CN114137473A - Unmanned aerial vehicle positioning method capable of covering signal of agricultural and forestry robot - Google Patents

Unmanned aerial vehicle positioning method capable of covering signal of agricultural and forestry robot Download PDF

Info

Publication number
CN114137473A
CN114137473A CN202111605836.0A CN202111605836A CN114137473A CN 114137473 A CN114137473 A CN 114137473A CN 202111605836 A CN202111605836 A CN 202111605836A CN 114137473 A CN114137473 A CN 114137473A
Authority
CN
China
Prior art keywords
aerial vehicle
unmanned aerial
forestry
agricultural
robot
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202111605836.0A
Other languages
Chinese (zh)
Inventor
庞丽萍
张明堃
王爽
王金鹤
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Ningbo Kaide Technology Service Co ltd
Original Assignee
Ningbo Kaide Technology Service Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Ningbo Kaide Technology Service Co ltd filed Critical Ningbo Kaide Technology Service Co ltd
Priority to CN202111605836.0A priority Critical patent/CN114137473A/en
Publication of CN114137473A publication Critical patent/CN114137473A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S1/00—Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith
    • G01S1/02—Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith using radio waves
    • G01S1/08—Systems for determining direction or position line
    • 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
    • Y02T10/10—Internal combustion engine [ICE] based vehicles
    • Y02T10/40—Engine management systems

Abstract

The invention relates to an unmanned aerial vehicle positioning method capable of covering robot signals based on an optimization technology, which provides the concept of an agricultural and forestry robot and a demand market thereof, and aims at the short board of the agricultural and forestry robot in the aspects of signal sources and positioning functions. The model is decomposed into a multi-constraint simple semi-definite programming problem model and a sequence maximum element problem model through the structure of the multi-constraint complex semi-definite programming model. And solving the problem of the maximum elements of the sequence by using a cyclic strategy, and designing an efficient random algorithm for solving the multi-constraint semi-specification. The method is used for solving the problems of signal loss of coverage robot signals and the like in positioning of the unmanned aerial vehicle in agricultural and forestry production.

Description

Unmanned aerial vehicle positioning method capable of covering signal of agricultural and forestry robot
Technical Field
The invention belongs to the technical field of information processing, and relates to an unmanned aerial vehicle positioning method capable of covering signals of an agricultural and forestry robot based on an optimization technology.
Background
In the production activities of agriculture and forestry, a robot capable of unmanned automatic operation is required to be used, and the robot is called an agriculture and forestry robot. In terms of communication aspect, the agricultural and forestry operation area is large, the range is wide, and a signal coverage network is not easy to establish. In addition, the height of agricultural and forestry crops compared with the height of farmland crops is obviously not neglected, and is one of the important reasons for influencing signal coverage and signal communication.
The robot in the agriculture and forestry scope is narrower than the field of vision of farmland because the forestry landscape, therefore the agriculture and forestry robot is less than the farmland robot, and the required quantity of agriculture and forestry robot still is relevant with forest growth cycle and planting area moreover. Moreover, due to the fact that the height of the agricultural and forestry crops is large, the agricultural and forestry crops have signal shielding performance, and the position monitoring of the agricultural and forestry robots brings non-negligible errors. Thus requiring a supplemental signal for accurate monitoring.
The invention utilizes the airborne base station of the unmanned aerial vehicle to supplement signals, aims to accurately supplement signals covering the agricultural robot, utilizes the unmanned aerial vehicle to position the agricultural and forestry robot, and solves the problem of signal coverage based on an optimization technology.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention designs an unmanned aerial vehicle positioning method capable of covering signals of an agricultural and forestry robot based on an optimization technology. The problem of sequence maximum elements is solved by using a classical loop strategy, and an efficient random algorithm for solving multi-constraint semi-definite specification is designed. The technology is used for solving the problems of signal loss and robot anti-collision and the like faced by unmanned aerial vehicle positioning in agriculture and forestry production.
The agricultural and forestry robot of the invention refers to: the machine is controlled by different program software, can be adapted to various operations, can sense and adapt to the variety and environmental change of crops such as agriculture and forestry, and can be operated automatically without people. The robot has wide market prospect in various agriculture and forestry operation fields such as fruit tree picking, pesticide spraying and the like.
The robot in the agriculture and forestry scope is more narrow than the farmland field of vision because the forestry landscape, therefore agriculture and forestry robot compares the volume less with the farmland robot, because the agriculture and forestry crop height is big, has the nature of sheltering from to the signal, needs the supplementary signal to the accuracy is monitored.
The technical scheme of the invention is as follows: an unmanned aerial vehicle positioning method capable of covering signals of agricultural and forestry robots based on an optimization technology is characterized in that hardware depended by the method comprises an unmanned aerial vehicle and a plurality of agricultural and forestry robots M, an airborne base station is arranged on the unmanned aerial vehicle, the agricultural and forestry robots M are supplemented with signals by the airborne base station, a distance measurement unit used in the method is meter, a time unit is minute, and an unmanned aerial vehicle positioning optimization model capable of covering signals of the agricultural and forestry robots is established as follows:
the movement range of the ith agriculture and forestry robot Mi is defined as an elliptical area, and is marked as EiI is a positive integer having a major axis of
Figure BDA0003434008490000011
Short axis of
Figure BDA0003434008490000012
With EiThe position center of (1) is the origin of coordinates, the north-south direction is the longitudinal axis, and t is used2In which the east-west direction is the horizontal axis and is denoted by t1Is represented byiThe included angle between the major axis and the positive half axis of the transverse axis is theta i, EiIs a rotational transformation matrix of
Figure BDA0003434008490000013
EiThe equation of (a) is:
Figure BDA0003434008490000014
Eiequation data matrix of
Figure BDA0003434008490000021
Figure BDA0003434008490000022
Figure BDA00034340084900000218
Representation matrix AiIs positively defined and is AiContrary to (2)A matrix, i ═ 1, …, m, m being a positive integer;
translating the origin of the coordinate system to ═ i (η i)1,ηi2)TEta i is EiIn the plane position coordinates of the GPS position, a central coordinate point of the ellipse, bi＝Aiηi,ci＝ηiTAiEta i-1, constructing a maximum eigenvalue of lambda1iAnd the second characteristic value is λ2iOf (2) matrix QiSaid matrix
Figure BDA0003434008490000023
Figure BDA0003434008490000024
Is that
Figure BDA0003434008490000025
Reciprocal of (2), QiCorresponding to λ1iAnd λ2iRespectively is u1iAnd u2iThen EiIs parallel to the minor axis of u1iThe long axis is parallel to u2i，EiRe-using a matrix as:
Figure BDA0003434008490000026
i is 1, …, m, x represents
Figure BDA0003434008490000027
The point (b) in (c) is,
Figure BDA0003434008490000028
is a two-dimensional real number space, EiIs centered at
Figure BDA0003434008490000029
The signal coverage of the airborne base station of the drone is circular B,
Figure BDA00034340084900000210
wherein β ═ β (β)1，β2) A coordinate point in a plane position coordinate representing a GPS position of the drone, gamma being a real number, a coverage radius of a signal of the drone before takeoff being
Figure BDA00034340084900000211
The height data of the tree is set as H ═ H1,…,hnN represents the number of trees, and when the height of the unmanned aerial vehicle from the ground is rho, the rho is required to meet the condition that the rho is more than or equal to 1+ hiAnd i is 1, … n, according to the pythagorean theorem, the radiation radius of the signal of the unmanned aerial vehicle in the air is
Figure BDA00034340084900000212
Due to the fact that
Figure BDA00034340084900000213
So that there is a positive integer τiSo that the following holds:
Figure BDA00034340084900000214
the unmanned aerial vehicle positioning optimization model is as follows:
Figure BDA00034340084900000215
because the optimization of the variable rho is irrelevant to the variables beta, gamma and tau, the unmanned aerial vehicle positioning optimization model is decomposed into two irrelevant simple optimization sub-problems:
multi-constraint simple semi-definite planning problem:
Figure BDA00034340084900000216
Figure BDA00034340084900000217
and the sequence maximum meta problem:
ρ＝1+maxj＝1,…,nhj。 (3)
the solution method of the unmanned aerial vehicle positioning optimization model comprises the following steps:
the first step is to solve the multi-constraint simple semi-definite programming problem:
step1.1 initialization
Setting unmanned aerial vehicle starting coordinate beta0And an overlay parameter gamma0，τ0Setting step size { a }kWith a sequence of penalty parameters [ sigma ]k}，ak、σkIs a real number;
step1.2 iteration
The iteration index k is 0, …, N-1, N is a positive integer, and a positive integer i is randomly selected from 1, … mk，
Computing matrices
Figure BDA0003434008490000031
Maximum eigenvalue λ ofmaxAnd a maximum eigenvalue λmaxCorresponding feature vector
Figure BDA0003434008490000032
If λmax>0。
βk+1＝βk-2ak(βk-σkuβ)
γk+1＝γk-2ak(-1-σk)
Figure BDA0003434008490000033
Otherwise
βk+1＝βk-2ak(βk)
γk+1＝γk-2ak(-1)
Step1.3: output ((beta)N)T,γN,(τN)T)TTo obtain the coordinate betaN；
Second step of
Solving the sequence maximum element rho as 1+ max by using a loop strategyj＝1,…,nhj；
Step2.1：ρ＝0，j＝1；
Step2.2: if rho is less than or equal to hj，ρ＝hj，j＝j+1；
Step2.3: if j < N, turning to Step2.2;
step2.4, outputting rho, and finally obtaining the coordinate beta of the agricultural and forestry unmanned aerial vehicleNRadius of radiation of
Figure BDA0003434008490000034
Step2.5 end.
The attached drawings of the specification:
fig. 1 shows a top view of agricultural and forestry operations, showing a signal transmission path of a robot.
Fig. 2 shows a drone altitude diagram, showing drone altitude limits.
FIG. 3 shows a signal coverage effect diagram of the agricultural and forestry robot in a fixed direction.
FIG. 4 shows a signal coverage effect diagram of the agricultural and forestry robot with variable direction.
Agricultural and forestry robot of fixed direction: eiThe included angle between the major axis of the shaft (theta) and the positive half axis of the horizontal axis of the coordinate system is constant and invariable.
Variable direction agriculture and forestry robot: eiThe angle theta i between the major axis of (a) and the positive half axis of the horizontal axis of the coordinate system is changed.
The invention has the beneficial effects
1. The invention combines the characteristics of agricultural production and forestry production and provides the concept of the agricultural and forestry robot and the market for the agricultural and forestry robot. Utilize agriculture and forestry robot to compare with the farmland robot, characteristics such as the volume is less, agriculture and forestry crop height is big, have the nature of sheltering from to the signal, propose the strategy of utilizing unmanned aerial vehicle machine to carry basic station supplementary signal, be favorable to the accuracy to carry out information monitoring, guarantee that unmanned aerial vehicle and robot can not bump with agriculture and forestry crop.
2. The multi-constraint complex semi-definite planning model is established by combining the information of the elliptical coverage of the horizontal range and the longitudinal height of the unmanned aerial vehicle, the model can be decomposed into simple subproblems by utilizing the structure, a guarantee is provided for designing a high-efficiency and rapid algorithm, the performance index required by the unmanned aerial vehicle is reduced, and the cost for manufacturing the unmanned aerial vehicle can be saved.
3. A random algorithm for solving semi-definite programming and a cyclic strategy for solving the maximum elements of the sequence are designed, so that the problems of large number of agricultural and forestry machines and non-static operation are solved, and the instantaneity of positioning is ensured.
Detailed Description
Referring to fig. 1 to 4, in the method for positioning an unmanned aerial vehicle capable of covering signals of an agricultural and forestry robot based on an optimization technology, hardware relied on in the method includes an unmanned aerial vehicle and a plurality of agricultural and forestry robots M, the unmanned aerial vehicle is provided with an onboard base station, the onboard base station is used for supplementing signals to the agricultural and forestry robots M, a distance measurement unit used in the method is meter, a time unit is minute, and an unmanned aerial vehicle positioning optimization model capable of covering signals of the agricultural and forestry robots is established as follows:
the movement range of the ith agriculture and forestry robot Mi is defined as an elliptical area, and is marked as EiI is a positive integer having a major axis of
Figure BDA0003434008490000041
Short axis of
Figure BDA0003434008490000042
With EiThe position center of (1) is the origin of coordinates, the north-south direction is the longitudinal axis, and t is used2In which the east-west direction is the horizontal axis and is denoted by t1Is represented byiThe included angle between the major axis and the positive half axis of the transverse axis is theta i, EiIs a rotational transformation matrix of
Figure BDA0003434008490000043
EiThe equation of (a) is:
Figure BDA0003434008490000044
Eiequation data matrix of
Figure BDA0003434008490000045
Figure BDA0003434008490000046
Figure BDA0003434008490000047
Representation matrix AiIs positively defined and is Ai1, …, m, m being a positive integer;
translating the origin of the coordinate system to ═ i (η i)1,ηi2)TEta i is EiIn the plane position coordinates of the GPS position, a central coordinate point of the ellipse, bi＝Aiηi,ci＝ηiTAiEta i-1, constructing a maximum eigenvalue of lambda1iAnd the second characteristic value is λ2iOf (2) matrix QiSaid matrix
Figure BDA0003434008490000048
Figure BDA0003434008490000049
Is that
Figure BDA00034340084900000410
Reciprocal of (2), QiCorresponding to λ1iAnd λ2iRespectively is u1iAnd u2iThen EiIs parallel to the minor axis of u1iThe long axis is parallel to u2i，EiRe-using a matrix as:
Figure BDA00034340084900000411
i is 1, …, m, x represents
Figure BDA00034340084900000412
The point (b) in (c) is,
Figure BDA00034340084900000413
is a two-dimensional real number space, EiIs centered at
Figure BDA00034340084900000414
The signal coverage of the airborne base station of the drone is circular B,
Figure BDA00034340084900000415
wherein β ═ β (β)1，β2) A coordinate point in a plane position coordinate representing a GPS position of the drone, gamma being a real number, a coverage radius of a signal of the drone before takeoff being
Figure BDA00034340084900000416
The height data of the tree is set as H ═ H1,…,hnN represents the number of trees, and when the height of the unmanned aerial vehicle from the ground is rho, the rho is required to meet the condition that rho is more than or equal to 1+ hiAnd i is 1, … n, according to the pythagorean theorem, the radiation radius of the signal of the unmanned aerial vehicle in the air is
Figure BDA00034340084900000417
Due to the fact that
Figure BDA00034340084900000418
So that there is a positive integer τiSo that the following holds:
Figure BDA00034340084900000419
the unmanned aerial vehicle positioning optimization model is as follows:
Figure BDA00034340084900000420
because the optimization of the variable rho is irrelevant to the variables beta, gamma and tau, the unmanned aerial vehicle positioning optimization model is decomposed into two irrelevant simple optimization sub-problems:
multi-constraint simple semi-definite planning problem:
Figure BDA00034340084900000421
Figure BDA00034340084900000422
and the sequence maximum meta problem:
ρ＝1+maxj＝1,…,nhj。 (3)
the solution method of the unmanned aerial vehicle positioning optimization model comprises the following steps:
the first step is to solve the multi-constraint simple semi-definite programming problem:
step1.1 initialization
Setting unmanned aerial vehicle starting coordinate beta0And an overlay parameter gamma0，τ0Setting step size { a }kWith a sequence of penalty parameters [ sigma ]k}，ak、σkIs a real number;
step1.2 iteration
The iteration index k is 0, …, N-1, N is a positive integer, and a positive integer i is randomly selected from 1, … mk，
Computing matrices
Figure BDA0003434008490000051
Maximum eigenvalue λ ofmaxAnd a maximum eigenvalue λmaxCorresponding feature vector
Figure BDA0003434008490000052
If λmax>0。
βk+1＝βk-2ak(βk-σkuβ)
γk+1＝γk-2ak(-1-σk)
Figure BDA0003434008490000053
Otherwise
βk+1＝βk-2ak(βk)
γk+1＝γk-2ak(-1)
Step1.3: output ((beta)N)T,γN,(τN)T)TTo obtain the coordinate betaN；
Second step of
Solving the sequence maximum element rho as 1+ max by using a loop strategyj＝1,…,nhj；
Step2.1：ρ＝0，j＝1；
Step2.2: if rho is less than or equal to hj，ρ＝hj，j＝j+1；
Step2.3: if j < N, turning to Step2.2;
step2.4, outputting rho, and finally obtaining the coordinate beta of the agricultural and forestry unmanned aerial vehicleNRadius of radiation of
Figure BDA0003434008490000054
Step2.5 end.
Numerical results
The invention relates to a fixed-direction robot and a steerable robot in a numerical experiment. Wherein the signal coverage effect is shown in fig. 3 and 4. In fig. 3 and 4, each ellipse represents an agricultural robot, the major axis of which is the real-time walking direction of the agricultural robot, and the whole ellipse represents the position where the robot is expected to appear after a period of time. The dashed line represents the area where the signal coverage of the UAV intersects the ground, already covering all agricultural and forestry robots. The circle indicates that the robot is temporarily at rest. Within the time of 20 seconds, if the moving speed of the agricultural and forestry robot in the working state is v meters/second, the long axis of the ellipse can be set to be 20v meters, and the mobile agricultural and forestry robot can be ensured to be always within the coverage range of the UAV signal.

Claims (2)

1. An unmanned aerial vehicle positioning method capable of covering agriculture and forestry robot signals based on an optimization technology is characterized by comprising the following steps: hardware depended by the method comprises an unmanned aerial vehicle and a plurality of agricultural and forestry robots M, wherein an airborne base station is arranged on the unmanned aerial vehicle, the agricultural and forestry robots M are supplemented with signals by the airborne base station, the distance measurement unit used in the method is meter, the time unit is minute, and an unmanned aerial vehicle positioning optimization model capable of covering the agricultural and forestry robot signals is established as follows:
the movement range of the ith agriculture and forestry robot Mi is defined as an elliptical area, and is marked as EiI is a positive integer having a major axis of
Figure FDA0003434008480000011
Short axis of
Figure FDA0003434008480000012
With EiThe position center of (1) is the origin of coordinates, the north-south direction is the longitudinal axis, and t is used2In which the east-west direction is the horizontal axis and is denoted by t1Is represented byiThe included angle between the long axis and the positive half axis of the transverse axis is theta i, EiIs a rotational transformation matrix of
Figure FDA0003434008480000013
EiThe equation of (a) is:
Figure FDA0003434008480000014
Eiequation data matrix of
Figure FDA0003434008480000015
Figure FDA0003434008480000016
Figure FDA0003434008480000017
Representation matrix AiIs positively defined and is Ai1, …, m, m being a positive integer;
translating the origin of the coordinate system to
Figure FDA00034340084800000122
Eta i is EiThe center coordinate point of the ellipse in the plane position coordinates in the GPS position,
Figure FDA00034340084800000123
constructing a maximum eigenvalue as λ1iAnd the second characteristic value is λ2iOf (2) matrix QiSaid matrix
Figure FDA0003434008480000018
Is that
Figure FDA0003434008480000019
Reciprocal of (2), QiCorresponding to λ1iAnd λ2iRespectively is u1iAnd u2iThen EiIs parallel to the minor axis of u1iThe long axis is parallel to u2i，EiRe-using a matrix as:
Figure FDA00034340084800000110
i is 1, …, m, x represents
Figure FDA00034340084800000111
The point (b) in (c) is,
Figure FDA00034340084800000112
is a two-dimensional real number space, EiIs centered at
Figure FDA00034340084800000113
The said machine of the said unmanned aerial vehicle carriesThe signal coverage of the base station is a circle B,
Figure FDA00034340084800000114
wherein β ═ β (β)1，β2) A coordinate point in a plane position coordinate representing a GPS position of the drone, gamma being a real number, a coverage radius of a signal of the drone before takeoff being
Figure FDA00034340084800000115
The height data of the tree is set as H ═ H1，…，hnN represents the number of trees, and when the height of the unmanned aerial vehicle from the ground is rho, the rho is required to meet the condition that the rho is more than or equal to 1+ hiAnd i is 1, … n, according to the pythagorean theorem, the radiation radius of the signal of the unmanned aerial vehicle in the air is
Figure FDA00034340084800000116
Due to the fact that
Figure FDA00034340084800000117
So that there is a positive integer τiSo that the following holds:
Figure FDA00034340084800000118
the unmanned aerial vehicle positioning optimization model is as follows:
Figure FDA00034340084800000119
because the optimization of the variable rho is irrelevant to the variables beta, gamma and tau, the unmanned aerial vehicle positioning optimization model is decomposed into two irrelevant simple optimization sub-problems:
multi-constraint simple semi-definite planning problem:
Figure FDA00034340084800000120
Figure FDA00034340084800000121
and the sequence maximum meta problem:
ρ＝1+maxj＝1，…，nhj。 (3)
2. the unmanned aerial vehicle positioning method based on optimization technology and capable of covering signal of agricultural and forestry robot of claim 1, wherein: the solution method of the unmanned aerial vehicle positioning optimization model comprises the following steps:
the first step is to solve the multi-constraint simple semi-definite programming problem:
step1.1: initialization
Setting unmanned aerial vehicle starting coordinate beta0And an overlay parameter gamma0，T0Setting step size { a }kWith a sequence of penalty parameters [ sigma ]k}，ak、σkIs a real number;
step1.2: iteration
An iteration index k is 0, N-1, N is a positive integer, and a positive integer i is randomly selected from 1, … mk，
Computing matrices
Figure FDA0003434008480000021
Maximum eigenvalue λ ofmaxAnd a maximum eigenvalue λmaxCorresponding feature vector
Figure FDA0003434008480000022
If λmax＞0，
βk+1＝βk-2ak(βk-σkuβ)
γk+1＝γk-2ak(-1-σk)
Figure FDA0003434008480000023
Otherwise
βk+1＝βk-2ak(βk)
γk+1＝γk-2ak(-1)
Step1.3: output of
Figure FDA0003434008480000025
Obtain the coordinate betaN；
Second step of
Solving the sequence maximum element rho as 1+ max by using a loop strategyj＝1，…，nhj；
Step2.1：ρ＝0，j＝1；
Step2.2: if rho is less than or equal to hj，ρ＝hj，j＝j+1；
Step2.3: if j is less than N, turning to Step2.2;
step2.4: outputting rho to finally obtain the coordinate beta of the agricultural and forestry unmanned aerial vehicleNRadius of radiation of
Figure FDA0003434008480000024
Step2.5: and (6) ending.
CN202111605836.0A 2021-12-25 2021-12-25 Unmanned aerial vehicle positioning method capable of covering signal of agricultural and forestry robot Pending CN114137473A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111605836.0A CN114137473A (en) 2021-12-25 2021-12-25 Unmanned aerial vehicle positioning method capable of covering signal of agricultural and forestry robot

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111605836.0A CN114137473A (en) 2021-12-25 2021-12-25 Unmanned aerial vehicle positioning method capable of covering signal of agricultural and forestry robot

Publications (1)

Publication Number Publication Date
CN114137473A true CN114137473A (en) 2022-03-04

Family

ID=80383298

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111605836.0A Pending CN114137473A (en) 2021-12-25 2021-12-25 Unmanned aerial vehicle positioning method capable of covering signal of agricultural and forestry robot

Country Status (1)

Country Link
CN (1) CN114137473A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114302339A (en) * 2021-12-25 2022-04-08 宁波凯德科技服务有限公司 Augmented Lagrange method capable of covering robot signal for positioning unmanned aerial vehicle

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114302339A (en) * 2021-12-25 2022-04-08 宁波凯德科技服务有限公司 Augmented Lagrange method capable of covering robot signal for positioning unmanned aerial vehicle

Similar Documents

Publication Publication Date Title
CN107933921B (en) Aircraft, spraying route generation and execution method and device thereof, and control terminal
US11526997B2 (en) Targeting agricultural objects to apply units of treatment autonomously
US11406052B2 (en) Cartridges to employ an agricultural payload via an agricultural treatment delivery system
US11812681B2 (en) Precision treatment of agricultural objects on a moving platform
US11465162B2 (en) Obscurant emission to assist image formation to automate agricultural management and treatment
US20230083872A1 (en) Pixel projectile delivery system to replicate an image on a surface using pixel projectiles
US20210185942A1 (en) Managing stages of growth of a crop with micro-precision via an agricultural treatment delivery system
US20210186006A1 (en) Autonomous agricultural treatment delivery
CN112528912A (en) Crop growth monitoring embedded system and method based on edge calculation
CN114137473A (en) Unmanned aerial vehicle positioning method capable of covering signal of agricultural and forestry robot
Joseph et al. Innovative analysis of precision farming techniques with artificial intelligence
US11653590B2 (en) Calibration of systems to deliver agricultural projectiles
Hiraguri et al. Autonomous drone-based pollination system using AI classifier to replace bees for greenhouse tomato cultivation
CN114302339A (en) Augmented Lagrange method capable of covering robot signal for positioning unmanned aerial vehicle
Yadav et al. Importance of drone technology in Indian agriculture, farming
Li et al. UAVs-Based Smart Agriculture IoT Systems: An Application-Oriented Design
Hu et al. Unmanned aerial vehicles for plant protection and precision agriculture: a study on low-altitude route planning method of unmanned aerial vehicles
DeepanshuSrivastava et al. UAVs in Agriculture
Breslla et al. Sensor-fusion and deep neural networks for autonomous UAV navigation within orchards
Romero-Lugo et al. A Review on Deep Learning UAV Systems for Visual Obstacle Detection in Crop Environments
Shrinidhi et al. UAV Platform for Pesticide Spraying and Disease Detection for Areca Nut and Pepper Plantations
Acevedo Ramos Machine learning based energy-efficient uav trajectory design for site-specific crop spraying
Gomathi et al. Computer vision for unmanned aerial vehicles in agriculture: applications, challenges, and opportunities
Malook et al. Artificial Intelligence in Agriculture for Application of Pesticides and Herbicides
Ouyang et al. Research on Adaptive Navigation System of Mountain Orchard Plant Protection Unmanned Aerial Vehicle Based on Simultaneous Localization and Mapping and Global Navigation Satellite System Fusion

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination