CN215813349U - Unmanned aerial vehicle formation target real-time tracking and modeling system - Google Patents
Unmanned aerial vehicle formation target real-time tracking and modeling system Download PDFInfo
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- CN215813349U CN215813349U CN202120210171.2U CN202120210171U CN215813349U CN 215813349 U CN215813349 U CN 215813349U CN 202120210171 U CN202120210171 U CN 202120210171U CN 215813349 U CN215813349 U CN 215813349U
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Abstract
The utility model belongs to the technical field of robots and automatic control, in particular to a system for real-time tracking and modeling of targets formed by unmanned aerial vehicles, which aims at solving the problems that the current unmanned aerial vehicles have low cruising range, low shooting efficiency, small search range, limited shooting angle and the like if only a single unmanned aerial vehicle is used for shooting air routes, and the real-time tracking and three-dimensional reconstruction of the targets are difficult to realize, it includes four rotor flying robot and sensor module, and four rotor flying robot include unmanned aerial vehicle frame, carbon fiber stay tube, flight cloud platform, paddle, DC brushless motor and battery, and sensor module and battery all set up the top central point in the unmanned aerial vehicle frame and put, and sensor module and DC brushless motor all are connected with the battery, and the carbon fiber stay tube is a plurality of, and a plurality of carbon fiber stay tubes all set up the bottom in the unmanned aerial vehicle frame. The unmanned aerial vehicle forms the cluster, and the target is scanned and reconstructed more efficiently.
Description
Technical Field
The utility model relates to the technical field of robots and automatic control, in particular to a real-time tracking and modeling system for targets formed by unmanned aerial vehicles.
Background
At present, unmanned aerial vehicle relies on its year thing advantage, provides numerous solutions in the application of aspects such as logistics distribution, pipeline inspection, military reconnaissance, agricultural plant protection, high definition aerial photography. Compared with traditional ground or helicopter search and rescue, the unmanned aerial vehicle is lower in cost and higher in efficiency. The ability of single unmanned aerial vehicle has certain limit all the time, if search range is little, environmental sensitivity is low, operating duration is short etc. compare in single unmanned aerial vehicle, many unmanned aerial vehicles cooperate and can improve task efficiency greatly. Because a plurality of unmanned aerial vehicles can share information, the perception capability of the whole unmanned aerial vehicle cluster to the environment is greatly improved.
In the prior art, a high-definition camera is carried on an unmanned aerial vehicle to be subjected to aerial photography, patrol and reconnaissance, and is easily influenced by environments such as visible light, haze and the like, particularly, the camera almost loses shooting capability at night, and the camera acquires plane data. At present, unmanned aerial vehicle continuation of the journey is generally not high, if only use single unmanned aerial vehicle to carry out the airline and shoot, has to shoot inefficiency, and the search range is little, shoots the limited scheduling problem of angle, is difficult to realize the real-time tracking and the three-dimensional reconstruction to the target.
SUMMERY OF THE UTILITY MODEL
The utility model aims to solve the defects that the current unmanned aerial vehicle has low cruising range, and the problems of low shooting efficiency, small search range, limited shooting angle and the like exist if only a single unmanned aerial vehicle is used for shooting a flight path, and the real-time tracking and three-dimensional reconstruction of a target are difficult to realize.
In order to achieve the purpose, the utility model adopts the following technical scheme:
the utility model provides a system for unmanned aerial vehicle formation target real-time tracking and modeling, including four rotor flying robot and sensor module, four rotor flying robot includes the unmanned aerial vehicle frame, the carbon fiber stay tube, the flight cloud platform, the paddle, direct current brushless motor and battery, sensor module and battery all set up the top central point in the unmanned aerial vehicle frame and put, sensor module and direct current brushless motor all are connected with the battery, the carbon fiber stay tube is a plurality of, a plurality of carbon fiber stay tubes all set up the bottom in the unmanned aerial vehicle frame, paddle and direct current brushless motor are four, four paddles and four direct current brushless motor's output shaft, four direct current brushless motor all set up the top in the unmanned aerial vehicle frame, sensor module includes the machine carries the computer, inertia measuring unit, three-dimensional laser radar, 5G communication module and GPS orientation module, inertia measuring unit, three-dimensional laser radar, the model is established to the sensor module, The 5G communication module and the GPS positioning module are both connected with an airborne computer, and the three-dimensional laser radar is installed on the flying holder.
Preferably, the quad-rotor flying robot and the sensor module form an unmanned aerial vehicle formation.
Preferably, the unmanned aerial vehicles form a formation to carry a three-dimensional laser radar.
Preferably, the unmanned aerial vehicle formation carries an airborne computer, and sends a speed command to the electronic speed regulator through a PID control algorithm to control the rotation direction and the rotation speed of the motor so as to control the flight direction and the flight attitude of the unmanned aerial vehicle.
Preferably, the unmanned aerial vehicle formation carries on the inertia measurement unit, and the inertia measurement unit includes triaxial gyroscope, triaxial acceleration sensor, earth magnetometer and barometer, carries out data fusion and attitude solution through the kalman filter algorithm.
Preferably, the unmanned aerial vehicle formation carries on GPS positioning module, and GPS positioning module and inertial measurement unit constitute unmanned aerial vehicle positioning system.
Preferably, the unmanned aerial vehicle formation is controlled by adopting bionic formation, and natural wild goose migration is simulated to realize unmanned aerial vehicle formation control.
Preferably, the unmanned aerial vehicle formation scanning method adopts a method of simulating topology optimization in mechanical structure design and optimizing material distribution according to given load conditions, constraint conditions and performance indexes. And optimizing the unmanned aerial vehicle cluster arrangement in the unmanned aerial vehicle target three-dimensional scanning process.
Preferably, the three-dimensional reconstruction method includes calibrating point cloud data scanned by different laser radars, identifying feature points and feature planes in point clouds, aligning time axes of the point cloud data of the different laser radars, matching and splicing point cloud features, and achieving three-dimensional reconstruction.
Preferably, the 5G module carried by the unmanned aerial vehicle flight platform relies on the characteristics of high speed, low time delay, high reliability and large data communication of the communication network thereof, and achieves the formation interconnection, real-time, clustering and intellectualization of the unmanned aerial vehicle.
Preferably, the three-dimensional laser radar is carried on the unmanned aerial vehicle cluster, and the three-dimensional model of the object is constructed by scanning the target in a surrounding and omnibearing manner and fusing data acquired by different laser radars according to a point cloud feature matching algorithm. Relative position relation between unmanned aerial vehicles is obtained through mutual scanning between laser radars to according to the coincidence condition of point cloud characteristic, adjust unmanned aerial vehicle position and gesture, accomplish scanning work with unmanned aerial vehicle as few as possible.
Preferably, the 5G communication module transmits data scanned by the plurality of laser radars in different directions to the headquarters by using the characteristics of 5G communication, high bandwidth and low time delay.
Preferably, the GPS positioning module can slightly acquire the position information of the unmanned aerial vehicle, and the positioning precision of the unmanned aerial vehicle is improved by matching with the inertial measurement unit.
Preferably, the real-time tracking and modeling method for the unmanned aerial vehicle formation targets specifically comprises the following steps:
s1: bionic formation control: in nature, biological groups can orderly coordinate the movement of the whole group through simple information interaction. Planning the route of the unmanned aerial vehicle, selecting the unmanned aerial vehicle station closest to the destination to fly the unmanned aerial vehicle, simulating wild goose migration flight, and setting the behavior mode of the unmanned aerial vehicle, if: how to assemble the unmanned aerial vehicles, what form of formation to arrange, how to avoid the mutual collision between barrier and the aircraft. Similar to the experienced wild gooses in band, the wild gooses in band make a decision on the whole goose group, and the unmanned machine head-forming machine senses the danger in front at first and adjusts the formation and the flying direction in advance.
S2: obstacle is kept away to intelligence: in the task execution process of the unmanned aerial vehicle, obstacle avoidance is the most basic requirement, particularly when the task is executed in an unknown environment, the unmanned aerial vehicle can only rely on the decision-making capability of the unmanned aerial vehicle, in the bionic formation control, the obstacle avoidance strategy is that the real-time three-dimensional reconstruction is carried out on the surrounding small-range environment by means of the carried three-dimensional laser radar, the obstacle identification is carried out on point cloud data, and the corresponding adjustment is carried out according to the size and distance of the obstacle and the self state of the unmanned aerial vehicle.
S3: "topology optimization" is a method of simulating and optimizing material distribution based on given load condition, constraint condition and performance index by means of topology optimization in mechanical structure design. The unmanned aerial vehicle cluster is arranged and optimized in the unmanned aerial vehicle target three-dimensional scanning process, the three-dimensional laser radar scanning range is given, the distance between the unmanned aerial vehicles is restricted, different laser radars scan the coincidence condition of target three-dimensional information, the unmanned aerial vehicles are reasonably arranged, space resources are reasonably distributed, and the number of unmanned aerial vehicles in the scanning work is reduced as much as possible.
S4: three-dimensional reconstruction: the method comprises the steps of calibrating point cloud data scanned by different laser radars, converting the point cloud data into the same coordinate system for facilitating subsequent point cloud splicing, identifying characteristic points and characteristic surfaces in point clouds, aligning time axes of the point cloud data of the different laser radars, matching and splicing point cloud characteristics, and achieving three-dimensional reconstruction.
S5: 5G communication: the digital cellular mobile 5G communication network has the characteristics of high speed, low time delay, high reliability and big data communication, the formation interconnection, real-time, clustering and intellectualization of the unmanned aerial vehicles are realized, the information collected by the unmanned aerial vehicle cluster is transmitted back to the master station through the 5G network by the cluster, and the master station makes decision deployment by means of the data.
Compared with the prior art, the utility model has the advantages that:
the utility model is different from the defects that an unmanned aerial vehicle cluster carries a camera, has high requirements on light and visibility and can shoot a plane scene by itself, carries a three-dimensional laser radar to carry out three-dimensional scanning reconstruction on a target, and has more visual display and higher precision;
the unmanned aerial vehicle carries the laser radar to scan, and the unmanned aerial vehicle realizes the exploration of dangerous places and places with potential safety hazards, so that the safety of personnel is ensured.
The unmanned aerial vehicle forms the cluster, and the target is scanned and reconstructed more efficiently.
Drawings
FIG. 1 is a system structure diagram of a four-rotor flying robot platform sensor construction scheme provided by the utility model;
FIG. 2 is a flow chart of a system for real-time tracking and modeling of targets formed by unmanned aerial vehicles according to the present invention;
FIG. 3 is a flow chart of a point cloud stitching three-dimensional reconstruction method provided by the utility model;
FIG. 4 is a schematic diagram of a multi-lidar scanning range according to the present invention;
fig. 5 is a schematic diagram of the implementation of real-time tracking and modeling of the formation targets of the unmanned aerial vehicles according to the present invention.
In the figure: 11. a four-rotor flying robot; 12. a sensor module; 111. an unmanned aerial vehicle frame; 112. a carbon fiber support tube; 113. a flying head; 114. a paddle; 115. a DC brushless motor; 116. a battery; 12. a sensor module; 121. an onboard computer; 122. an inertial measurement unit; 123. a three-dimensional laser radar; 124. a 5G communication module; 125. and a GPS positioning module.
Detailed Description
The technical solutions in the embodiments will be described clearly and completely with reference to the drawings in the embodiments, and it is obvious that the described embodiments are only a part of the embodiments, but not all embodiments.
Example one
Referring to fig. 1-5, a system for real-time tracking and modeling of formation targets of unmanned aerial vehicles, including a quad-rotor flying robot 11 and a sensor module 12, the quad-rotor flying robot 11 includes an unmanned aerial vehicle frame 111, a carbon fiber support tube 112, a flying pan-tilt 113, a plurality of blades 114, a dc brushless motor 115 and a battery 116, the sensor module 12 and the battery 116 are both disposed at a top center position of the unmanned aerial vehicle frame 111, the sensor module 12 and the dc brushless motor 115 are both connected to the battery 116, the carbon fiber support tube 112 is multiple, the carbon fiber support tubes 112 are all disposed at a bottom of the unmanned aerial vehicle frame 111, the blades 114 and the dc brushless motor 115 are four, the four blades 114 are connected to output shafts of the dc brushless motor 115, the dc brushless motor 115 are all disposed at a top of the unmanned aerial vehicle frame 111, the sensor module 12 includes an onboard computer 121, an inertia measurement unit 122, a dc brushless motor 115, a battery 116, a battery module, Three-dimensional laser radar 123, 5G communication module 124 and GPS orientation module 125, inertial measurement unit 122, three-dimensional laser radar 123, 5G communication module 124 and GPS orientation module 125 all are connected with airborne computer 121, and three-dimensional laser radar 123 installs on flight cloud platform 113.
In this embodiment, the quad-rotor flying robot 11 and the sensor module 12 form an unmanned aerial vehicle formation.
In this embodiment, the unmanned aerial vehicle formation carries the three-dimensional laser radar 123.
In this embodiment, the unmanned aerial vehicle formation onboard computer 121 sends a speed command to the electronic speed regulator through a PID control algorithm to control the rotation direction and rotation speed of the motor, so as to control the flight direction and attitude of the unmanned aerial vehicle.
In this embodiment, the unmanned aerial vehicle formation carries on the inertia measurement unit 122, and the inertia measurement unit 122 includes triaxial gyroscope, triaxial acceleration sensor, geomagnetism meter and barometer, carries out data fusion and attitude solution through kalman filter algorithm.
In this embodiment, the unmanned aerial vehicle formation carries the GPS positioning module 125, and the GPS positioning module 125 and the inertial measurement unit 122 constitute an unmanned aerial vehicle positioning system.
In the embodiment, the unmanned aerial vehicle formation adopts bionic formation control to simulate natural wild goose migration to realize unmanned aerial vehicle formation control.
In this embodiment, the unmanned aerial vehicle formation scanning method adopts a method of optimizing topology in the design of a simulated mechanical structure and optimizing material distribution according to a given load condition, a constraint condition and a performance index. And optimizing the unmanned aerial vehicle cluster arrangement in the unmanned aerial vehicle target three-dimensional scanning process.
In this embodiment, the three-dimensional reconstruction method includes calibrating point cloud data scanned by different laser radars, identifying feature points and feature planes in point clouds, aligning time axes of the point cloud data of the different laser radars, matching and splicing point cloud features, and achieving three-dimensional reconstruction.
In this embodiment, the 5G module carried by the unmanned aerial vehicle flight platform relies on the characteristics of its communication network, such as high speed, low time delay, high reliability and large data communication, and realizes the formation interconnection, real-time, clustering and intellectualization of the unmanned aerial vehicle.
In this embodiment, the three-dimensional laser radar 123 is mounted on the drone swarm, and a three-dimensional model of the object is constructed by scanning the target in a surrounding and omnibearing manner and fusing data acquired by different laser radars according to a point cloud feature matching algorithm. Relative position relation between unmanned aerial vehicles is obtained through mutual scanning between laser radars to according to the coincidence condition of point cloud characteristic, adjust unmanned aerial vehicle position and gesture, accomplish scanning work with unmanned aerial vehicle as few as possible.
Preferably, the 5G communication module 124 transmits data scanned by the plurality of laser radars in different directions to the headquarters by using the characteristics of 5G communication, high bandwidth and low time delay.
In this embodiment, the GPS positioning module 125 can obtain the position information of the unmanned aerial vehicle, and improve the positioning accuracy of the unmanned aerial vehicle by cooperating with the inertial measurement unit.
In this embodiment, the method for real-time tracking and modeling of the formation targets of the unmanned aerial vehicles specifically includes the following steps:
s1: bionic formation control: in nature, biological groups can orderly coordinate the movement of the whole group through simple information interaction. Planning the route of the unmanned aerial vehicle, selecting the unmanned aerial vehicle station closest to the destination to fly the unmanned aerial vehicle, simulating wild goose migration flight, and setting the behavior mode of the unmanned aerial vehicle, if: how to assemble the unmanned aerial vehicles, what form of formation to arrange, how to avoid the mutual collision between barrier and the aircraft. Similar to the experienced wild gooses in band, the wild gooses in band make a decision on the whole goose group, and the unmanned machine head-forming machine senses the danger in front at first and adjusts the formation and the flying direction in advance.
S2: obstacle is kept away to intelligence: in the task execution process of the unmanned aerial vehicle, obstacle avoidance is the most basic requirement, particularly when the task is executed in an unknown environment, the unmanned aerial vehicle can only rely on the decision-making capability of the unmanned aerial vehicle, in the bionic formation control, the obstacle avoidance strategy is that the real-time three-dimensional reconstruction is carried out on the surrounding small-range environment by means of the carried three-dimensional laser radar, the obstacle identification is carried out on point cloud data, and the corresponding adjustment is carried out according to the size and distance of the obstacle and the self state of the unmanned aerial vehicle.
S3: "topology optimization" is a method of simulating and optimizing material distribution based on given load condition, constraint condition and performance index by means of topology optimization in mechanical structure design. The unmanned aerial vehicle cluster is arranged and optimized in the unmanned aerial vehicle target three-dimensional scanning process, a three-dimensional laser radar 123 scanning range is given, the distance between unmanned aerial vehicles is restricted, different laser radars scan the coincidence condition of target three-dimensional information, the unmanned aerial vehicles are reasonably arranged, space resources are reasonably distributed, and the number of unmanned aerial vehicles in the scanning work is reduced as much as possible.
S4: three-dimensional reconstruction: the method comprises the steps of calibrating point cloud data scanned by different laser radars, converting the point cloud data into the same coordinate system for facilitating subsequent point cloud splicing, identifying characteristic points and characteristic surfaces in point clouds, aligning time axes of the point cloud data of the different laser radars, matching and splicing point cloud characteristics, and achieving three-dimensional reconstruction.
S5: 5G communication: the digital cellular mobile 5G communication network has the characteristics of high speed, low time delay, high reliability and big data communication, the formation interconnection, real-time, clustering and intellectualization of the unmanned aerial vehicles are realized, the information collected by the unmanned aerial vehicle cluster is transmitted back to the master station through the 5G network by the cluster, and the master station makes decision deployment by means of the data.
In this embodiment, the quad-rotor flying robot 11 carries the GPS positioning module 125 and the inertia measurement unit 122 to realize preliminary positioning, and carries the three-dimensional laser radar 123 to realize obstacle recognition and obstacle avoidance, and sends a speed instruction to the electronic governor through a PID control algorithm to control the rotation direction and the rotation speed of the dc brushless motor 115, thereby controlling the flying direction and the flying attitude of the unmanned aerial vehicle. Through 5G communication module 124, form a formation with many unmanned aerial vehicles, simulate the experienced wide goose band method of wide goose migration in nature, select an unmanned aerial vehicle as the head machine, rely on its three-dimensional laser radar 123 to discern the barrier in advance and make the adjustment to the unmanned aerial vehicle formation, avoid "touching the reef" and avoid the mutual collision for keeping away same barrier. In the process of three-dimensional scanning of the target, the unmanned aerial vehicles are optimized in formation and arrangement by using a topology optimization method similar to that in mechanical structure design structure optimization, and the number of the unmanned aerial vehicles required by tasks is reduced. And carrying out feature matching and splicing on point cloud data scanned by the three-dimensional aurora radar 123 carried by a plurality of unmanned aerial vehicles, and realizing three-dimensional reconstruction of the target.
Referring to fig. 1-5, the specific principle steps of point cloud three-dimensional reconstruction are as follows:
(S1) the unmanned aerial vehicle receives the outbound task, sends a corresponding number of unmanned aerial vehicles according to the size of the predicted target, forms a formation by simulating the migration of wild gooses, realizes the positioning of the unmanned aerial vehicle through the GPS positioning module 125 and the inertia measuring unit 122, adjusts the flight attitude and speed of the unmanned aerial vehicle through a PID control algorithm, and quickly arrives at the scene.
(S2) surrounding the target after the unmanned aerial vehicle formation arrives at the site, scanning by a laser radar to obtain the initial size of the target, and imitating a topology optimization method in the mechanical structure design. The unmanned aerial vehicle cluster is arranged in the unmanned aerial vehicle target three-dimensional scanning process and optimized, the unmanned aerial vehicles are reasonably arranged, space resources are reasonably distributed, and the number of unmanned aerial vehicles in the scanning work is reduced as much as possible.
(S3) the obtained radar data, the inertial measurement unit 122 and the GPS positioning module 125 are used for calculating the self attitude of the unmanned aerial vehicle; solving a relative world coordinate system lower rotation torque matrix and a translation matrix according to the self attitude of the unmanned aerial vehicle; transferring the acquired point cloud data to a unified world coordinate system; dividing the point cloud to obtain sub-point cloud; and obtaining a characteristic sequence of the sub-point cloud. Matching characteristic sequences obtained by different laser radars according to the minimum Euclidean distance, aligning sequence point clouds in accordance with a threshold value, splicing the point clouds according to aligned data, and performing noise reduction treatment on a finished three-dimensional point cloud model to finish three-dimensional reconstruction.
(S4) returning to the airplane battle.
The above descriptions are only preferred embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the scope of the present invention, and the technical solutions and the utility model concepts of the present invention are equivalent to, replaced or changed.
Claims (5)
1. A real-time tracking and modeling system for targets formed by unmanned aerial vehicles in formation comprises a four-rotor flying robot (11) and a sensor module (12), and is characterized in that the four-rotor flying robot (11) comprises an unmanned aerial vehicle rack (111), carbon fiber supporting tubes (112), a flying pan-tilt (113), blades (114), a direct-current brushless motor (115) and a battery (116), the sensor module (12) and the battery (116) are arranged at the center of the top of the unmanned aerial vehicle rack (111), the sensor module (12) and the direct-current brushless motor (115) are connected with the battery (116), the carbon fiber supporting tubes (112) are multiple, the carbon fiber supporting tubes (112) are arranged at the bottom of the unmanned aerial vehicle rack (111), the blades (114) and the direct-current brushless motor (115) are four, the four blades (114) are connected with output shafts of the four direct-current brushless motors (115), four brushless DC motor (115) all set up the top in unmanned aerial vehicle frame (111), sensor module (12) are including airborne computer (121), inertia measuring unit (122), three-dimensional laser radar (123), 5G communication module (124) and GPS orientation module (125) all are connected with airborne computer (121), install on flying cloud platform (113) three-dimensional laser radar (123).
2. The system for real-time tracking and modeling of formation targets for unmanned aerial vehicles according to claim 1, wherein the quad-rotor flying robot (11) and the sensor module (12) form a formation of unmanned aerial vehicles.
3. The system for real-time tracking and modeling of formation targets of unmanned aerial vehicles according to claim 2, wherein the formation of unmanned aerial vehicles carries a three-dimensional lidar (123).
4. The system for real-time tracking and modeling of formation targets of unmanned aerial vehicles according to claim 2, wherein the unmanned aerial vehicle formation carries an inertial measurement unit (122), and the inertial measurement unit (122) comprises a three-axis gyroscope, a three-axis acceleration sensor, a magnetometer and a barometer.
5. The system for real-time tracking and modeling of formation targets of unmanned aerial vehicles according to claim 2, wherein the formation of unmanned aerial vehicles carries a GPS positioning module (125), and the GPS positioning module (125) and the inertial measurement unit (122) form an unmanned aerial vehicle positioning system.
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| CN115801123A (en) * | 2022-09-26 | 2023-03-14 | 中国科学院西安光学精密机械研究所 | Laser communication formation method and system based on unmanned aerial vehicle |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115801123A (en) * | 2022-09-26 | 2023-03-14 | 中国科学院西安光学精密机械研究所 | Laser communication formation method and system based on unmanned aerial vehicle |
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