning system, collaborative navigation is difficult to be performed in complex environments such as hills, forests, cities and the like.
Disclosure of Invention
The invention aims to provide a rapid temporary reconstruction collaborative navigation system and a rapid temporary reconstruction collaborative navigation method for ranging data chains, which realize high-precision collaborative navigation positioning under a GNSS refusing environment.
The technical solution for realizing the purpose of the invention is as follows: the utility model provides a quick-speed temporary reconstruction collaborative navigation based on data link range finding, this system includes aerial unmanned subsystem, ground unmanned subsystem and communication data link, wherein:
the aerial unmanned subsystem comprises an aerial unmanned flying network formed by a plurality of aerial unmanned platforms, the aerial unmanned platforms are provided with airborne sensors, and the airborne sensors acquire navigation positioning data and assist the unmanned platforms in navigation positioning;
the ground unmanned subsystem comprises a ground cooperative communication network formed by a plurality of ground unmanned platforms; the ground unmanned platform can communicate with the aerial unmanned platform, and when the aerial unmanned platform enters the communication range of the ground unmanned platform, the navigation position information of the ground unmanned platform is utilized to correct the positioning error of the aerial unmanned platform, so that the collaborative navigation of the aerial unmanned platform and the ground unmanned platform is realized;
the communication data chain is connected with each unmanned platform and comprises a graphic transmission data chain and a data transmission data chain, wherein the graphic transmission data chain is used for transmitting image information, and the data transmission data chain is used for transmitting data information such as the gesture, the speed and the position of each unmanned platform; and the unmanned platforms calculate and obtain the relative distance between the unmanned platforms by using a two-way single-way distance measurement method through the data transmitted by the communication data link, so as to realize the collaborative navigation of multiple unmanned platforms.
The fast cooperative navigation method based on the reconstruction of the data link ranging comprises the following steps:
step 1, powering on a rapid-temporary-reconfiguration collaborative navigation system, establishing a coordinate reference by an aerial unmanned platform, calculating position information of the aerial unmanned platform and other aerial unmanned platforms, and performing formation flight;
step 2, the aerial unmanned platform collects data information by using an inertial sensor and transmits the data information to a processor carried by the aerial unmanned platform;
step 3, carrying out gesture calculation by using a processor carried by the aerial unmanned platform to obtain the current position information of each aerial unmanned platform;
step 4, judging whether collaborative navigation data obtained through data link communication are input, wherein the collaborative navigation data comprise the relative distance and the relative position between unmanned platforms, and when the collaborative navigation data are not input, directly entering the step 3 to perform gesture calculation; when the collaborative navigation data is input, taking the collaborative navigation data as an observed quantity, taking data obtained by resolving an inertial sensor as a state quantity, and entering a step 5;
step 5, according to a method based on geometric constraint, performing attitude update by using Kalman filtering to realize the positioning of the unmanned aerial platform;
Step 6, judging whether communication information of the ground unmanned platform or the ground anchor point exists, and when the communication information of the ground unmanned platform or the ground anchor point does not exist, entering a step 3 to perform gesture calculation; when the information of the ground unmanned platform or the ground anchor point exists, the step 7 is entered;
and 7, performing navigation calculation on the information of the ground unmanned platform and the information of the air unmanned platform, and updating the navigation position by using a quick-speed temporary reconstruction collaborative navigation method to realize air-ground collaborative navigation.
Compared with the prior art, the invention has the remarkable advantages that:
(1) The distance measurement information among the unmanned platforms is utilized, a data chain is used for communication, and the data obtained by communication is utilized to form a geometric figure for assisting the unmanned platforms in navigation, so that the multi-unmanned-platform cooperative formation flying in the GNSS refusing environment is realized;
(2) Based on geometric characteristics, the fast cooperative navigation in the reconstruction is realized, and the cooperative navigation in the air and the ground and the high-precision cooperative navigation positioning under the GNSS refusing environment are realized;
(3) The navigation system is independent of a GNSS positioning system, can perform collaborative navigation in complex environments such as hills, forests and cities, completes formation flight, suppresses error divergence of the navigation system, and has strong anti-interference capability.
Drawings
FIG. 1 is a schematic diagram of the fast-temporary-reconstruction collaborative navigation system based on data link ranging according to the present invention.
Fig. 2 is a schematic diagram of geometric translation.
Fig. 3 is a geometric rotation diagram.
Fig. 4 is a schematic diagram of a rapid temporary reconstruction method.
Fig. 5 is a graph of collaborative navigation error analysis for multiple unmanned platforms.
Fig. 6 is a flow chart of the fast-temporary-reconstruction collaborative navigation method based on data link ranging according to the present invention.
Detailed Description
The invention relates to an air-ground cooperative positioning method based on data link ranging, which is called a rapid temporary reconstruction cooperative navigation method based on data link ranging, and aims at the problems that inertial navigation errors of multiple unmanned platforms are high in divergence speed, formation is difficult to maintain and the like in a GNSS refused environment; aiming at the problems of unmatched height and speed between an air domain and a ground domain, the traditional geometric constraint method is adopted, the unmanned platform is required to be converted to the same height for calculation, and larger navigation positioning errors can be generated, so that the rapid temporary reconstruction collaborative navigation method based on geometric characteristics is provided, the collaborative navigation of the air and the ground is realized, the high-precision collaborative navigation positioning under the GNSS rejection environment is realized, the working efficiency of the unmanned aerial vehicle group is improved, and the task requirements are met. Meanwhile, the rapid cooperative navigation method based on data link ranging provided by the invention does not depend on a GNSS positioning system, can perform cooperative navigation in complex environments (hilly, forest, city and other environments), completes formation flight, suppresses error divergence of a navigation system, and has stronger anti-interference capability. The technical scheme of the invention is described in detail below.
It is easy to understand that various embodiments of the present invention can be envisioned by those of ordinary skill in the art without altering the true spirit of the present invention in light of the present teachings. Accordingly, the following detailed description and drawings are merely illustrative of the invention and are not intended to be exhaustive or to limit or restrict the invention.
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.
The invention relates to a rapid temporary reconstruction collaborative navigation system based on data link ranging, which comprises an air unmanned subsystem, a ground unmanned subsystem and a communication data link, wherein:
the aerial unmanned subsystem comprises an aerial unmanned flying network formed by a plurality of aerial unmanned platforms, the aerial unmanned platforms are provided with airborne sensors, and the airborne sensors acquire navigation positioning data and assist the unmanned platforms in navigation positioning;
the ground unmanned subsystem comprises a ground cooperative communication network formed by a plurality of ground unmanned platforms; the ground unmanned platform can communicate with the aerial unmanned platform, and when the aerial unmanned platform enters the communication range of the ground unmanned platform, the navigation position information of the ground unmanned platform is utilized to correct the positioning error of the aerial unmanned platform, so that the collaborative navigation of the aerial unmanned platform and the ground unmanned platform is realized;
the communication data chain is connected with each unmanned platform and comprises a graphic transmission data chain and a data transmission data chain, wherein the graphic transmission data chain is used for transmitting image information, and the data transmission data chain is used for transmitting data information such as the gesture, the speed and the position of each unmanned platform; and the unmanned platforms calculate and obtain the relative distance between the unmanned platforms by using a two-way single-way distance measurement method through the data transmitted by the communication data link, so as to realize the collaborative navigation of multiple unmanned platforms.
As a specific example, the calculation of the two-way single-way ranging method is used to obtain the relative distance between the unmanned platforms, so as to realize the collaborative navigation of multiple unmanned platforms, which is specifically as follows:
each unmanned platform simultaneously transmits single-way ranging signals, simultaneously receives single-way ranging signals transmitted by other unmanned platforms, obtains distance information between the current unmanned platforms by measuring time intervals between the two signals, and utilizes unmanned platform position and speed information transmitted by a communication data chain to perform cooperative positioning among multiple unmanned platforms, so that positioning accuracy is improved.
As a specific example, the aerial unmanned platform in the aerial unmanned subsystem is a rotor unmanned plane; the aerial unmanned platforms can communicate with each other, exchange position information and distance information, establish a measurement polygon based on geometric figure constraint according to the distance information obtained by a communication data chain, further inhibit error divergence of inertial navigation, realize collaborative navigation positioning of multiple aerial unmanned platforms, and improve system positioning accuracy.
As a specific example, the communication data chain includes a graphic data chain for performing data transmission data chain and image transmission, which can be used for transmitting data, measuring distance, has higher anti-interference performance, can perform higher-frequency communication, does not affect the normal position update of the system, can obtain the relative distance between unmanned platforms through the data calculation transmitted by the communication data chain, and realizes the co-positioning of multiple unmanned platforms by using the relative distance.
As a specific example, the ground unmanned platform in the ground unmanned subsystem is a ground mobile robot, can flexibly operate in a complex environment, and has higher stability; even if the GNSS signals are not available, the ground unmanned platform can still keep higher precision; the ground unmanned platform records the characteristic points and the ground anchor point information in advance, and when the ground unmanned platform passes through the characteristic points, the ground unmanned platform automatically corrects the position information of the ground unmanned platform, so that the ground unmanned platform can perform tasks more accurately, and the working efficiency is improved.
As a specific example, the collaborative navigation positioning of the multiple aerial unmanned platforms adopts a method based on geometric figure constraint, a communication data chain is utilized for ranging, an inertial sensor measures pose information, an airborne sensor assists in positioning, the relative distance and the relative speed between the aerial unmanned platforms are measured in real time, the position, the speed and the pose data information between the aerial unmanned platforms are transmitted through the communication data chain, the system is constrained through geometric figures formed between the aerial unmanned platforms, a measurement polygon is constructed, and the inertial navigation error of the multiple aerial unmanned platforms is corrected.
As a specific example, the collaborative navigation of the aerial unmanned platform and the ground unmanned platform is specifically:
Each unmanned platform has a communication address of the unmanned platform, and the current unmanned platform can be judged to be an aerial unmanned platform or a ground anchor point by using the address;
when only the aerial unmanned platform exists in the communication network, collaborative navigation is realized through the relative distance constraint of each aerial unmanned platform;
when the aerial unmanned platform and the ground unmanned platform carry out collaborative navigation, because the height distance between the platforms is larger, a space-ground collaborative positioning system based on geometric figure constraint is used to generate larger errors, and the error divergence of inertial navigation cannot be effectively restrained, so when the ground unmanned platform or a ground anchor point is connected to a communication network, the normal vector formed between the aerial unmanned platform and the ground unmanned platform is utilized to acquire the observation information of the aerial unmanned platform, and the error divergence of the aerial unmanned platform is corrected, so that the space-ground collaborative navigation is realized.
When the ground unmanned platform is accessed to a communication network, each aerial unmanned platform can communicate with the ground unmanned platform and correct the relative positioning information of the aerial unmanned platform based on the position information of the ground unmanned platform; such information may help the unmanned aerial platform perform tasks more efficiently, correct cumulative errors resulting from long-term operation of the inertial sensors, and thereby improve stability and accuracy of flight.
Meanwhile, when the ground unmanned platform cannot receive GNSS signals due to complex environment, object shielding and the like, but the aerial unmanned platform can receive the signals, the position information of the ground unmanned platform can be corrected by utilizing the position information of the aerial unmanned platform and the distance information between the aerial unmanned platform and the ground unmanned platform, and the position of the ground unmanned platform can be more accurately determined through the information provided by the aerial unmanned platform, so that bidirectional correction among multiple unmanned platforms is realized.
The invention discloses a rapid temporary reconstruction collaborative navigation method based on data link ranging, which is based on the rapid temporary reconstruction collaborative navigation system based on the data link ranging and specifically comprises the following steps:
step 1, powering on a rapid-temporary-reconfiguration collaborative navigation system, establishing a coordinate reference by an aerial unmanned platform, calculating position information of the aerial unmanned platform and other aerial unmanned platforms, and performing formation flight;
step 2, the aerial unmanned platform collects data information by using an inertial sensor and transmits the data information to a processor carried by the aerial unmanned platform;
step 3, carrying out gesture calculation by using a processor carried by the aerial unmanned platform to obtain the current position information of each aerial unmanned platform;
Step 4, judging whether collaborative navigation data obtained through data link communication are input, wherein the collaborative navigation data comprise the relative distance and the relative position between unmanned platforms, and when the collaborative navigation data are not input, directly entering the step 3 to perform gesture calculation; when the collaborative navigation data is input, taking the collaborative navigation data as an observed quantity, taking data obtained by resolving an inertial sensor as a state quantity, and entering a step 5;
step 5, according to a method based on geometric constraint, performing attitude update by using Kalman filtering to realize the positioning of the unmanned aerial platform;
step 6, judging whether communication information of the ground unmanned platform or the ground anchor point exists, and when the communication information of the ground unmanned platform or the ground anchor point does not exist, entering a step 3 to perform gesture calculation; when the information of the ground unmanned platform or the ground anchor point exists, the step 7 is entered;
and 7, performing navigation calculation on the information of the ground unmanned platform and the information of the air unmanned platform, and updating the navigation position by using a quick-speed temporary reconstruction collaborative navigation method to realize air-ground collaborative navigation.
As a specific example, when the ground unmanned platform cannot receive GNSS signals, but the aerial unmanned platform is able to receive these signals, the position information of the ground unmanned platform is corrected using the position information of the aerial unmanned platform and the distance information with the ground unmanned platform.
As a specific example, in step 5, according to a method based on geometric constraints, the pose is updated by using kalman filtering, so as to realize the positioning of the aerial unmanned platform, which is specifically as follows:
step 1, an aerial unmanned platform is set as a rotor unmanned plane, and a group of unmanned planes is formed byThe composition of the individual nodes is that,is a nodeIs used to determine the actual position of the (c) in the (c),is a nodeAn output position corresponding to the inertial sensor;node determined for ranging valuesWherein the ranging values are obtained from a communication data chain;
step 2, taking the initial position of the first unmanned aerial vehicle as an initial position, establishing a coordinate reference, measuring distance information between the aerial unmanned aerial vehicle platforms through a two-way one-way distance measurement method, recording initial formation information by utilizing the distance information, determining position information of each aerial unmanned aerial vehicle platform, converting each aerial unmanned aerial vehicle platform to the same height by utilizing the position information obtained by each inertial sensor, and calculating the gravity center position of the polygon of the inertial sensor;
step 3, overlapping the polygon centers formed by the formation polygons and the polygon centers calculated by the inertial sensor to obtain the measured polygon after translation, and recording the current position as；
Step 4, after obtaining the measurement polygon after translation, rotating the measurement polygon after translation to enable the position information of the measurement polygon after translation and the position of the inertial sensor Information error is minimal, i.eMinimum:
（1）
in the method, in the process of the invention,in order to translate the coordinates of the positions of the nodes after rotation,the position of each aerial unmanned platform measured by the inertial sensor;
calculating the time by using least square methodAt minimum timeAndthe obtained position is then usedAs the observed quantity of the Kalman filtering, the Kalman filtering is performed by taking the position measured by the inertial sensor as a state quantity, so as to correct the accumulated error of the inertial sensor.
As a specific example, in step 7, navigation calculation is performed on information of the ground unmanned platform and information of the air unmanned platform, and navigation position update is performed by using a fast-in-flight reconstruction collaborative navigation method, so as to realize air-ground collaborative navigation, which is specifically as follows:
when the aerial unmanned platforms enter the communication range of the ground unmanned platforms, each aerial unmanned platform judges whether the ground unmanned platforms exist according to the transmitted data addresses, and when the ground unmanned platforms exist, a ranging triangle is formed by the mutual ranging information between the aerial unmanned platforms and the ground unmanned platforms;
the normal vector between the unmanned platforms is obtained by using the ranging triangle calculation, the normal vector and the distance information are used as observables, the position and the speed information between the unmanned platforms are used as state quantity, the Kalman filtering is used for real-time observation, the error divergence between the unmanned platforms is restrained, and the rapid temporary reconstruction air-ground collaborative navigation based on the data link ranging is realized;
The kalman filtering steps are as follows:
the state quantity is taken as
（2）
In the method, in the process of the invention,is an unmanned platformIs provided with a position information of (a),，is an unmanned platformThe speed information of the three unmanned platforms is used as the state quantity;
the state equation of the system is
（3）
In the method, in the process of the invention,as a priori estimates of the continuous system,is thatThe system state transition matrix is used at the moment,is thatA time-of-day system state matrix,is thatThe system noise input matrix is used at the moment,is thatTime system process noise;
discretization into
（4）
In the method, in the process of the invention,representation systemA state matrix of the time of day,is thatFrom moment to momentA system state transition matrix of time of day,is a systemA state matrix of the time of day,is thatThe system noise input matrix for the time of day,is thatSystem process noise at time, i.e., accelerometer noise;
set the observed quantity，The distance between the first unmanned platform and the second unmanned platform, the distance between the first unmanned platform and the third unmanned platform, the distance between the second unmanned platform and the third unmanned platform in the system,normal vector formed between unmanned platformsA component in the direction;
the measurement equation is
（5）
In the method, in the process of the invention,is in the systemThe actual observed value of the moment in time,is thatThe observation matrix of the time-of-day system, Is thatThe state matrix of the time-of-day system,is thatObserving noise of the time system;
discretization into
（6）
In the method, in the process of the invention,is thatActual observations of the time-of-day system;is in the systemThe observation matrix at the moment converts the state variable into a predicted observation value;is in the systemThe state matrix of the time-of-day system,is thatMeasuring noise by a time system;
according to extended Kalman filtering
（7）
In the method, in the process of the invention,is a systemThe time of day a priori estimates,is a systemFrom moment to momentA system state transition matrix of time of day,is a systemA posterior estimate of time of day;
thereby estimatingTime a priori error variance matrix
（8）
In the method, in the process of the invention,is in the systemThe a priori prediction error variance matrix of the time instant,is a systemFrom moment to momentA system state transition matrix of time of day,is in the systemThe posterior prediction error variance matrix of the moment,is thatIs used to determine the transposed matrix of (a),a system process noise covariance matrix;
updating Kalman filtering gain
（9）
In the method, in the process of the invention,is thatThe Kalman filtering gain of the moment system is a weighting coefficient for observation and prediction;is in the systemThe a priori prediction error variance matrix of the time instant,is in the systemA measurement matrix of the time of day,is thatIs to be used in the present invention,is thatMeasuring a noise covariance matrix by a time system;
thereby estimating The time state vector estimation value is
（10）
In the method, in the process of the invention,is in the systemA posterior state estimate of the time of day,is thatA priori state estimates of the time of day,is thatThe kalman filter gain of the time-of-day system,as a result of the observation of the system,is in the systemA measurement matrix of time;
updating the state error covariance matrix as
（11）
In the method, in the process of the invention,the system is atThe posterior prediction error variance matrix of the moment,is thatThe kalman filter gain of the time-of-day system,is in the systemA measurement matrix of the time of day,is in the systemThe a priori prediction error variance matrix of the time instant,is thatMeasuring a noise covariance matrix by a time system;
when the system does not have collaborative navigation data, each unmanned platform updates the gesture according to the data of the inertial sensor, and when the system receives the collaborative navigation data, kalman filtering is performed once to correct the position error of the inertial sensor, thereby realizing air-ground collaborative navigation.
The invention will be described in further detail with reference to the accompanying drawings and specific examples.
Examples
With reference to fig. 1, this embodiment provides a fast-in-flight reconstruction collaborative navigation system based on data link ranging, which includes:
the aerial unmanned system comprises an aerial unmanned flying network formed by a plurality of aerial unmanned flying platforms, and is provided with a communication data chain and various airborne sensors, and can be used for transmitting navigation positioning data acquired by the sensors and assisting the unmanned flying platform in navigation positioning;
The ground unmanned system comprises a ground cooperative communication network formed by a plurality of ground mobile robots, a high-precision sensor and an air-ground communication data chain are mounted, the ground cooperative communication network can communicate with an air unmanned platform, when the air unmanned platform enters a communication range of the ground unmanned platform, the navigation position information of the ground unmanned platform can be utilized, the positioning error of the air unmanned platform is corrected, and the navigation positioning precision of the air unmanned platform is improved;
and the communication data chain is connected with the unmanned platforms, comprises a graphic transmission data chain for transmitting image information and a data transmission data chain for transmitting data information such as the gesture, the speed, the position and the like of each unmanned platform, and can measure the relative distance information among the unmanned platforms.
Further, the communication data chain includes:
the data transmitted by the data link comprises a data transmission data link and an image transmission data link, can be used for transmitting data and measuring distance, has higher anti-interference performance, has higher frequency of transmitting data, does not influence the normal position update of a system, can obtain the relative distance between unmanned platforms through the data transmitted by the communication data link by calculating by using a two-way single-way ranging method, and realizes the collaborative navigation of multiple unmanned platforms.
In this embodiment, the collaborative navigation of the multi-aerial unmanned platform adopts a method based on geometric figure translation rotation to restrict error divergence of an inertial system, and the method mainly uses the principle that ranging information among the multi-aerial unmanned platforms cannot diverge with the increase of time, and performs collaborative navigation positioning by transmitting information such as ranging, pose and the like through a data link, and the main steps are as follows:
(1) Set the group of machines to be composed ofThe composition of the individual nodes is that, is a nodeIs used to determine the actual position of the (c) in the (c), is an inertial sensorIs provided with a plurality of output positions, node determined for ranging valuesIs the ranging value obtained in the data chain.
(2) And taking the initial position of the first unmanned aerial vehicle as an initial position, establishing a coordinate reference, measuring the distance information between unmanned aerial vehicles by a double-pass one-way distance measurement method, recording initial formation information by utilizing the distance information, determining the position information of each unmanned aerial vehicle, converting each unmanned aerial vehicle to the same height by utilizing the position information obtained by each inertial navigation device, and calculating the gravity center position of the polygon of the inertial sensor.
(3) Will beThe polygon center formed by the formation polygons coincides with the polygon center calculated by the inertial device, the measured polygon after translation is obtained, and the current position of the meter is As shown in fig. 2:
(4) After obtaining the measurement polygon after translation, the measurement polygon after translation is rotated to makeMinimum of
In the method, in the process of the invention,in order to translate the coordinates of the positions of the nodes after rotation,the position of each aerial unmanned platform measured by the inertial sensor;
calculating the time by using least square methodAt minimum timeAndthe position is takenAs the observed quantity of the Kalman filtering, the Kalman filtering is carried out by taking the position measured by the inertial device as a state quantity, so as to correct the accumulated error of the inertial navigation device. FIG. 3 is a geometric rotation
The method can effectively restrict the error divergence degree of inertial navigation, and essentially restricts the system by utilizing the error divergence of an inertial device to follow Gaussian distribution, so that the method can effectively restrict the error divergence of inertial navigation under the environment with a large number of unmanned platforms and has the advantages of simple control, formation flight and positioning precision improvement.
As a further improvement of the invention, the principle analysis of the air-ground collaborative relative positioning system is developed aiming at the problems of unmatched height and speed between the air domain and the ground domain, a traditional geometric figure constraint method is adopted, so that larger errors are generated between the ground unmanned platform and the air unmanned platform, a polyhedral structure is generated, the operation amount of the system is greatly increased, and a collaborative navigation method for rapid temporary reconstruction is provided.
When the aerial unmanned platform passes through the ground unmanned platform or the communication range of the ground anchor point, the reconstruction is carried out, the error constraint is carried out by utilizing the normal vector among a plurality of unmanned platforms, the cooperative positioning between the aerial unmanned platform and the ground unmanned platform is realized, a positioning anchor point with higher precision is provided for the aerial unmanned platform, the position error divergence of the aerial unmanned platform is corrected, and the high-precision navigation positioning under the GNSS refusing environment is realized; the method can effectively improve the positioning precision of the system and improve the co-positioning efficiency of the system.
When the unmanned aerial vehicle platform in the air enters the communication range of the unmanned aerial vehicle on the ground, each unmanned aerial vehicle can judge whether the unmanned aerial vehicle on the ground exists according to the transmitted data address, and when the unmanned aerial vehicle on the ground exists, the ranging triangle is formed by the mutual ranging information between the unmanned aerial vehicle and the unmanned aerial vehicle, as shown in fig. 4:
the normal vector between the unmanned platforms can be obtained through calculation by utilizing the triangle, the normal vector and the distance information are taken as observed quantity, the information such as the position, the speed and the like between the unmanned platforms are taken as state quantity, the Kalman filtering is used for carrying out real-time observation, the error divergence between the unmanned platforms is restrained, and the rapid temporary reconstruction air-ground collaborative navigation based on the data link ranging is realized.
The Kalman filtering mainly comprises the following steps:
the state quantity is as follows:
wherein,is an unmanned platformThe information of the position on the base plate,，is an unmanned platformThe speed information of the three unmanned platforms is used as the state quantity.
The state equation of the system is
In the method, in the process of the invention,as a priori estimates of the continuous system,is thatThe system state transition matrix is used at the moment,is thatA time-of-day system state matrix,is thatThe system noise input matrix is used at the moment,is thatTime system process noise;
discretization into
In the method, in the process of the invention,representation systemA state matrix of the time of day,is thatFrom moment to momentA system state transition matrix of time of day,is a systemA state matrix of the time of day,is thatThe system noise input matrix for the time of day,is thatTime of day systemProcess noise, i.e. accelerometer noise.
Set its observed quantity，The distance between the first unmanned platform and the second unmanned platform, the distance between the first unmanned platform and the third unmanned platform, the distance between the second unmanned platform and the third unmanned platform in the system,for forming normal vector between unmanned aerial vehiclesComponents in the direction.
The measurement equation is
In the method, in the process of the invention,is in the systemThe actual observed value of the moment in time, Is thatThe observation matrix of the time-of-day system,is thatThe state matrix of the time-of-day system,is thatObserving noise of the time system;
discretization into
In the method, in the process of the invention,is thatActual observations of the time-of-day system;is in the systemThe observation matrix at the moment converts the state variable into a predicted observation value;is in the systemThe state matrix of the time-of-day system,is thatMeasuring noise by a time system;
in the method, in the process of the invention,to measure noise, the method comprises the following steps of
In the method, in the process of the invention,is a systemThe time of day a priori estimates,is a systemFrom moment to momentA system state transition matrix of time of day,is a systemA posterior estimate of time of day;
obtaining a priori estimates of the systemAnd updating a priori prediction error variance matrix of the system
In the method, in the process of the invention,is in the systemThe a priori prediction error variance matrix of the time instant,is a systemFrom moment to momentA system state transition matrix of time of day,is in the systemThe posterior prediction error variance matrix of the moment,is thatIs used to determine the transposed matrix of (a),a system process noise covariance matrix;
updating system Kalman filtering gain
In the method, in the process of the invention,is thatThe Kalman filtering gain of the moment system is a weighting coefficient for observation and prediction;is in the systemThe a priori prediction error variance matrix of the time instant,is in the system A measurement matrix of the time of day,is thatIs to be used in the present invention,is thatMeasuring a noise covariance matrix by a time system;
thereby estimatingThe time state vector estimation value is
In the method, in the process of the invention,is in the systemA posterior state estimate of the time of day,is thatA priori state estimates of the time of day,is thatThe kalman filter gain of the time-of-day system,as a result of the observation of the system,is in the systemA measurement matrix of time;
updating the state error covariance matrix as
In the method, in the process of the invention,the system is atThe posterior prediction error variance matrix of the moment,is thatThe kalman filter gain of the time-of-day system,is in the systemA measurement matrix of the time of day,is in the systemThe a priori prediction error variance matrix of the time instant,is thatMeasuring a noise covariance matrix by a time system;
when the system does not have collaborative navigation data, each unmanned platform updates the gesture according to the inertial navigation data, and when the system receives the collaborative navigation data, kalman filtering is performed once to correct the position error of the inertial navigation, thereby realizing the air-ground collaborative navigation.
The method utilizes geometric constraint among multiple unmanned platforms to realize collaborative navigation technology between an air domain and a ground domain, determines a normal vector of the triangle through a triangle formed by the ground domain and the air domain, and utilizes ranging data and normal vector data as observation values and Kalman filtering to realize collaborative navigation of the air and the ground.
Compared with the constraint method of the geometric figure, the method has the advantages of small calculated amount, high corresponding speed and high positioning precision, can switch the target at any time, is not influenced by the geometric figure, can realize the rapid temporary reconstruction of the ground domain and the air domain, and has the navigation error shown in figure 5 under the cooperative navigation method of the rapid temporary reconstruction.
The simulation conditions are as follows: four unmanned aerial vehicles and two unmanned aerial vehicles carry out collaborative navigation, and the gyro zero drift of an inertial navigation device used by the four unmanned aerial vehicles is 10Gyro noise of 0.01Zero offset of 15 for accelerometerAccelerometer noise of 60The method comprises the steps of carrying out a first treatment on the surface of the 4 unmanned aerial vehicle keeps 22 unmanned vehicles are kept stationary, the positioning is known, and the simulation time is 195The method comprises the steps of carrying out a first treatment on the surface of the The error of ranging by using a data chain between unmanned platforms is 3The dashed line is the mean square error between 4 unmanned aerial vehicles of polygonal translational rotation method, the solid line is the mean square error of the rapid-temporary-reconstruction collaborative navigation method, and it can be seen that the error is further reduced by using the rapid-temporary-reconstruction collaborative navigation method compared with the polygonal rotational translational method, and the positioning accuracy can be still kept higher in a longer time without using GNSS signals, thereby meeting the formation flight among multiple unmanned systems and reducing the collaborative navigation positioning error of the rapid-temporary-reconstruction collaborative navigation method And high-precision navigation positioning under the GNSS rejection environment is realized.
This embodiment is described in further detail with reference to fig. 6:
(1) the system is electrified, a coordinate reference is established by the No. 1 aircraft, the position information of the No. 1 aircraft and each unmanned platform is calculated by using the coordinate reference of the No. 1 aircraft, and formation flight is carried out;
(2) the unmanned platform collects data information by using an inertial sensor and transmits the data information to a processor carried by the unmanned platform;
(3) using a processor to perform gesture calculation to obtain current position information of each unmanned platform;
(4) judging whether collaborative navigation data are input or not, and directly entering the step (2) to perform gesture calculation when the collaborative navigation data are not input; when the collaborative navigation data is input, taking the collaborative navigation data as an observed quantity, taking data obtained by resolving an inertial device as a state quantity, and entering a step (5);
(5) the acquired data chain information is utilized, a geometric figure translation rotation method is adopted, kalman filtering is used for carrying out gesture updating, and the positioning accuracy of the unmanned aerial platform is improved;
(6) judging whether the communication information of the ground unmanned platform or the ground anchor point exists or not, and entering the step (2) to perform gesture calculation when the communication information of the ground unmanned platform or the ground anchor point exists; when the information of the ground unmanned platform or the ground anchor point exists, the step (7) is entered;
(7) And carrying out navigation calculation on the information of the ground unmanned platform and the information of the air unmanned platform, and updating the navigation position by using a rapid-temporary-reconstruction collaborative navigation method to realize air-ground collaborative navigation.
The rapid cooperative navigation method based on data link ranging described in the step (7) utilizes the characteristic of high positioning precision of the ground unmanned aerial vehicle platform to correct the positioning error of the aerial unmanned aerial vehicle platform, and meanwhile, when the aerial unmanned aerial vehicle platform can acquire GNSS signals, but the ground unmanned aerial vehicle platform cannot acquire GNSS signals due to the complex ground environment and other reasons, the position information of the ground unmanned aerial vehicle platform can be reversely corrected through the aerial unmanned aerial vehicle platform, so that rapid cooperative navigation is realized, the anti-interference performance of the system is improved, and the robustness of the system is enhanced.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention.
It should be appreciated that in the above description of exemplary embodiments of the invention, various features of the invention are sometimes described in the context of a single embodiment or with reference to a single figure in order to streamline the invention and aid those skilled in the art in understanding the various aspects of the invention. The present invention should not, however, be construed as including features that are essential to the patent claims in the exemplary embodiments.

Claims (9)

1. The utility model provides a quick temporary reconstruction collaborative navigation based on data link range finding which characterized in that, this system includes aerial unmanned subsystem, ground unmanned subsystem and communication data link, wherein:
the aerial unmanned subsystem comprises an aerial unmanned flying network formed by a plurality of aerial unmanned platforms, the aerial unmanned platforms are provided with airborne sensors, and the airborne sensors acquire navigation positioning data and assist the unmanned platforms in navigation positioning; the aerial unmanned platforms can communicate with each other, exchange position information and distance information, establish a measurement polygon based on geometric constraint according to the distance information obtained by a communication data chain, and realize collaborative navigation positioning of multiple aerial unmanned platforms;
the ground unmanned subsystem comprises a ground cooperative communication network formed by a plurality of ground unmanned platforms, wherein the ground unmanned platforms record characteristic points and ground anchor point information in advance and automatically correct own position information when passing through the points; the ground unmanned platform can communicate with the aerial unmanned platform, and when the aerial unmanned platform enters the communication range of the ground unmanned platform, the navigation position information of the ground unmanned platform is utilized to correct the positioning error of the aerial unmanned platform, so that the collaborative navigation of the aerial unmanned platform and the ground unmanned platform is realized;
The communication data chain is connected with each unmanned platform and comprises a graphic transmission data chain and a data transmission data chain, wherein the graphic transmission data chain is used for transmitting image information, and the data transmission data chain is used for transmitting data information such as the gesture, the speed and the position of each unmanned platform; the relative distance between the unmanned platforms is calculated and obtained by the two-way single-way distance measurement method through the data transmitted by the communication data link by each unmanned platform, so that the collaborative navigation of multiple unmanned platforms is realized;
the collaborative navigation of the aerial unmanned platform and the ground unmanned platform is specifically as follows:
each unmanned platform has a communication address of the unmanned platform, and the current unmanned platform can be judged to be an aerial unmanned platform or a ground anchor point by using the address;
when only the aerial unmanned platform exists in the communication network, collaborative navigation is realized through the relative distance constraint of each aerial unmanned platform; when a ground unmanned platform or a ground anchor point is accessed to a communication network, forming a ranging triangle by mutual ranging information between the aerial unmanned platform and the ground unmanned platform; and obtaining normal vectors among the unmanned platforms by using ranging triangle calculation, taking the normal vectors and the distance information as observables, taking the position and speed information among the unmanned platforms as state quantities, and carrying out real-time observation by using Kalman filtering so as to correct error divergence of the unmanned aerial platforms and realize space-ground collaborative navigation.
2. The rapid-temporary-reconstruction collaborative navigation system based on data link ranging according to claim 1, wherein the relative distance between unmanned platforms is obtained by calculation by using a two-way single-way ranging method, so as to realize collaborative navigation of multiple unmanned platforms, and the system is specifically as follows:
each unmanned platform simultaneously transmits single-way ranging signals, simultaneously receives single-way ranging signals transmitted by other unmanned platforms, obtains distance information between the current unmanned platforms by measuring time intervals between the two signals, and performs co-location among multiple unmanned platforms by utilizing unmanned platform position and speed information transmitted by a communication data chain.
3. The data link ranging-based rapid-temporary-reconstruction collaborative navigation system according to claim 1, wherein an aerial unmanned platform in the aerial unmanned subsystem is a rotorcraft unmanned.
4. The rapid-temporary-reconstruction collaborative navigation system based on data-chain ranging of claim 1, wherein a ground unmanned platform in the ground unmanned subsystem is a ground mobile robot.
5. The rapid cooperative navigation system based on data link ranging for reconstruction of claim 3, wherein the cooperative navigation positioning of the multiple unmanned aerial platforms adopts a method based on geometric constraint, the communication data link is utilized for ranging, the inertial sensor measures pose information, the airborne sensor assists in positioning, the relative distance and relative speed between the unmanned aerial platforms are measured in real time, the position, speed and pose data information between the unmanned aerial platforms are transmitted through the communication data link, and the measurement polygon is constructed through the geometric figure formed between the unmanned aerial platforms, so that the inertial navigation error of the multiple unmanned aerial platforms is corrected.
6. The fast in-flight reconstruction collaborative navigation method based on data link ranging is characterized by comprising the following steps of:
step 1, powering on a rapid-temporary-reconfiguration collaborative navigation system, establishing a coordinate reference by an aerial unmanned platform, calculating position information of the aerial unmanned platform and other aerial unmanned platforms, and performing formation flight;
step 2, the aerial unmanned platform collects data information by using an inertial sensor and transmits the data information to a processor carried by the aerial unmanned platform;
step 3, carrying out gesture calculation by using a processor carried by the aerial unmanned platform to obtain the current position information of each aerial unmanned platform;
step 4, judging whether collaborative navigation data obtained through data link communication are input, wherein the collaborative navigation data comprise the relative distance and the relative position between unmanned platforms, and when the collaborative navigation data are not input, directly entering the step 3 to perform gesture calculation; when the collaborative navigation data is input, taking the collaborative navigation data as an observed quantity, taking data obtained by resolving an inertial sensor as a state quantity, and entering a step 5;
Step 5, according to a method based on geometric constraint, performing attitude update by using Kalman filtering to realize the positioning of the unmanned aerial platform;
step 6, judging whether communication information of the ground unmanned platform or the ground anchor point exists, and when the communication information of the ground unmanned platform or the ground anchor point does not exist, entering a step 3 to perform gesture calculation; when the information of the ground unmanned platform or the ground anchor point exists, the step 7 is entered;
and 7, performing navigation calculation on the information of the ground unmanned platform and the information of the air unmanned platform, and updating the navigation position by using a quick-speed temporary reconstruction collaborative navigation method to realize air-ground collaborative navigation.
7. The method of claim 6, wherein when the ground unmanned platform cannot receive GNSS signals but the aerial unmanned platform can receive the GNSS signals, the position information of the ground unmanned platform is corrected by using the position information of the aerial unmanned platform and the distance information between the aerial unmanned platform and the ground unmanned platform.
8. The rapid cooperative navigation method based on data link ranging of claim 6, wherein in step 5, according to the method based on geometric constraint, the pose is updated by using kalman filtering, so as to realize the positioning of the unmanned aerial platform, which is specifically as follows:
Step 1, an aerial unmanned platform is set as a rotor unmanned plane, and a group of unmanned planes is formed byIndividual node composition->For node->Is (are) the actual position of>For node->An output position corresponding to the inertial sensor; />Node determined for distance measurement value +.>Wherein the ranging values are obtained from a communication data chain;
step 2, taking the initial position of the first unmanned aerial vehicle as an initial position, establishing a coordinate reference, measuring distance information between the aerial unmanned aerial vehicle platforms through a two-way one-way distance measurement method, recording initial formation information by utilizing the distance information, determining position information of each aerial unmanned aerial vehicle platform, converting each aerial unmanned aerial vehicle platform to the same height by utilizing the position information obtained by each inertial sensor, and calculating the gravity center position of the polygon of the inertial sensor;
step 3, overlapping the polygon centers formed by the formation polygons and the polygon centers calculated by the inertial sensor to obtain the measured polygon after translation, and recording the current position as；
Step 4, after obtaining the measurement polygon after translation, rotating the measurement polygon after translation to minimize the error between the position information of the measurement polygon after translation and the position information of the inertial sensor, namelyMinimum:
（1）
in the method, in the process of the invention, For translating the position coordinates of each node after rotation, +.>The position of each aerial unmanned platform measured by the inertial sensor;
calculating the time by using least square methodMinimum +.>And->The obtained position->As the observed quantity of the Kalman filtering, the Kalman filtering is performed by taking the position measured by the inertial sensor as a state quantity, so as to correct the accumulated error of the inertial sensor.
9. The rapid cooperative navigation method based on data link ranging according to claim 6, wherein in step 7, navigation calculation is performed on information of a ground unmanned platform and information of an air unmanned platform, and navigation position update is performed by using the rapid cooperative navigation method to realize air-ground cooperative navigation, specifically comprising the following steps:
when the aerial unmanned platforms enter the communication range of the ground unmanned platforms, each aerial unmanned platform judges whether the ground unmanned platforms exist according to the transmitted data addresses, and when the ground unmanned platforms exist, a ranging triangle is formed by the mutual ranging information between the aerial unmanned platforms and the ground unmanned platforms;
the normal vector between the unmanned platforms is obtained by using the ranging triangle calculation, the normal vector and the distance information are used as observables, the position and the speed information between the unmanned platforms are used as state quantity, the Kalman filtering is used for real-time observation, the error divergence between the unmanned platforms is restrained, and the rapid temporary reconstruction air-ground collaborative navigation based on the data link ranging is realized;
The kalman filtering steps are as follows:
the state quantity is taken as
（2）
In the method, in the process of the invention,is unmanned platform->Position information of->，/>Is unmanned platform->The speed information of the three unmanned platforms is used as the state quantity;
the state equation of the system is
（3）
In the method, in the process of the invention,for a priori estimates of the continuous system, +.>Is->Time system state transition matrix,/->Is->Time system state matrix>Is->Time system noise input matrix, < >>Is->Time system process noise;
discretization into
（4）
In the method, in the process of the invention,representation system->State matrix of time, ">Is->Time to->Time-of-day system state transition matrix,/->For system->State matrix of time, ">Is->System noise input matrix of time of day,/>Is->System process noise at time, i.e., accelerometer noise;
set the observed quantity，/>The distance between the first unmanned platform and the second unmanned platform, the distance between the first unmanned platform and the third unmanned platform, the distance between the second unmanned platform and the third unmanned platform, and the distance between the second unmanned platform and the third unmanned platform in the system, respectively,/->Is the normal vector formed between unmanned platforms>A component in the direction;
the measurement equation is
（5）
In the method, in the process of the invention,for the system in->Actual observation of time of day +. >Is->Observation matrix of time system->Is->State matrix of time system->Is->Observing noise of the time system;
discretization into
（6）
In the method, in the process of the invention,is->Actual observations of the time-of-day system; />For the system in->The observation matrix at the moment converts the state variable into a predicted observation value; />For the system in->State matrix of time system->Is->Measuring noise by a time system;
according to extended Kalman filtering
（7）
In the method, in the process of the invention,for system->Time a priori estimate,/->For system->Time to->Time-of-day system state transition matrix,/->For system->A posterior estimate of time of day;
thereby estimatingTime a priori error variance matrix
（8）
In the method, in the process of the invention,for the system in->A priori prediction error variance matrix of time instant +.>For system->Time to->Time-of-day system state transition matrix,/->For the system in->Time posterior prediction error variance matrix, +.>Is->Transposed matrix of>A system process noise covariance matrix;
updating Kalman filtering gain
（9）
In the method, in the process of the invention,is->The Kalman filtering gain of the moment system is a weighting coefficient for observation and prediction; />For the system in->A priori prediction error variance matrix of time instant +.>For the system in->Measurement matrix of time of day- >Is->Transpose of->Is->Measuring a noise covariance matrix by a time system;
thereby estimatingThe time state vector estimation value is
（10）
In the method, in the process of the invention,for the system in->Posterior state estimation of time of day +.>Is->A priori state estimation of time of day,/->Is thatKalman filtering gain of time-of-day system, +.>For system observations +.>For the system in->Moment of measurementAn array;
updating the state error covariance matrix as
（11）
In the method, in the process of the invention,the system is at->Time posterior prediction error variance matrix, +.>Is->Kalman filtering gain of time-of-day system, +.>For the system in->Measurement matrix of time of day->For the system in->The a priori prediction error variance matrix of the time instant,is->Measuring a noise covariance matrix by a time system;
when the system does not have collaborative navigation data, each unmanned platform updates the gesture according to the data of the inertial sensor, and when the system receives the collaborative navigation data, kalman filtering is performed once to correct the position error of the inertial sensor, thereby realizing air-ground collaborative navigation.
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