CN114701648A - Biological control type unmanned aerial vehicle special for living body transportation - Google Patents
Biological control type unmanned aerial vehicle special for living body transportation Download PDFInfo
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- B64C—AEROPLANES; HELICOPTERS
- B64C27/00—Rotorcraft; Rotors peculiar thereto
- B64C27/04—Helicopters
- B64C27/08—Helicopters with two or more rotors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
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- B64C39/00—Aircraft not otherwise provided for
- B64C39/02—Aircraft not otherwise provided for characterised by special use
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64D—EQUIPMENT FOR FITTING IN OR TO AIRCRAFT; FLIGHT SUITS; PARACHUTES; ARRANGEMENT OR MOUNTING OF POWER PLANTS OR PROPULSION TRANSMISSIONS IN AIRCRAFT
- B64D1/00—Dropping, ejecting, releasing, or receiving articles, liquids, or the like, in flight
- B64D1/02—Dropping, ejecting, or releasing articles
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U10/00—Type of UAV
- B64U10/10—Rotorcrafts
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- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/08—Control of attitude, i.e. control of roll, pitch, or yaw
- G05D1/0808—Control of attitude, i.e. control of roll, pitch, or yaw specially adapted for aircraft
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U2101/00—UAVs specially adapted for particular uses or applications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B64—AIRCRAFT; AVIATION; COSMONAUTICS
- B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
- B64U2201/00—UAVs characterised by their flight controls
- B64U2201/10—UAVs characterised by their flight controls autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS]
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
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Abstract
The invention discloses a biological control type unmanned aerial vehicle special for living body transportation, which comprises a biological cabin, four rotary wings, four protective covers, a camera, a battery, a flight control board, an onboard computer and a main body connecting piece, wherein the rotary wings and the protective covers are fixedly arranged at the bottom of the main body connecting piece, the biological cabin and the battery are detachably arranged in the middle of the main body connecting piece, the camera and the onboard computer are fixedly arranged at two sides of the main body connecting piece, the flight control board is fixedly arranged at the top of the main body connecting piece, the rotary wings and the flight control board are configured to complete the flight function of the unmanned aerial vehicle, the camera and the onboard computer are configured to complete the path planning and obstacle avoidance functions, and the biological cabin is configured to complete the biological living body transportation and putting-in functions. The invention can carry out autonomous obstacle avoidance and path planning and a multi-unmanned aerial vehicle cooperative operation mode, and can autonomously finish the specific organism throwing work in a certain area after the area is defined.
Description
Technical Field
The invention relates to the technical field of biological control type unmanned aerial vehicles, in particular to a biological control type unmanned aerial vehicle special for living body transportation.
Background
In the research of the existing unmanned aerial vehicle, along with the rapid development of an electronic information technology and the maturity of civil unmanned aerial vehicle hardware, the function classification of the unmanned aerial vehicle is more and more refined, and the unmanned aerial vehicle for special purposes also enters a rapid development channel.
In the biological field, local pests and external invading pests have for a long time caused serious damage or threats to the forest ecosystem and the agricultural planting field in China. However, the traditional chemical pesticide is used for a long time without control, so that a great number of pests have obvious drug resistance and are killed and killed by a great number of natural enemy insects, so that the pests are rampant. Many chemical pesticides severely pollute water, atmosphere and soil and enter human body through food chain, seriously harming human health. The natural enemy insects as an effective natural regulation and control factor play an important role in the population inhibition of the pests, and can effectively avoid the defects, so the natural enemy insects have wide application and development prospects.
The traditional throwing mode is manual carrying and throwing, so that the traditional throwing mode is not flexible and rapid enough, and the cost is high. Emerging unmanned aerial vehicle is with its characteristics that can be high-efficient and the flight task of low-cost more, very is fit for carrying out the biological control task, but can bring unmanned aerial vehicle's motion dumb with the mode of current unmanned aerial vehicle additional installation biological beehive to and the success rate of biological input is not high, to drawbacks such as the biological control effect is not good in an area.
Therefore, those skilled in the art are dedicated to develop a dedicated bio-control type unmanned aerial vehicle for living body transportation, which can realize autonomous flight, living body transportation and fixed-point delivery.
Disclosure of Invention
In view of the above-mentioned drawbacks of the prior art, the technical problem to be solved by the present invention is how to perform a quantitative and fixed-point delivery of a certain type of natural enemy insects.
In order to achieve the above purpose, the present invention provides a biological control type unmanned aerial vehicle for dedicated living body transportation, including a biological cabin, four rotors, four protective covers, a camera, a battery, a flight control board, an onboard computer and a main body connecting piece, wherein the rotors and the protective covers are fixedly arranged at the bottom of the main body connecting piece, the biological cabin and the battery are detachably arranged in the middle of the main body connecting piece, the camera and the onboard computer are fixedly arranged at two sides of the main body connecting piece, the flight control board is fixedly arranged at the top of the main body connecting piece, the rotors and the flight control board are configured to complete the flight function of the unmanned aerial vehicle, the camera and the onboard computer are configured to complete the path planning and obstacle avoidance functions, and the biological cabin is configured to complete the biological living body transportation and throwing functions.
Further, the main body connecting piece is made of polyester resin through photo-curing 3D printing and integrated molding.
Further, the biological bin is in the shape of a long cylinder and comprises a side-opening release door, a patch type temperature control box and a humidity control box which are arranged on two sides.
Further, the flight control board is a Pixhawk 4 flight control board.
Further, the camera is an Intel RealSense D435i camera.
Further, the on-board computer is a NVIDIA Jetson Xavier NX development board.
Further, the protective cover acts as a foot rest for the drone.
Further, the camera and the onboard computer are also configured to collect image information of crops in the detection area through the camera, and gather the information to the onboard computer for pest and disease damage detection of image recognition.
Further, the image recognition adopts a MobileNet algorithm.
Further, the unmanned aerial vehicle is configured to adopt a group cooperation mode, and carry out communication integration, task arrangement and carrying supply on the unmanned aerial vehicle cluster through the autonomous bee cabin, and carry out the automatic refilling of the unmanned aerial vehicle battery and the biological cabin.
The invention has the beneficial effects that:
1. the system can carry out autonomous obstacle avoidance and path planning and a multi-unmanned aerial vehicle cooperative operation mode, and can autonomously finish the specific biological delivery work in a certain area after the area is defined.
2. Compare in large-scale unmanned aerial vehicle, the position and the quantity that the mode of unmanned aerial vehicle cooperation operation can control the input are more accurate, improve the survival rate of input biology greatly to can accurate control input biology in the distribution density in this area.
3. With its small and exquisite volume and the function of independently keeping away the barrier, should put in the device and can carry out narrow and small position operation, or through figure recognition, carry out the accurate input to certain specific plant or environment.
4. Through information sharing of multiple unmanned aerial vehicles, a more deep investigation can be carried out on the launched area, and a launching strategy is corrected in real time; meanwhile, the directional delivery can be carried out aiming at the hypothetical target with a certain characteristic, and the actual biological condition of the area is analyzed.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
Fig. 1 is a schematic structural diagram of a bio-delivery drone according to a preferred embodiment of the invention;
fig. 2 is a top view of a bio-delivery drone according to a preferred embodiment of the present invention;
fig. 3 is an exploded view of a bio-delivery drone according to a preferred embodiment of the invention;
fig. 4 is a schematic structural diagram of a main body connecting member of the bio-delivery unmanned aerial vehicle according to a preferred embodiment of the invention;
fig. 5 is a schematic component placement diagram of a bio-delivery drone according to a preferred embodiment of the invention;
fig. 6 is a schematic structural diagram of a bio-cabin of the bio-delivery drone according to a preferred embodiment of the invention;
fig. 7 is a dynamic model of a drone under cross-wind interference according to a preferred embodiment of the invention;
FIG. 8 is a functional block diagram of a fuzzy controller in accordance with a preferred embodiment of the present invention;
fig. 9 illustrates an obstacle avoidance strategy for a drone according to a preferred embodiment of the present invention;
FIG. 10 is a schematic diagram of the MobileNet algorithm of the image recognition process in accordance with a preferred embodiment of the present invention;
FIG. 11 is a flow chart of an image recognition process in accordance with a preferred embodiment of the present invention;
fig. 12 is a schematic structural view of a drone bee space of a preferred embodiment of the present invention;
fig. 13 is a front view of a drone bee space of a preferred embodiment of the present invention;
figure 14 is a schematic diagram of a complete mission link of a drone in accordance with a preferred embodiment of the present invention;
FIG. 15a is predation functional response of adult arma bug in accordance with a preferred embodiment of the present invention to Spodoptera frugiperda 3-instar larvae;
FIG. 15b is a predation functional response of adult arma bug in accordance with a preferred embodiment of the present invention to Spodoptera frugiperda 4-instar larvae;
FIG. 15c is a predation functional response of adult arma bug in accordance with a preferred embodiment of the present invention to Spodoptera frugiperda 5 th instar larvae.
10-biologically throwing unmanned aerial vehicle, 11-biologically cabin, 12-rotor wing, 13-protective cover, 14-camera, 15-battery, 16-flight control board, 17-onboard computer, 18-main body connecting piece, 111-release door, 112-patch type temperature control, and 113-humidity control box.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings for clarity and understanding of technical contents. The present invention may be embodied in many different forms of embodiments and the scope of the invention is not limited to the embodiments set forth herein.
In the drawings, structurally identical elements are represented by like reference numerals, and structurally or functionally similar elements are represented by like reference numerals throughout the several views. The size and thickness of each component shown in the drawings are arbitrarily illustrated, and the present invention is not limited to the size and thickness of each component. The thickness of the components may be exaggerated where appropriate in the figures to improve clarity.
The flight mode of a large unmanned aerial vehicle is not flexible enough, and the flight mode is not as convenient and quick as that of a small special unmanned aerial vehicle when a small-area task is executed. What unmanned aerial vehicle of this application was walked is succinct light weight's route, and the working method is accurate delivery, plus many unmanned aerial vehicle cluster cooperation modes.
What the unmanned aerial vehicle of this application specifically realized is the biological control function of live body transportation to be the accurate delivery that large-scale unmanned aerial vehicle can not be competent, consequently the objective environment that faces will be more complicated changeable, the destination probably contains environment uncertain all such as brook, plateau, jungle humidity, wind speed, atmospheric pressure, need accomplish stability as far as possible and put in taking into account of survival rate, consider the cost control of unmanned aerial vehicle accident loss under the objective environment even.
Unmanned aerial vehicle overall structure is very compact, for the four rotor unmanned aerial vehicle of standard, and the wheel base is 70 x 70 millimeters, chooses for use 2 cun small motor, and unmanned aerial vehicle overall dimension is 130 x 90 millimeters, and the empty aircraft quality of taking off is about 160 grams.
The design aims at the fact that the operation can be carried out on various special terrains, not only on a vast plain, but also by combining the path planning and the autonomous obstacle avoidance function, and even the autonomous flight can be carried out in a jungle. The ultra-small size and ultra-low weight design also allows for greater stability in the face of complex aerodynamic environments, increasing mission success rates. Meanwhile, the survival rate of the living targets carried by the small rotor wings can be improved under the objective conditions of vibration, noise and the like.
As shown in fig. 1 to 3, the bio-throwing unmanned aerial vehicle 10 is mainly composed of a bio-bin 11, a rotor 12, a protective cover 13, a camera 14, a battery 15, a flight control panel 16, an onboard computer 17, and a main body connector 18. The rotor wing 12 and the flight control panel 16 complete the flight function of the unmanned aerial vehicle and belong to a power module of the unmanned aerial vehicle; the camera 14 and the onboard computer 17 complete path planning and obstacle avoidance functions and belong to autonomous flight modules; the biological bin 11 is a task execution module; finally, the battery 15 and the protective cover 13 belong to a flight assurance module.
The specific structural components are introduced as follows:
1. unmanned aerial vehicle main part:
main part connecting piece 18 uses photocuring 3D to print integrated into one piece preparation as shown in fig. 4 and 5, and the raw materials is polyester resin, and structural strength and quality can both be guaranteed, and whole quality only is 18 grams, improves unmanned aerial vehicle flight time greatly.
Unmanned aerial vehicle adopts and puts the rotor design down, has three big benefits like this: firstly, the lower rotor does not occupy the upper space of the unmanned aerial vehicle, so that the unmanned aerial vehicle is more convenient to debug and disassemble; secondly, the air flow generated by the rotor wing is disturbed downwards, so that the influence on the upper biological bin is reduced, and the habitual characteristic that insects take off obliquely upwards is attached, so that the success rate of biological release is higher; and finally, the protective cover of the rotor wing of the unmanned aerial vehicle directly acts as a foot rest, so that the takeoff quality is further reduced.
Finally, the battery 15 and the biological bin 11 are placed in the middle of the unmanned aerial vehicle, the main body connecting piece 18 is used for protecting the unmanned aerial vehicle, and meanwhile, a lateral quick-release structure is formed, so that the continuous operation time of the unmanned aerial vehicle is prolonged, and the effect of continuous operation of the unmanned aerial vehicle swarm is achieved.
2. A biological bin:
as shown in fig. 6, the bio-silo 11 is in the shape of a long cylinder, and the volume is increased as much as possible when the size of the unmanned aerial vehicle is fitted. The releasing adopts a side-open type releasing door 111 arranged on two sides, and is matched with the habit characteristic of oblique insect taking off, so that the taking-off channel of the insects is not interfered; the patch type temperature control 112 can simultaneously play a role in heating and cooling, so that the temperature in the biological bin 11 is always at the appropriate temperature for the target insects; the humidity control box 113 may also function as a humidifier. The biological bin 11 can make the insects in the bin in a more active state or a more energy-saving hibernation state through temperature and humidity adjustment according to different biological characteristics of the released insects and different target environments.
3. Selection of a control system:
firstly, a flight control system selects a Pixhawk 4 flight control panel 16 of a general PX4 system, the universality and the stability of the series of flight control panels 16 are detected by the market, the volume is proper, and the flight control system can be compatible with an onboard computer 17 simply. This series of flight control panel 16 uses STM32F765 chip, and the dominant frequency is higher, and the memory is bigger, and the arithmetic capability is higher, and the delay is littleer, can exert unmanned aerial vehicle performance best.
The performance pair ratios of the Pixhawk control system are shown in table 1.
TABLE 1 comparison of several Pixhawk control System Performance
Next, the camera 14 is selected, the Intel real sense D435i camera 14 is selected in the present application, and is a receptor for the external environment in autonomous flight of the unmanned aerial vehicle, and the D435i camera 14 is a currently excellent binocular camera, which is a stereo tracking solution and can provide high quality depth for various applications. The wide field of view is very suitable for applications such as robots or augmented reality and virtual reality, and in the applications, it is important to expand the scene view angle as much as possible. The shooting range of the camera 14 with small appearance is up to 10 meters, the camera can be easily integrated into a solution, the configuration is complete, and the Intel realistic SDK 2.0 technology is adopted, so that the unmanned aerial vehicle is the most ideal choice.
The on-board computer 17 selects an NVIDIA Jetson Xavier NX development board, which is a powerful Artificial Intelligence (AI) development board, and can run a plurality of modern neural networks in parallel and process high-resolution data from a plurality of sensors simultaneously by virtue of 384 Computing Unified Device Architecture (CUDA) cores, 48 Tensor cores and 2 NVIDIA Deep Learning Accelerator (NVIDIA Deep Learning Accelerator, NVDLA) engines, so that the requirement of a complete AI system is met, and all universal AI frameworks are supported. After the Ubuntu system is mounted, image recognition and settlement can be directly carried out on the airplane, and autonomous flight tasks such as obstacle avoidance and path planning can be completed.
The software control design is introduced as follows:
1. control of quad-rotor unmanned aerial vehicle
The existing four-rotor control scheme has a mature control scheme, and attention needs to be paid to trimming after carrying related equipment and design of a stabilizing controller of the unmanned aerial vehicle under the condition of environmental disturbance.
Firstly, modeling analysis is carried out on a four-rotor unmanned aerial vehicle power model.
The dynamic model of the drone under crosswind disturbance is shown in fig. 7.
The following formulas (1) and (2) are respectively a six-degree-of-freedom motion equation and a rotor wing output total force equation of the unmanned aerial vehicle.
Fz＝Ft+Fw＝2ρAVdVt (2)
Three of the six-degree-of-freedom motion equations (1) are defined by three coordinate axes of an unmanned aerial vehicle body coordinate system extending from the gravity center of the unmanned aerial vehicle, and formed pitch angles
The roll angle theta and the yaw angle psi have second derivatives, namely the rotation angular acceleration of the unmanned aerial vehicle along three axes according to Newton's second theorem, and are equal to the resultant force of all external rotation moments of the unmanned aerial vehicle; in the next three same principles, the second derivative of the unmanned aerial vehicle along the xyz three axes, that is, the acceleration of the unmanned aerial vehicle along the three directions, is the sum of external forces of the unmanned aerial vehicle along the three directions. (wherein the detailed decomposition of the external force and the external moment needs to be defined according to the model of the unmanned aerial vehicle, and the calculation of the external moment needs to consider the calculation of precession moments in different directions)
Rotor output total force equation (2) need combine 7 unmanned aerial vehicle in the dynamic model under crosswind interference of fig. 7, and lift that rotor output produced mainly determines for unmanned aerial vehicle rotor effective area and air incoming flow. When the rotor wing of the unmanned aerial vehicle is interfered by external crosswind, the incoming air flow is no longer VdV caused by external cross wind is also consideredtTherefore, the output force of the rotor under the crosswind condition is the sum of the two. (wherein ρ is the air density)A is the effective rotor area, VdThe incoming flow velocity of air, V, generated by the rotation of the lower rotor in a static statewIs the ambient crosswind velocity, VtThe two are synthesized into the air incoming flow speed and omega is the rotor wing speed)
For the model with the interference, the nonlinear controllers such as fuzzy control and sliding mode control can be selected, and the nonlinear controllers have better robustness and high adaptability to the environment, so that the stability of the unmanned aerial vehicle can be better improved.
A schematic block diagram of the fuzzy controller is shown in fig. 8.
The basic working principle of the second-order fuzzy controller is shown in the flow chart. Firstly, a task decides a current control quantity, subtracts an output quantity of a controlled object in the current actual state, the difference of the two control quantities is a control quantity e required at present, a second-order fuzzy controller reads a first derivative of the control quantity and the control quantity, after fuzzification, a definition is carried out on the control quantity required at present according to a fuzzy rule formulated in advance, a logic rule module formulated in advance infers the required control quantity according to the definition after fuzzification, three control quantities of proportion, differentiation and integration required by PID control on an airplane can be output after defuzzification, and the three control quantities are output to the controlled object through an execution mechanism. The fuzzy control can achieve a good control effect without performing very detailed calculation and modeling according to the possible situations set in advance, has high robustness and accords with the control logic of human beings.
2. Regarding the autonomous flight function, based on the NX development board carried on the unmanned aerial vehicle and the image acquisition function of the binocular camera, the unmanned aerial vehicle can achieve the functions of image recognition of obstacles, autonomous planning of flight paths and final arrival at target points.
Firstly, setting the farthest distance of an optical binocular camera, and initializing the maximum obstacle avoidance angle of the aircraft when an obstacle is detected; judging whether an obstacle is detected, if the obstacle is not detected, directly sending an expected angle converted by a remote control command into an attitude controller, and regulating and controlling the aircraft; if the obstacle is detected, the obstacle in the direction is continuously judged, so that the distance of the corresponding direction obstacle can be converted into the deflection percentage within 0-1, the deflection angle of the aircraft in the corresponding direction is obtained, the obtained deflection angle and the expected angle are summed, and finally the rolling angle and the course angle deviation angle required by the attitude controller are obtained.
The obstacle avoidance strategy of the unmanned aerial vehicle is shown in fig. 9.
This is a relatively simple planar obstacle avoidance strategy, which mainly consists of two cycles: and if the major cycle of the obstacle is monitored, selecting a minor cycle by an obstacle avoidance strategy. When an obstacle is encountered in a traveling path, the system stops the pitch axis (pitch axis) to return to zero according to the current flying state of the unmanned aerial vehicle when the traveling speed is high, then performs translational motion perpendicular to the traveling direction while monitoring, the translational motion is synthesized by small-angle rotation of the roll axis (roll axis) until the obstacle cannot be monitored, and then performs the flying according to the predetermined path again. The obstacle avoidance strategy of the unmanned aerial vehicle is nested in the monitoring cycle, the plane of the unmanned aerial vehicle is avoided by controlling the level, height and posture of the unmanned aerial vehicle, and the direction of the unmanned aerial vehicle is not changed in the translation process, so that the tracking flight after the obstacle avoidance is finished is smoother.
3. Image-recognized pest inspection
When unmanned aerial vehicle carries out the task of puting in the demarcation region, also can open the image contrast recognition task in this region simultaneously, according to the function of the camera of carrying, the sample that the inspection possesses specific characteristics feeds back the biological characteristics in this region.
Firstly, collecting image information of crops in a detection area through a camera, and converging the information to an onboard computer to finish detection; in the crop detection module, the MobileNet algorithm is used, which is a lightweight convolutional neural network that divides the standard convolution into two parts, depth convolution and point-by-point convolution, as shown in fig. 10. The method also divides the combination calculation and the filtering into two steps, wherein in the first step, a filter is used for carrying out convolution on each input channel, and then point-by-point convolution is used for carrying out convolution kernel operation on the result calculated by the deep convolution to obtain an output result. The method can effectively reduce the calculated amount and the model parameters, and finally feeds back the calculated amount and the model parameters to the terminal network.
The flow of the image recognition process is shown in fig. 11.
As described above, the type unmanned aerial vehicle is put in to small-size living beings of this application to its small and exquisite size and the weight of taking off, carried many meshes camera and airborne computer can bring a plurality of benefits in addition:
1. the system can carry out autonomous obstacle avoidance and path planning and a multi-unmanned aerial vehicle cooperative operation mode, and can autonomously finish the specific biological delivery work in a certain area after the area is defined.
2. Compare in large-scale unmanned aerial vehicle, the position and the quantity that the mode of unmanned aerial vehicle cooperation operation can control the input are more accurate, improve the survival rate of input biology greatly to can accurate control input biology in the distribution density in this area.
3. With its small and exquisite volume and the function of independently keeping away the barrier, should put in the device and can carry out narrow and small position operation, or through figure recognition, carry out the accurate input to a certain specific plant or environment.
4. Through information sharing of multiple unmanned aerial vehicles, a more deep investigation can be carried out on the launched area, and a launching strategy is corrected in real time; meanwhile, the directional delivery can be carried out aiming at the hypothetical target with a certain characteristic, and the actual biological condition of the area is analyzed.
Above four points, embodied the intellectuality of the small-size biological input type unmanned aerial vehicle of this application, the advantage of accurate input, efficiency and success rate of biological control in an area can be improved greatly to these advantages.
Firstly, the unmanned aerial vehicle adopts a group cooperation mode and has an autonomous bee cabin, and as shown in fig. 12 and 13, the unmanned aerial vehicle can carry out tasks such as communication integration and task arrangement on an unmanned aerial vehicle cluster; and carry on and supply, can carry out the autonomic filling again of unmanned aerial vehicle battery and biological storehouse, realize going on of long-time lasting task.
The drone full mission link is shown in fig. 14.
Finally, the actual simulation was performed according to one case of existing biological control.
The Spodoptera frugiperda is wide in suitable living area, strong in migratory flight capability and high in propagation multiple, and after the Spodoptera frugiperda is confirmed to be introduced into Jiangcheng counties of Puer city in Yunnan from 2019 and 11 months, 124 counties and counties in 16 counties and cities in Yunnan are harmful and spread to 19 provinces and counties in China. Arma chinensis belongs to hemiptera and is widely distributed in China. The nymphs and adults of the insect can prey on various pests such as lepidoptera, coleoptera, diptera and the like, particularly have stronger prey capability on the lepidoptera pests, and are important predatory natural enemies.
Predation function responses of adult arma chinensis to 3, 4 and 5 th larvae of spodoptera frugiperda are shown in fig. 15a, 15b and 15 c.
The volume of the biological warehouse carried by the unmanned aerial vehicle is about 20 cubic centimeters, 15-25 adult arma bugs can be carried at one time, the quantity of spodoptera exigua in 100 multiplied by 100 meters can be controlled by one-time putting amount according to density measurement, the supply time and the information acquisition time are calculated, and one-time task is about 20 minutes. Under the condition of the supply of the unmanned plane bee cabin and the cooperative work of a plurality of unmanned planes, a set of 4 unmanned plane clusters can automatically finish the release of arma bug adults within ten kilometers of the diameter in about 40 hours. And under the information acquisition and image analysis's of unmanned aerial vehicle carrying with holding, can carry out real-time condition judgement and unmanned aerial vehicle route adjustment.
Use the native type natural enemy insect can effectively prevent and treat the spodoptera littoralis, use the small-size biological transportation type unmanned aerial vehicle of this embodiment to carry out accurate input, can not only effectively and reduce the flooding of foreign species fast, can not seriously destroy local ecological balance again.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.
Claims (10)
1. The utility model provides a biological prevention and cure type unmanned aerial vehicle of special live body transportation, its characterized in that, includes biological storehouse, rotor, safety cover, camera, battery, flies the control panel, airborne computer and main part connecting piece, rotor and safety cover quantity are four, and fixed setting is in the bottom of main part connecting piece, biological storehouse and battery can be dismantled the setting and be in the centre of main part connecting piece, camera and airborne computer are fixed to be set up the both sides of main part connecting piece, it is in to fly the fixed setting of control panel the top of main part connecting piece, rotor and airborne control panel are configured to accomplish unmanned aerial vehicle's flight function, camera and airborne computer are configured to accomplish the route planning and keep away the barrier function, biological storehouse is configured to accomplish biological live body transportation and puts in the function.
2. The dedicated living body transportation biocontrol unmanned aerial vehicle of claim 1, wherein the body connector is integrally formed by photo-curing 3D printing using polyester resin.
3. The special bio-control unmanned aerial vehicle for living body transportation according to claim 1, wherein the bio-cabin has a shape of a long cylinder and comprises a bilateral-arranged side-open release door, a patch-type temperature control and humidity control box.
4. The biocontrol drone of live transport of claim 3, wherein the flight control panel is a Pixhawk 4 flight control panel.
5. The dedicated live-action biocontrol drone of claim 1, wherein said camera is an Intel RealSense D435i camera.
6. The dedicated live-action biocontrol drone of claim 1, wherein said onboard computer is a NVIDIA Jetson Xavier NX exploitation panel.
7. The dedicated live action biocontrol drone of claim 1, wherein said protective cover acts as a foot stand for said drone.
8. The dedicated live-action biocontrol drone of claim 1, wherein said camera and onboard computer are further configured to collect image information of the crop in the detection area through said camera and to converge the information to said onboard computer for image-recognized pest detection.
9. The special purpose in-vivo transit biocontrol unmanned aerial vehicle of claim 8, wherein said image recognition employs a MobileNet algorithm.
10. The dedicated live-action biocontrol drone of claim 1, wherein the drone is configured to employ a group cooperation mode and to perform communication integration, mission placement, and piggyback replenishment of the drone battery and bio-pods through the autonomous bee-pods for re-autonomous filling of the drone battery and bio-pods.
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