CN109839951B - System and method for generating unmanned aerial vehicle autonomous tracking path model - Google Patents
System and method for generating unmanned aerial vehicle autonomous tracking path model Download PDFInfo
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Abstract
The invention discloses a system and a method for generating an unmanned aerial vehicle autonomous tracking path model, wherein the system comprises: the unmanned aerial vehicle remote controller is used for enabling a flyer to remotely control the unmanned aerial vehicle, and generating control data to be transmitted to an airborne system of the unmanned aerial vehicle; the RTK base station acquires network positioning information of the network RTK base station group, combines the network positioning information with RTK satellite positioning information of the RTK base station, and calculates positioning calibration data of the unmanned aerial vehicle; the unmanned aerial vehicle airborne system controls the unmanned aerial vehicle to fly according to data from the unmanned aerial vehicle remote controller, combines RTK satellite positioning information of the unmanned aerial vehicle with positioning calibration data from the RTK base station, calculates the accurate position of the unmanned aerial vehicle, and sends the accurate position to the ground station; the ground station screens the key point coordinates of the unmanned aerial vehicle flight according to the accurate position information sent by the unmanned aerial vehicle airborne system, and generates an unmanned aerial vehicle autonomous tracking path model. The method is beneficial to generating an accurate unmanned aerial vehicle autonomous tracking path model, and brings great convenience to autonomous inspection of the unmanned aerial vehicle.
Description
Technical Field
The invention relates to unmanned aerial vehicle inspection, in particular to a system and a method for generating an unmanned aerial vehicle autonomous tracking path model.
Background
At present, the demand of unmanned aerial vehicles in the industrial field is increasing, and the unmanned aerial vehicles are applied to various aspects such as national defense, agriculture, electric power, fire fighting, public security and the like, and have great demand on occasions where conventional manpower cannot be smoothly completed; especially in the field of patrolling and examining, unmanned aerial vehicle has very important application, and current mode of patrolling and examining mainly has two kinds: firstly, the unmanned aerial vehicle is controlled by the flying hand of the unmanned aerial vehicle to carry out inspection, but the mode extremely depends on the technology of the flying hand, if the same tower needs to be inspected regularly, the potential safety hazard is increased undoubtedly, the consistency of the inspection path is difficult to ensure by the flying hand to control the flight, and the workload of later data analysis is increased; and secondly, generating a waypoint path according to the GPS information and the detailed parameters of the tower, such as the type, the voltage level, the height of the tower, the material of the insulator and the like of the tower, thereby guiding the unmanned aerial vehicle to autonomously patrol. This approach necessarily requires the cooperation of the relevant departments to provide accurate data, is relatively cumbersome, has certain limitations, and may pose a significant risk if the provided data deviates to the extent that the aircraft may crash into a tower. Therefore, the system and the method for generating the unmanned aerial vehicle autonomous tracking path model are important for unmanned aerial vehicle inspection.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides a system and a method for generating an unmanned aerial vehicle autonomous tracking path model.
The purpose of the invention is realized by the following technical scheme: a generation system of an unmanned aerial vehicle autonomous tracking path model comprises:
the unmanned aerial vehicle remote controller is used for enabling a flyer to remotely control the unmanned aerial vehicle, and generating control data to be transmitted to an airborne system of the unmanned aerial vehicle;
the RTK base station is used for acquiring network positioning information of the network RTK base station group through the 4G network, combining the network positioning information with RTK satellite positioning information of the RTK base station, calculating positioning calibration data of the unmanned aerial vehicle and transmitting the positioning calibration data to an airborne system of the unmanned aerial vehicle;
the unmanned aerial vehicle airborne system controls the unmanned aerial vehicle to fly according to data from the unmanned aerial vehicle remote controller, combines RTK satellite positioning information of the unmanned aerial vehicle with positioning calibration data from the RTK base station, calculates the accurate position of the unmanned aerial vehicle, and sends the accurate position to the ground station;
the ground station screens the key point coordinate that unmanned aerial vehicle flies according to the accurate positional information that unmanned aerial vehicle airborne system sent, generates unmanned aerial vehicle and independently tracks the route model, supplies unmanned aerial vehicle to independently patrol and examine the use.
The RTK base station comprises a base station RTK positioning module, a 4G network module, a base station control module and a base station communication module, wherein the base station RTK positioning module and the 4G network module are respectively connected with the base station control module, and the base station control module is connected with the base station communication module;
the base station RTK positioning module is used for acquiring RTK satellite positioning information of an RTK base station;
the 4G network module is used for acquiring network positioning information of a network RTK base station group from a base station system established by a network RTK operator through a 4G network signal;
the base station control module is used for calculating differential correction data by combining RTK satellite positioning information and network positioning information of a base station;
and the base station communication module is used for sending the obtained differential correction data to an unmanned aerial vehicle airborne system.
The unmanned aerial vehicle airborne system comprises a remote control signal receiving module, an airborne RTK positioning module, an airborne communication module and a flight control module, wherein the remote control signal receiving module, the airborne RTK positioning module and the airborne communication module are all connected with the flight control module;
the remote control signal receiving module is used for receiving remote control data sent by the unmanned aerial vehicle remote controller;
the airborne RTK positioning module is used for acquiring RTK positioning information of the unmanned aerial vehicle;
the flight control module is used for controlling the unmanned aerial vehicle to fly according to the received remote control data, combining RTK satellite positioning information of the unmanned aerial vehicle with positioning calibration data from an RTK base station, and calculating the accurate position of the unmanned aerial vehicle;
the airborne communication module is used for receiving differential correction data from an RTK base station or transmitting an accurate position of the unmanned aerial vehicle in the flying process to a ground station.
The model generation method of the generation system of the unmanned aerial vehicle autonomous tracking path model comprises the following steps:
s1, a flier sends flight control data to an unmanned aerial vehicle airborne system by using an unmanned aerial vehicle remote controller, and the unmanned aerial vehicle airborne system controls the unmanned aerial vehicle to fly according to the flight control data to complete one-time manual inspection;
s2, the RTK base station acquires network positioning information of a network RTK base station group through a 4G network, calculates differential correction data by combining RTK satellite positioning information of the RTK base station, and sends the differential correction data to an unmanned aerial vehicle airborne system;
s3, in the manual inspection process, an unmanned aerial vehicle airborne system acquires RTK positioning information of the unmanned aerial vehicle, receives differential correction data, calculates accurate coordinates of the unmanned aerial vehicle and transmits the coordinates to a ground station;
and S4, screening key point coordinates of the unmanned aerial vehicle flight according to the accurate coordinates sent by the unmanned aerial vehicle airborne system in the manual inspection process by the ground station, and generating an unmanned aerial vehicle autonomous tracking path model for autonomous inspection of the unmanned aerial vehicle.
Wherein the step S2 includes the following substeps:
s201, the RTK base station acquires RTK satellite positioning information of the base station for n +1 times, a space coordinate system is established, the acquired RTK satellite positioning information for n +1 times is converted into coordinate points under the space coordinate system, any point P is set as An actual coordinate of the RTK base station, and positioning errors delta of other coordinate points A1, A2, aA1,δA2,...,δAnThe RTK calibration data is obtained by performing a mean processing as follows
S202, the RTK base station acquires network positioning information of the RTK base station for m times through a 4G network, and establishes a base station geodetic coordinate system; converting the obtained m-time network positioning information into coordinate points B1, B2,. Bm under a base station geodetic coordinate system, and simultaneously converting a coordinate point P under the base station geodetic coordinate system;
calculating a positioning error δ between coordinate points B1, B2B1,δB2,., δ m, performing mean processing to obtain network calibration data as follows
S203, calibrating the RTK data
And network calibration data
Difference is carried out to obtain difference data deltap：
S204, repeating the steps S201 to S203 for k times to obtain k differential data deltap1,Δp2,...,ΔpkThe mean value processing is performed as follows to obtain difference correction data
Send to unmanned aerial vehicle airborne system:
the step S3 includes the following sub-steps:
s301, in the manual inspection process, an unmanned aerial vehicle airborne system acquires RTK positioning information of the unmanned aerial vehicle;
s302, converting the obtained RTK positioning information into a coordinate Q under a base station geodetic coordinate system by an unmanned aerial vehicle airborne system;
s303, the unmanned aerial vehicle airborne system corrects the coordinate Q according to the difference correction data, and the actual positioning coordinate of the unmanned aerial vehicle obtained through resolving is as follows:
and S304, the unmanned aerial vehicle airborne system transmits the calculated actual positioning coordinates of the unmanned aerial vehicle to the ground station.
The invention has the beneficial effects that: according to the unmanned aerial vehicle autonomous inspection system, manual inspection of the unmanned aerial vehicle is sequentially completed under the control of the flying hand, the position information of the unmanned aerial vehicle in the inspection process is positioned and sent to the ground station for key point screening, an unmanned aerial vehicle autonomous tracking path model is generated for the unmanned aerial vehicle autonomous inspection, the problem that the unmanned aerial vehicle inspection depends on the flying hand excessively is avoided, and the consistency of each inspection path is also ensured; meanwhile, the invention only needs to start up and then carries out coordinate positioning once through the 4G network, and then combines with the RTK satellite positioning information to obtain the differential correction data, the unmanned aerial vehicle can not carry out coordinate positioning on the RTK base station again in the flying process, only needs the RTK base station to transmit the differential correction data to the unmanned aerial vehicle, and no matter whether the 4G signal is lost or not, the unmanned aerial vehicle can not be influenced by the reasons of obstacle shielding and the like, and the invention has the advantages of strong anti-jamming capability and stable operation.
Drawings
FIG. 1 is a schematic block diagram of the system of the present invention;
FIG. 2 is a schematic diagram of a hardware architecture of an RTK base station in an embodiment;
FIG. 3 is a flow chart of the method of the present invention.
Detailed Description
The technical solutions of the present invention are further described in detail below with reference to the accompanying drawings, but the scope of the present invention is not limited to the following.
As shown in fig. 1, a system for generating an autonomous tracking path model of a drone includes:
the unmanned aerial vehicle remote controller is used for enabling a flyer to remotely control the unmanned aerial vehicle, and generating control data to be transmitted to an airborne system of the unmanned aerial vehicle;
the RTK base station is used for acquiring network positioning information of the network RTK base station group through the 4G network, combining the network positioning information with RTK satellite positioning information of the RTK base station, calculating positioning calibration data of the unmanned aerial vehicle and transmitting the positioning calibration data to an airborne system of the unmanned aerial vehicle;
the unmanned aerial vehicle airborne system controls the unmanned aerial vehicle to fly according to data from the unmanned aerial vehicle remote controller, combines RTK satellite positioning information of the unmanned aerial vehicle with positioning calibration data from the RTK base station, calculates the accurate position of the unmanned aerial vehicle, and sends the accurate position to the ground station;
the ground station screens the key point coordinate that unmanned aerial vehicle flies according to the accurate positional information that unmanned aerial vehicle airborne system sent, generates unmanned aerial vehicle and independently tracks the route model, supplies unmanned aerial vehicle to independently patrol and examine the use.
The RTK base station comprises a base station RTK positioning module, a 4G network module, a base station control module and a base station communication module, wherein the base station RTK positioning module and the 4G network module are respectively connected with the base station control module, and the base station control module is connected with the base station communication module;
the base station RTK positioning module is used for acquiring RTK satellite positioning information of an RTK base station;
the 4G network module is used for acquiring network positioning information of a network RTK base station group from a base station system established by a network RTK operator through a 4G network signal;
the base station control module is used for calculating differential correction data by combining RTK satellite positioning information and network positioning information of a base station;
and the base station communication module is used for sending the obtained differential correction data to an unmanned aerial vehicle airborne system.
The unmanned aerial vehicle airborne system comprises a remote control signal receiving module, an airborne RTK positioning module, an airborne communication module and a flight control module, wherein the remote control signal receiving module, the airborne RTK positioning module and the airborne communication module are all connected with the flight control module;
the remote control signal receiving module is used for receiving remote control data sent by the unmanned aerial vehicle remote controller;
the airborne RTK positioning module is used for acquiring RTK positioning information of the unmanned aerial vehicle;
the flight control module is used for controlling the unmanned aerial vehicle to fly according to the received remote control data, combining RTK satellite positioning information of the unmanned aerial vehicle with positioning calibration data from an RTK base station, and calculating the accurate position of the unmanned aerial vehicle;
the airborne communication module is used for receiving differential correction data from an RTK base station or transmitting an accurate position of the unmanned aerial vehicle in the flying process to a ground station.
In an embodiment of the present application, a hardware architecture of the RTK base station is as shown in fig. 2, where a base station RTK positioning module is integrated in an RTK board card, a 4G network module is integrated in a 4G board card, a base station control module is integrated in a control board card, and a base station communication module is integrated in a data link; in the embodiment, the RTK base station further comprises a power management module, the power management module comprises a switch circuit, a power processing unit and an LED key display control unit, an external power is transmitted to the power processing unit and the LED key display control unit through a power switch, the power processing unit supplies power to the 4G board card, the RTK board card, the control board card and the data link respectively, wherein the power processing unit detects low power of the external power according to an input voltage and transmits a detection result to the control board card, and the control board card performs under-voltage turn-off control on the switch circuit according to the detection result; meanwhile, the LED display control unit comprises an LED display and a control panel, the LED display displays the working state of the RTK base station, and the control panel is used for workers to manually control the RTK base station.
As shown in fig. 3, the model generation method of the generation system of the autonomous tracking path model of the unmanned aerial vehicle includes the following steps:
s1, a flier sends flight control data to an unmanned aerial vehicle airborne system by using an unmanned aerial vehicle remote controller, and the unmanned aerial vehicle airborne system controls the unmanned aerial vehicle to fly according to the flight control data to complete one-time manual inspection;
s2, the RTK base station acquires network positioning information of a network RTK base station group through a 4G network, calculates differential correction data by combining RTK satellite positioning information of the RTK base station, and sends the differential correction data to an unmanned aerial vehicle airborne system;
s3, in the manual inspection process, an unmanned aerial vehicle airborne system acquires RTK positioning information of the unmanned aerial vehicle, receives differential correction data, calculates accurate coordinates of the unmanned aerial vehicle and transmits the coordinates to a ground station;
and S4, screening key point coordinates of the unmanned aerial vehicle flight according to the accurate coordinates sent by the unmanned aerial vehicle airborne system in the manual inspection process by the ground station, and generating an unmanned aerial vehicle autonomous tracking path model for autonomous inspection of the unmanned aerial vehicle.
Wherein the step S2 includes the following substeps:
s201, the RTK base station acquires RTK satellite positioning information of the base station for n +1 times, a space coordinate system is established, the acquired RTK satellite positioning information for n +1 times is converted into coordinate points under the space coordinate system, any point P is set as An actual coordinate of the RTK base station, and positioning errors delta of other coordinate points A1, A2, aA1,δA2,...,δAnThe RTK calibration data is obtained by performing a mean processing as follows
S202, the RTK base station acquires network positioning information of the RTK base station for m times through a 4G network, and establishes a base station geodetic coordinate system; converting the obtained m-time network positioning information into coordinate points B1, B2,. Bm under a base station geodetic coordinate system, and simultaneously converting a coordinate point P under the base station geodetic coordinate system;
calculating a positioning error δ between coordinate points B1, B2B1,δB2,., δ m, performing mean processing to obtain network calibration data as follows
S203, calibrating the RTK data
And network calibration data
Difference is carried out to obtain difference data deltap：
S204, repeating the steps S201 to S203 for k times to obtain k differential data deltap1,Δp2,...,ΔpkThe mean value processing is performed as follows to obtain difference correction data
Send to unmanned aerial vehicle airborne system:
the step S3 includes the following sub-steps:
s301, in the manual inspection process, an unmanned aerial vehicle airborne system acquires RTK positioning information of the unmanned aerial vehicle;
s302, converting the obtained RTK positioning information into a coordinate Q under a base station geodetic coordinate system by an unmanned aerial vehicle airborne system;
s303, the unmanned aerial vehicle airborne system corrects the coordinate Q according to the difference correction data, and the actual positioning coordinate of the unmanned aerial vehicle obtained through resolving is as follows:
and S304, the unmanned aerial vehicle airborne system transmits the calculated actual positioning coordinates of the unmanned aerial vehicle to the ground station.
In conclusion, the unmanned aerial vehicle automatic tracking system completes the manual inspection of the unmanned aerial vehicle in sequence under the control of the flying hand, positions the position information of the unmanned aerial vehicle in the inspection process, and sends the position information to the ground station for key point screening to generate an unmanned aerial vehicle automatic tracking path model for the unmanned aerial vehicle to automatically inspect, so that the problem that the unmanned aerial vehicle inspection depends on the flying hand excessively is avoided, and the consistency of the inspection path at each time is also ensured.
Meanwhile, in the process of positioning the unmanned aerial vehicle, if the GPS data is directly utilized for positioning, the positioning precision is within the range of 2.5 meters, and the error is large; if the conventional RTK base station is directly used for positioning the unmanned aerial vehicle, the positioning offset of the unmanned aerial vehicle is irregular due to the fact that the RTK base station has offset, the offset cannot be copied and the offset is irregular, the positioning offset of the unmanned aerial vehicle is irregular and the unmanned aerial vehicle can be irregularly positioned, although the RTK positioning accuracy can reach centimeter level, the method is not suitable for the autonomous tracking high-accuracy positioning of the unmanned aerial vehicle; if the network RTK is used for directly calibrating the coordinate system of the unmanned aerial vehicle, the coordinates of the unmanned aerial vehicle need to be continuously corrected through the 4G network, and most importantly, if an obstacle or a remote area occurs, the 4G network is not good, data loss easily occurs, the positioning coordinates of the unmanned aerial vehicle are misaligned, and accidents occur. According to the invention, the differential correction data is calculated through the network positioning information of the RTK base station and the RTK satellite positioning information, and is transmitted to the unmanned aerial vehicle for positioning correction, so that the precise coordinate of the unmanned aerial vehicle under the base station coordinate system is obtained, the positioning accuracy of the unmanned aerial vehicle is improved, meanwhile, the invention only needs to perform one-time coordinate positioning through the 4G network after starting up, and then the differential correction data can be obtained by combining the RTK satellite positioning information, the RTK base station can not be subjected to coordinate positioning again in the flying process of the unmanned aerial vehicle, only needs to transmit the differential correction data to the unmanned aerial vehicle through the RTK base station, and no matter whether 4G signals are lost or not, obstacles are shielded, and the like, so that the unmanned aerial vehicle positioning system has the advantages of strong anti-jamming capability and stable operation.
Finally, it is to be understood that the foregoing is illustrative of the preferred embodiments of the present invention and is not to be construed as limited to the disclosed forms, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (6)
1. The utility model provides a generating system of unmanned aerial vehicle autonomous tracking route model which characterized in that: the method comprises the following steps:
the unmanned aerial vehicle remote controller is used for enabling a flyer to remotely control the unmanned aerial vehicle, and generating control data to be transmitted to an airborne system of the unmanned aerial vehicle;
the RTK base station is used for acquiring network positioning information of the network RTK base station group through the 4G network, combining the network positioning information with RTK satellite positioning information of the RTK base station, calculating positioning calibration data of the unmanned aerial vehicle, and transmitting the positioning calibration data to an airborne system of the unmanned aerial vehicle:
the RTK base station acquires RTK satellite positioning information of the base station for n +1 times, establishes a space coordinate system, converts the acquired RTK satellite positioning information for n +1 times into coordinate points under the space coordinate system, sets any point P as An actual coordinate of the RTK base station, and calculates positioning errors delta of other coordinate points A1, A2,. An and the point PA1,δA2,...,δAnThe RTK calibration data is obtained by performing a mean processing as follows
The RTK base station acquires network positioning information of the RTK base station for m times through a 4G network, and establishes a base station geodetic coordinate system; converting the obtained m-time network positioning information into coordinate points B1, B2,. Bm under a base station geodetic coordinate system, and simultaneously converting a coordinate point P under the base station geodetic coordinate system;
calculating a positioning error δ between coordinate points B1, B2B1,δB2,., δ m, performing mean processing to obtain network calibration data as follows
Calibrating RTK data
And network calibration data
Difference is carried out to obtain difference data deltap：
Repeating the operation k times to obtain k differential data deltap1,Δp2,...,ΔpkThe mean value processing is performed as follows to obtain difference correction data
Send to unmanned aerial vehicle airborne system:
the unmanned aerial vehicle airborne system controls the unmanned aerial vehicle to fly according to data from the unmanned aerial vehicle remote controller, combines RTK satellite positioning information of the unmanned aerial vehicle with positioning calibration data from the RTK base station, calculates the accurate position of the unmanned aerial vehicle, and sends the accurate position to the ground station;
the ground station screens the key point coordinate that unmanned aerial vehicle flies according to the accurate positional information that unmanned aerial vehicle airborne system sent, generates unmanned aerial vehicle and independently tracks the route model, supplies unmanned aerial vehicle to independently patrol and examine the use.
2. The system for generating the unmanned aerial vehicle autonomous tracking path model according to claim 1, wherein: the RTK base station comprises a base station RTK positioning module, a 4G network module, a base station control module and a base station communication module, wherein the base station RTK positioning module and the 4G network module are respectively connected with the base station control module, and the base station control module is connected with the base station communication module;
the base station RTK positioning module is used for acquiring RTK satellite positioning information of an RTK base station;
the 4G network module is used for acquiring network positioning information of a network RTK base station group from a base station system established by a network RTK operator through a 4G network signal;
the base station control module is used for calculating differential correction data by combining RTK satellite positioning information and network positioning information of a base station;
and the base station communication module is used for sending the obtained differential correction data to an unmanned aerial vehicle airborne system.
3. The system for generating the unmanned aerial vehicle autonomous tracking path model according to claim 1, wherein: the unmanned aerial vehicle airborne system comprises a remote control signal receiving module, an airborne RTK positioning module, an airborne communication module and a flight control module, wherein the remote control signal receiving module, the airborne RTK positioning module and the airborne communication module are all connected with the flight control module;
the remote control signal receiving module is used for receiving remote control data sent by the unmanned aerial vehicle remote controller;
the airborne RTK positioning module is used for acquiring RTK positioning information of the unmanned aerial vehicle;
the flight control module is used for controlling the unmanned aerial vehicle to fly according to the received remote control data, combining RTK satellite positioning information of the unmanned aerial vehicle with positioning calibration data from an RTK base station, and calculating the accurate position of the unmanned aerial vehicle;
the airborne communication module is used for receiving differential correction data from an RTK base station or transmitting an accurate position of the unmanned aerial vehicle in the flying process to a ground station.
4. The model generation method of the generation system of the unmanned aerial vehicle autonomous tracking path model according to any one of claims 1 to 3, characterized in that: the method comprises the following steps:
s1, a flier sends flight control data to an unmanned aerial vehicle airborne system by using an unmanned aerial vehicle remote controller, and the unmanned aerial vehicle airborne system controls the unmanned aerial vehicle to fly according to the flight control data to complete one-time manual inspection;
s2, the RTK base station acquires network positioning information of a network RTK base station group through a 4G network, calculates differential correction data by combining RTK satellite positioning information of the RTK base station, and sends the differential correction data to an unmanned aerial vehicle airborne system;
s3, in the manual inspection process, an unmanned aerial vehicle airborne system acquires RTK positioning information of the unmanned aerial vehicle, receives differential correction data, calculates accurate coordinates of the unmanned aerial vehicle and transmits the coordinates to a ground station;
and S4, screening key point coordinates of the unmanned aerial vehicle flight according to the accurate coordinates sent by the unmanned aerial vehicle airborne system in the manual inspection process by the ground station, and generating an unmanned aerial vehicle autonomous tracking path model for autonomous inspection of the unmanned aerial vehicle.
5. The model generation method of the generation system of the unmanned aerial vehicle autonomous tracking path model according to claim 4, characterized in that: the step S2 includes the following sub-steps:
s201, the RTK base station acquires RTK satellite positioning information of the base station for n +1 times, a space coordinate system is established, the acquired RTK satellite positioning information for n +1 times is converted into coordinate points under the space coordinate system, any point P is set as An actual coordinate of the RTK base station, and positioning errors delta of other coordinate points A1, A2, aA1,δA2,...,δAnThe RTK calibration data is obtained by performing a mean processing as follows
S202, the RTK base station acquires network positioning information of the RTK base station for m times through a 4G network, and establishes a base station geodetic coordinate system; converting the obtained m-time network positioning information into coordinate points B1, B2,. Bm under a base station geodetic coordinate system, and simultaneously converting a coordinate point P under the base station geodetic coordinate system;
calculating a positioning error δ between coordinate points B1, B2B1,δB2,., δ m, performing mean processing to obtain network calibration data as follows
S203, calibrating the RTK data
And network calibration data
Difference is carried out to obtain difference data deltap：
S204, repeating the steps S201 to S203 for k times to obtain k differential data deltap1,Δp2,...,ΔpkThe mean value processing is performed as follows to obtain difference correction data
Send to unmanned aerial vehicle airborne system:
6. the model generation method of the generation system of the unmanned aerial vehicle autonomous tracking path model according to claim 4, characterized in that: the step S3 includes the following sub-steps:
s301, in the manual inspection process, an unmanned aerial vehicle airborne system acquires RTK positioning information of the unmanned aerial vehicle;
s302, converting the obtained RTK positioning information into a coordinate Q under a base station geodetic coordinate system by an unmanned aerial vehicle airborne system;
s303, the unmanned aerial vehicle airborne system corrects the coordinate Q according to the difference correction data, and the actual positioning coordinate of the unmanned aerial vehicle obtained through resolving is as follows:
and S304, the unmanned aerial vehicle airborne system transmits the calculated actual positioning coordinates of the unmanned aerial vehicle to the ground station.
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| CN112334791B (en) * | 2019-11-05 | 2024-03-08 | 深圳市大疆创新科技有限公司 | Positioning method, positioning system, remote control equipment and RTK module |
| WO2021134687A1 (en) * | 2019-12-31 | 2021-07-08 | 深圳市大疆创新科技有限公司 | Waypoint planning method, control terminal, positioning device, system, and storage medium |
| CN112673283A (en) * | 2020-03-31 | 2021-04-16 | 深圳市大疆创新科技有限公司 | Positioning method, control terminal and movable platform |
| WO2021232273A1 (en) * | 2020-05-20 | 2021-11-25 | 深圳市大疆创新科技有限公司 | Unmanned aerial vehicle and control method and apparatus therefor, remote control terminal, and unmanned aerial vehicle system |
| CN111896983B (en) * | 2020-06-30 | 2023-12-05 | 昆明能讯科技有限责任公司 | Method for correcting positioning coordinates of non-RTK multi-rotor unmanned aerial vehicle |
| CN113985910B (en) * | 2021-09-09 | 2023-07-18 | 常州希米智能科技有限公司 | Unmanned aerial vehicle control method and system combining mobile network and satellite positioning |
| CN115047916A (en) * | 2022-07-25 | 2022-09-13 | 南方电网电力科技股份有限公司 | Unmanned aerial vehicle control method, airborne terminal, electronic equipment and storage medium |
| CN115273538A (en) * | 2022-08-29 | 2022-11-01 | 王炜程 | GNSS-RTK technology-based parking space detection system and deployment and working methods thereof |
| CN115617074B (en) * | 2022-10-21 | 2024-09-06 | 广东中科瑞泰智能科技有限公司 | Unmanned aerial vehicle control method, device and system based on redundant positioning mechanism |
| CN118945810B (en) * | 2024-07-22 | 2025-05-09 | 泰州城发数字科技有限公司 | Unmanned aerial vehicle coordinate calibration method, system and medium based on three-dimensional coordinates |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102590840A (en) * | 2012-02-10 | 2012-07-18 | 中国测绘科学研究院 | Satellite positioning carrier phase difference method |
| CN103235595A (en) * | 2013-04-27 | 2013-08-07 | 湖南科技大学 | Control system and control method of outdoor micro ground swarm robot |
| CN104765043A (en) * | 2015-03-17 | 2015-07-08 | 广东工业大学 | High-integration satellite navigation continuously-operating reference station |
| CN105403901A (en) * | 2015-12-30 | 2016-03-16 | 广州中海达卫星导航技术股份有限公司 | DGNSS satellite orbit deviation correction method |
| CN108089207A (en) * | 2017-06-06 | 2018-05-29 | 中国科学院光电研究院 | A kind of NRTK based on single poor ionosphere modeling enhances localization method |
| CN108334104A (en) * | 2017-12-28 | 2018-07-27 | 成都优艾维智能科技有限责任公司 | A kind of autonomous cruising inspection system of unmanned plane and method based on RTK positioning |
| CN208384121U (en) * | 2018-07-06 | 2019-01-15 | 西北农林科技大学 | A kind of calibrator (-ter) unit based on the base station RTK |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2003127994A (en) * | 2001-10-24 | 2003-05-08 | Kansai Electric Power Co Inc:The | Unmanned flying object control system |
| JP6813427B2 (en) * | 2016-08-31 | 2021-01-13 | パナソニック インテレクチュアル プロパティ コーポレーション オブ アメリカＰａｎａｓｏｎｉｃ Ｉｎｔｅｌｌｅｃｔｕａｌ Ｐｒｏｐｅｒｔｙ Ｃｏｒｐｏｒａｔｉｏｎ ｏｆ Ａｍｅｒｉｃａ | Positioning systems, positioning methods, and mobile robots |
-
2019
- 2019-01-24 CN CN201910067216.2A patent/CN109839951B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102590840A (en) * | 2012-02-10 | 2012-07-18 | 中国测绘科学研究院 | Satellite positioning carrier phase difference method |
| CN103235595A (en) * | 2013-04-27 | 2013-08-07 | 湖南科技大学 | Control system and control method of outdoor micro ground swarm robot |
| CN104765043A (en) * | 2015-03-17 | 2015-07-08 | 广东工业大学 | High-integration satellite navigation continuously-operating reference station |
| CN105403901A (en) * | 2015-12-30 | 2016-03-16 | 广州中海达卫星导航技术股份有限公司 | DGNSS satellite orbit deviation correction method |
| CN108089207A (en) * | 2017-06-06 | 2018-05-29 | 中国科学院光电研究院 | A kind of NRTK based on single poor ionosphere modeling enhances localization method |
| CN108334104A (en) * | 2017-12-28 | 2018-07-27 | 成都优艾维智能科技有限责任公司 | A kind of autonomous cruising inspection system of unmanned plane and method based on RTK positioning |
| CN208384121U (en) * | 2018-07-06 | 2019-01-15 | 西北农林科技大学 | A kind of calibrator (-ter) unit based on the base station RTK |
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