CN110147040B - Flight trajectory and power distribution joint optimization method for energy-carrying transmission of unmanned aerial vehicle - Google Patents

Flight trajectory and power distribution joint optimization method for energy-carrying transmission of unmanned aerial vehicle Download PDF

Info

Publication number
CN110147040B
CN110147040B CN201910283356.3A CN201910283356A CN110147040B CN 110147040 B CN110147040 B CN 110147040B CN 201910283356 A CN201910283356 A CN 201910283356A CN 110147040 B CN110147040 B CN 110147040B
Authority
CN
China
Prior art keywords
unmanned aerial
aerial vehicle
energy
power distribution
optimization
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.)
Active
Application number
CN201910283356.3A
Other languages
Chinese (zh)
Other versions
CN110147040A (en
Inventor
陈瑾
黄斐
丁国如
王海超
龚玉萍
郑学强
罗屹洁
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.)
Army Engineering University of PLA
Original Assignee
Army Engineering University of PLA
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 Army Engineering University of PLA filed Critical Army Engineering University of PLA
Priority to CN201910283356.3A priority Critical patent/CN110147040B/en
Publication of CN110147040A publication Critical patent/CN110147040A/en
Application granted granted Critical
Publication of CN110147040B publication Critical patent/CN110147040B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Supply And Distribution Of Alternating Current (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)

Abstract

The invention discloses a flight trajectory and power distribution joint optimization method for energy-carrying transmission of an unmanned aerial vehicle. The method comprises the following steps: modeling the flight path design and power distribution optimization problem of energy-carrying transmission of the unmanned aerial vehicle; modeling an optimization problem of updating the unmanned aerial vehicle transmitting power distribution by fixing the flight path of the unmanned aerial vehicle into a first subproblem; distributing the transmitting power of the fixed unmanned aerial vehicle, and modeling an optimization problem of updating the flight path of the unmanned aerial vehicle into a second subproblem; and performing alternate iterative optimization on the two subproblems, and performing combined optimization on the flight trajectory and the transmission power distribution of the unmanned aerial vehicle by adopting a combined optimization algorithm. The invention can effectively solve the problems that the ground node energy in the Internet of things is limited, wired charging is difficult or impossible to realize, and meanwhile, energy carrying transmission with information receiving requirements is required; can utilize unmanned aerial vehicle's flight characteristic to improve the channel situation, improve ground node and take the energy collection efficiency that can the transmission course at unmanned aerial vehicle to the promotion that can the transmission performance is taken in the acquisition.

Description

Flight trajectory and power distribution joint optimization method for energy-carrying transmission of unmanned aerial vehicle
Technical Field
The invention relates to the field of communication between the Internet of things and an unmanned aerial vehicle in a wireless communication technology, in particular to a flight trajectory and power distribution joint optimization method for energy-carrying transmission of the unmanned aerial vehicle.
Background
Currently, with the innovation of wireless communication technology, the internet of things system is receiving more and more research attention. On the one hand, the ground internet of things equipment has information instruction requirements to complete the internet of things service lacking in infrastructure. Internet of things devices, on the other hand, are typically energy-limited. Traditionally, wireless terminals are battery-charged, and in order to extend the life of the network, the batteries must be manually replaced or recharged. It is often costly, inconvenient, dangerous (in toxic environments), or even infeasible (such as sensors mounted inside buildings or medical devices implanted in the body).
Emerging drone technology is being used as an innovative approach to complement existing systems of internet of things. Compared with a traditional ground wireless communication network, the unmanned aerial vehicle serving as an air transceiver has many advantages. On the one hand, drones have highly controllable mobility and low cost, and on the other hand, drone auxiliary systems are more flexible and faster in terms of deployment and reconfiguration. Moreover, due to the possibility of having a larger line-of-sight transmission link, there is better channel conditions between the drone and the ground equipment to increase system capacity. In addition, unmanned aerial vehicle can move to thing networking device dynamically to improve the efficiency of information transmission and energy transfer. However, in many practical scenarios, information transmission or energy transfer alone is not sufficient in an internet of things system. Therefore, the simultaneous information and energy transmission technology, namely the energy-carrying transmission technology, has an important application prospect in the internet of things, and the research is hot in recent years.
The research of the unmanned aerial vehicle auxiliary internet of things system is still in a starting stage. Most of the existing research focuses on unmanned aerial vehicles transmitting information to or receiving information from ground internet of things equipment. And in the last few years, some research work for unmanned aerial vehicle-assisted information transmission or energy transmission has emerged, but has not been applied in the scene of the internet of things. On the other hand, although the subject of energy-carrying transmission in wireless networks has been widely studied in recent years, most of the research results are not directly applied to the unmanned aerial vehicle-assisted internet of things system.
Disclosure of Invention
The invention aims to provide a flight trajectory and power distribution combined optimization method for energy-carrying transmission of an unmanned aerial vehicle. The flight trajectory and the power distribution of the energy-carrying transmission of the unmanned aerial vehicle are jointly optimized, the maximum of the minimum energy collected by the ground internet of things equipment is achieved, and the requirement that each ground equipment receives a certain amount of data is met.
The technical solution for realizing the purpose of the invention is as follows:
a flight trajectory and power distribution joint optimization method for energy-carrying transmission of an unmanned aerial vehicle comprises the following steps:
step 1, modeling the optimization problem of flight trajectory design and power distribution of energy-carrying transmission of the unmanned aerial vehicle. The optimization problem comprises an optimization target, an optimization variable and a constraint condition;
and 2, decomposing the original problem in the step 1 into two sub-problems. Modeling an optimization problem of fixed unmanned aerial vehicle flight trajectory and updating unmanned aerial vehicle transmitting power distribution into a first subproblem; distributing the transmitting power of the fixed unmanned aerial vehicle, and modeling an optimization problem of updating the flight path of the unmanned aerial vehicle into a second subproblem;
and 3, alternately and iteratively optimizing the two sub-problems in the step 2, and performing combined optimization on the flight trajectory and the transmission power distribution of the unmanned aerial vehicle by adopting a combined optimization algorithm.
Further, the model for the flight trajectory design and power distribution problem of the energy-carrying transmission of the unmanned aerial vehicle in the step 1 is as follows,
Figure BDA0002022431830000021
where E is the lower bound of the smallest of the collected energy among all ground nodes.
Figure BDA0002022431830000022
Is a collection of ground nodes, each node
Figure BDA0002022431830000023
Has the coordinates of (x)k,yk0), and w)k＝(xk,yk)。
Figure BDA0002022431830000024
Is a set of working hours and is,
Figure BDA0002022431830000025
is a set of time slots, so δ ttT/N is the unit slot length. The height of the unmanned aerial vehicle is H, and the position of the unmanned aerial vehicle in the nth time slot is (x)U[n],yU[n]H), wherein q [ n ]]＝(xU[n],yU[n]). Initial position of unmanned aerial vehicle andthe coordinates of the projection of the end position onto the ground are denoted qI＝q[0]＝(x0,y0) And q isF＝q[N]＝(xF,yF). The maximum flying speed of the unmanned aerial vehicle is Vmax. The division ratio of each node is the same, wherein the received power of the node ratio rho is sent to the energy collector, and the received power of the node ratio 1-rho is sent to the information receiver. In time T, the total power transmitted to the ground node by the unmanned aerial vehicle is P0. B is the bandwidth of the entire system. Beta is a0Is the channel power gain per unit distance and alpha is the ambient attenuation factor. p is a radical ofk[n]And in the nth time slot, the power transmitted to the kth node by the unmanned aerial vehicle, and the eta is more than or equal to 0 and less than or equal to 1, so that the energy conversion efficiency of each node rectifier is obtained. Sigma2Is the noise power spectral density. In addition, all ground nodes are respectively and simultaneously responsible for the task of receiving information besides energy collection, and the data volume threshold of each node is considered to be gammath。
Further, the problem of fixing the flight path of the unmanned aerial vehicle and updating the transmission power distribution of the unmanned aerial vehicle in the step 2 is modeled as follows,
Figure BDA0002022431830000031
the above problem can be solved by using an interior point algorithm.
Further, the problem establishment model for allocating the transmission power of the fixed unmanned aerial vehicle and updating the flight path of the unmanned aerial vehicle in the step 2 is as follows,
Figure BDA0002022431830000041
wherein,
Figure BDA0002022431830000042
Figure BDA0002022431830000043
with first-order Taylor expansion, we get the (i + 1) th iteration given the ith iteration unmanned aerial vehicle trajectory
Figure BDA0002022431830000044
And
Figure BDA0002022431830000045
wherein
Figure BDA0002022431830000046
Lower boundary of (1)
Figure BDA0002022431830000047
And
Figure BDA0002022431830000048
lower bound of (2)
Figure BDA0002022431830000049
Therefore:
Figure BDA00020224318300000410
because of the function
Figure BDA00020224318300000411
Is a convex function, so:
Figure BDA00020224318300000412
let a equal to ρ η β0pk[n]And b ═ H2To obtain:
Figure BDA00020224318300000413
then, because
Figure BDA00020224318300000414
Is a convex function of x, obtained by Taylor expansionAnd (3) discharging:
Figure BDA0002022431830000051
let x | | qi+1[n]-ωk||2-||qi[n]-ωk||2，b＝||qi[n]-ωk||2+H2And
Figure BDA0002022431830000052
based on equation (14), we obtain:
Figure BDA0002022431830000053
wherein,
Figure BDA0002022431830000054
is that
Figure BDA0002022431830000055
The lower bound of (c).
The above problem can be solved by using an interior point algorithm.
Further, the flight trajectory and transmission power allocation joint optimization algorithm of the unmanned aerial vehicle described in step 3 is specifically realized by the following steps:
1. and (5) initializing.
Input variable pk[n]And q [ n ]]Is initialized to
Figure BDA0002022431830000056
And q is0[n]The iteration number i is 0 and the error precision epsilon.
2. And (5) performing iterative operation.
In this step, the following operations are performed iteratively in sequence:
(1) fixing q [ n ]]Is qi[n]Solving the subproblem one to obtain an optimized variable
Figure BDA0002022431830000057
Of (2) an optimal solution
Figure BDA0002022431830000058
(2) Fixing
Figure BDA0002022431830000059
Is composed of
Figure BDA00020224318300000510
Solving the second subproblem to obtain an optimized variable qi[n]Of (2) an optimal solution q*[n]；
(3) Update { p [ n ]],q[n]}i+1＝{p[n],q[n]}*；
(4) When it is satisfied with
Figure BDA0002022431830000061
When i is i +1, jump to (1); otherwise the iteration terminates.
3. Output of
Output unmanned aerial vehicle in all time slots
Figure BDA0002022431830000062
Flight path q [ n ] of unmanned aerial vehicle]And is assigned to
Figure BDA0002022431830000063
Transmitting power p of ground nodek[n]。
Compared with the prior art, the invention has the following advantages:
1. the invention can effectively solve the problems that the ground node energy is limited, the wired charging is difficult or impossible to realize, and meanwhile, the energy-carrying transmission with the information receiving requirement is required;
2. according to the invention, the flight characteristics of the unmanned aerial vehicle can be utilized to improve the channel condition, the ground node energy collection efficiency in the energy-carrying transmission process of the unmanned aerial vehicle is improved, and the energy-carrying transmission performance is improved.
Drawings
Fig. 1 is a flowchart of implementation steps of a flight trajectory and power distribution joint optimization method for energy-carrying transmission of an unmanned aerial vehicle according to the present invention.
FIG. 2 is a schematic diagram of a system model according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of a power division method in an embodiment of the present invention.
FIG. 4 is a graph of algorithm convergence in an embodiment of the present invention.
FIG. 5 is a comparison graph of an optimization algorithm in an embodiment of the present invention.
Fig. 6 is a schematic diagram of the trajectory and speed of the drone of case 1 in the embodiment of the present invention.
Fig. 7 is a schematic diagram of the trajectory and speed of the drone of case 2 in an embodiment of the present invention.
Fig. 8 is a diagram of energy collection and information reception at different thresholds according to an embodiment of the present invention.
Fig. 9 is a graph comparing energy collection and information reception at different power division ratios according to an embodiment of the present invention.
Detailed Description
The invention relates to a joint optimization method for flight path and power distribution of energy-carrying transmission of an unmanned aerial vehicle, which comprises the following steps as shown in figure 1:
step 1, modeling the optimization problem of flight trajectory design and power distribution of energy-carrying transmission of the unmanned aerial vehicle. The optimization problem comprises an optimization target, an optimization variable and a constraint condition;
step 2, decomposing the original problem in the step 1 into two sub-problems; modeling an optimization problem of fixed unmanned aerial vehicle flight trajectory and updating unmanned aerial vehicle transmitting power distribution into a first subproblem; distributing the transmitting power of the fixed unmanned aerial vehicle, and modeling an optimization problem of updating the flight path of the unmanned aerial vehicle into a second subproblem;
and 3, alternately and iteratively optimizing the two sub-problems in the step 2 and the step 3, and performing joint optimization on the flight path and the transmission power distribution of the unmanned aerial vehicle by adopting a joint optimization algorithm.
The invention is implemented as follows:
firstly, according to the model for establishing the flight trajectory design and power distribution problem of the energy-carrying transmission of the unmanned aerial vehicle in the step 1, the model comprises the following steps:
Figure BDA0002022431830000071
where E is the lower bound of the smallest of the collected energy among all ground nodes.
Figure BDA0002022431830000072
Is a collection of ground nodes, each node
Figure BDA0002022431830000073
Has the coordinates of (x)k,yk0), and w)k＝(xk,yk)。
Figure BDA0002022431830000074
Is a set of working hours and is,
Figure BDA0002022431830000075
is a set of time slots, sotT/N is the unit slot length. The height of the unmanned plane is H, and the position of the unmanned plane in the nth time slot is (x)U[n],yU[n]H), wherein q]n]＝(xU[n],yU[n]). The coordinates of the initial position and the final position of the unmanned aerial vehicle projected to the ground are represented as qI＝q[0]＝(x0,y0) And q isF＝q[N]＝(xF,yF). The maximum flying speed of the unmanned aerial vehicle is Vmax. The division ratio of each node is the same, wherein the received power of the node ratio rho is sent to the energy collector, and the received power of the node ratio 1-rho is sent to the information receiver. In time T, the total power transmitted to the ground node by the unmanned aerial vehicle is P0. B is the bandwidth of the entire system. Beta is a beta0Is the channel power gain per unit distance and alpha is the ambient attenuation factor. p is a radical ofk[n]And in the nth time slot, the power transmitted to the kth node by the unmanned aerial vehicle, and the eta is more than or equal to 0 and less than or equal to 1, so that the energy conversion efficiency of each node rectifier is obtained. Sigma2Is the noise power spectral density. Setting the data quantity threshold of each node as gammath。
Secondly, according to the flight track of the fixed unmanned aerial vehicle in the step 2, updating the problem establishment model of the unmanned aerial vehicle transmission power distribution as follows:
Figure BDA0002022431830000081
the above problem can be solved by using an interior point algorithm.
Thirdly, according to the emission power distribution of the fixed unmanned aerial vehicle in the step 2, the problem establishment model for updating the flight path of the unmanned aerial vehicle is as follows:
Figure BDA0002022431830000082
wherein,
Figure BDA0002022431830000091
Figure BDA0002022431830000092
q in the above formulae (20) and (21)i[n]And q isi+1[n]Is the position of the ith iteration of the slot drone.
The above problem can be solved by using an interior point algorithm.
Fourthly, according to the flight track and emission power distribution joint optimization algorithm of the unmanned aerial vehicle in the step 3, specifically, the method is realized through the following steps:
1. and (5) initializing.
Input variable pk[n]And q [ n ]]Is initialized to
Figure BDA0002022431830000093
And q is0[n]The iteration number i is 0 and the error precision epsilon.
2. And (5) performing iterative operation.
In this step, the following operations are iteratively performed in sequence:
(1) fixing q [ n ]]Is qi[n]To find outSolving the subproblem one to obtain an optimized variable
Figure BDA0002022431830000094
Of (2) an optimal solution
Figure BDA0002022431830000095
(2) Fixing
Figure BDA0002022431830000096
Is composed of
Figure BDA0002022431830000097
Solving the second subproblem to obtain an optimized variable qi[n]Is calculated by the optimal solution q*[n]；
(3) Update { p [ n ]],q[n]}i+1＝{p[n],q[n]}*；
(4) When it is satisfied with
Figure BDA0002022431830000098
When i is i +1, jump to (1); otherwise the iteration terminates.
3. Output of
Output unmanned aerial vehicle in all time slots
Figure BDA0002022431830000101
Flight path q [ n ] of unmanned aerial vehicle]And is assigned to
Figure BDA0002022431830000102
Transmitting power p of ground nodek[n]。
Example (b): one embodiment of the present invention is described below, in which the system simulation uses Matlab software. The following embodiments examine the effectiveness and superiority of the unmanned aerial vehicle flight trajectory and transmission power distribution optimization method for energy-carrying transmission of the unmanned aerial vehicle in the internet of things.
In this embodiment, as shown in fig. 2, the unmanned aerial vehicle flies in the air and carries out energy-carrying transmission to the ground node. In simulation, the random distribution of K-4 ground nodes in the size of 100 multiplied by 100m is considered2Within the square area of (a). Unmanned aerial vehicle's flightThe height H is 10m, and the initial position and the end position of the flight are set to (0,0,10) and (100, 10), respectively. Consider that the sum of the transmit power of the drone is P0The sum of the channel bandwidths is B1 MHz at 1W. Other parameters are set as: t30 s, N60, η 0.5, β0＝10-3α -2 and ε -10-4Length of unit slot of deltat0.5 s. The reference track of the unmanned aerial vehicle is the connecting line of the starting point and the ending point. To verify the validity of the algorithm design, two cases were studied:
case 1: the ground nodes are distributed on two sides of the reference track. The reference trajectory is flying at a uniform speed along the path.
Case 2: the ground nodes are distributed on one side of the reference track. As in the reference trace of case 1.
In two cases, the maximum flight speed of the unmanned aerial vehicle is respectively researched to be Vmax10m/s and Vmax20 m/s.
As shown in fig. 4, in case 1 and case 2, as the number of iterations increases, the objective function tends to converge, verifying the convergence of the algorithm. And further, the larger the maximum flying speed of the unmanned aerial vehicle is, the larger the maximum value of the minimum ground node energy collection is. Wherein gamma isth＝102Mbits。
As shown in FIG. 5, the performance superiority of the algorithm is verified by comparing the proposed algorithm with two reference algorithms, namely, optimizing only the power and optimizing only the track. The reason is that the unmanned aerial vehicle allocates more power to the ground node under the condition that the channel condition is better.
As shown in fig. 6-1 and 7-1, the drone speed drops to 0m/s at certain times, indicating that the drone is hovering during this period of time. The greater the maximum airspeed of the drone, the longer the drone is hovering. As shown in fig. 6-2 and 7-2, the longer the drone is hovering at a node, the less power it will obtain at that node. And vice versa. In addition, when the drone hovers at any node, the greater the maximum flight speed of the drone, the less power is allocated to the respective node. This reflects the fairness of our design, as the system considers that all nodes get the maximum of the smallest of the energies. It can also be derived that the closer the drone is to the node, the greater the power allocated to the node because the higher the channel gain.
As shown in fig. 8, each node does not receive more data than its upper bound at different thresholds. In case 1, when the threshold is set
Figure BDA0002022431830000111
Constraint C1 in problem (8) will not affect the optimal objective function value and the minimum value of the received information in all nodes. When the threshold is gammathFrom
Figure BDA0002022431830000112
Increase to
Figure BDA0002022431830000113
The maximum of the minimum of the collected energy among all the nodes is reduced, and the maximum of the minimum of the received information is infinitely close to the upper bound
Figure BDA0002022431830000114
As shown in fig. 9, at γthOn the basis of 10Mbits, the interval is set to be 0.1, p is adjusted from 0.1 to 0.9, and the flight path and the power distribution trend of the unmanned aerial vehicle are not changed. As the division ratio ρ increases, the more energy collected, the less data amount is received.

Claims (4)

1. A flight trajectory and power distribution joint optimization method for energy-carrying transmission of an unmanned aerial vehicle is characterized by comprising the following steps:
step 1, modeling an optimization problem of flight trajectory design and power distribution of energy-carrying transmission of an unmanned aerial vehicle, wherein the optimization problem comprises an optimization target, an optimization variable and a constraint condition, and specifically comprises the following steps:
Figure FDA0003517011990000011
wherein E is the intermediate reception of all ground nodesLower bound of the minimum energy;
Figure FDA0003517011990000012
is a set of ground nodes, each node k ∈ having coordinates of (x)k，yk0), and w)k＝(xk，yk)；
Figure FDA0003517011990000014
Is a set of working hours and is,
Figure FDA0003517011990000015
is a set of time slots, sotT/N is the unit slot length; the height of the unmanned aerial vehicle is H, and the position of the unmanned aerial vehicle in the nth time slot is (x)U[n],yU[n]H), wherein q [ n ]]＝(xU[n],yU[n]) (ii) a The coordinates of the initial position and the final position of the unmanned aerial vehicle projected to the ground are represented as qI＝q[0]＝(x0,y0) And q isF＝q[N]＝(xF,yF) (ii) a The maximum flying speed of the unmanned aerial vehicle is Vmax(ii) a The division ratio of each node is the same, wherein the received power of the node ratio rho is sent to an energy collector, and the received power of the node ratio 1-rho is sent to an information receiver; in time T, the total power transmitted to the ground node by the unmanned aerial vehicle is P0(ii) a B is the bandwidth of the whole system; beta is a0Is the channel power gain per unit distance, and α is the environmental attenuation factor; p is a radical ofk[n]The power transmitted to the kth node by the unmanned aerial vehicle is the nth time slot, and the eta is more than or equal to 0 and less than or equal to 1, which is the energy conversion efficiency of each node rectifier; sigma2Is the noise power spectral density; setting the data quantity threshold of each node as gammath；
Step 2, decomposing the original problem in the step 1 into two sub-problems; modeling an optimization problem of fixed unmanned aerial vehicle flight trajectory and updating unmanned aerial vehicle transmitting power distribution into a first subproblem; distributing the transmitting power of the fixed unmanned aerial vehicle, and modeling an optimization problem of updating the flight path of the unmanned aerial vehicle into a second subproblem;
and 3, alternately and iteratively optimizing the two sub-problems in the step 2 and the step 3, and performing combined optimization on the flight trajectory and the transmission power distribution of the unmanned aerial vehicle by adopting a combined optimization algorithm.
2. The unmanned aerial vehicle flight trajectory and power distribution joint optimization method according to claim 1, wherein: the problem establishing model for fixing the flight path of the unmanned aerial vehicle and updating the transmission power distribution of the unmanned aerial vehicle in the step 2 is as follows,
Figure FDA0003517011990000021
the problem is solved by adopting an interior point algorithm.
3. The unmanned aerial vehicle energy-carrying transmission flight trajectory and power distribution joint optimization method according to claim 1, wherein the method comprises the following steps: the problem establishment model for fixing the emission power distribution of the unmanned aerial vehicle and updating the flight path of the unmanned aerial vehicle in the step 2 is as follows,
Figure FDA0003517011990000022
wherein,
Figure FDA0003517011990000031
Figure FDA0003517011990000032
q in the above formulae (5) and (6)i[n]And q isi+1[n]The position of the ith iteration of the unmanned aerial vehicle at the nth time slot is solved by adopting an interior point algorithm.
4. The unmanned aerial vehicle energy-carrying transmission flight trajectory and power distribution joint optimization method according to claim 1, wherein the method comprises the following steps: and 3, performing combined optimization on the flight trajectory and the transmission power distribution of the unmanned aerial vehicle by adopting a combined optimization algorithm, and specifically, realizing the following steps:
5.1 initialization
Input variable pk[n]And q [ n ]]Is initialized to
Figure FDA0003517011990000033
And q is0[n]The iteration number i is 0, and the error precision is epsilon;
5.2 iterative operations
In this step, the following operations are iteratively performed in sequence:
(1) fixing q [ n ]]Is qi[n]Solving the subproblem one to obtain an optimized variable
Figure FDA0003517011990000034
Optimal solution of
Figure FDA0003517011990000035
(2) Fixing the device
Figure FDA0003517011990000036
Is composed of
Figure FDA0003517011990000037
Solving the second subproblem to obtain an optimized variable qi[n]Of (2) an optimal solution q*[n]；
(3) Update { p [ n ]],q[n]}i+1＝{p[n],q[n]}*；
(4) When it is satisfied with
Figure FDA0003517011990000038
When i is i +1, jump to (1); otherwise the iteration terminates.
5.3 output
Outputting the flight track q [ n ] of the unmanned aerial vehicle on all the time slots n ∈]And the transmission power p allocated to the kth e ground nodek[n]。
CN201910283356.3A 2019-04-10 2019-04-10 Flight trajectory and power distribution joint optimization method for energy-carrying transmission of unmanned aerial vehicle Active CN110147040B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201910283356.3A CN110147040B (en) 2019-04-10 2019-04-10 Flight trajectory and power distribution joint optimization method for energy-carrying transmission of unmanned aerial vehicle

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201910283356.3A CN110147040B (en) 2019-04-10 2019-04-10 Flight trajectory and power distribution joint optimization method for energy-carrying transmission of unmanned aerial vehicle

Publications (2)

Publication Number Publication Date
CN110147040A CN110147040A (en) 2019-08-20
CN110147040B true CN110147040B (en) 2022-05-20

Family

ID=67588711

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201910283356.3A Active CN110147040B (en) 2019-04-10 2019-04-10 Flight trajectory and power distribution joint optimization method for energy-carrying transmission of unmanned aerial vehicle

Country Status (1)

Country Link
CN (1) CN110147040B (en)

Families Citing this family (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN110730495B (en) * 2019-10-15 2022-07-19 中国人民解放军陆军工程大学 Unmanned aerial vehicle data distribution optimization method under energy constraint
CN110730031B (en) * 2019-10-22 2022-03-11 大连海事大学 Unmanned aerial vehicle track and resource allocation joint optimization method for multi-carrier communication
CN111182469B (en) * 2020-01-07 2021-04-16 东南大学 Energy collection network time distribution and unmanned aerial vehicle track optimization method
CN111327355B (en) * 2020-01-21 2021-03-12 北京大学 Unmanned aerial vehicle edge perception calculation and joint transmission method, device, medium and equipment
CN111405581B (en) * 2020-03-20 2023-03-28 重庆邮电大学 Energy-carrying unmanned aerial vehicle position deployment method for maximum weighted energy collection
CN112104502B (en) * 2020-09-16 2021-10-12 云南大学 Time-sensitive multitask edge computing and cache cooperation unloading strategy method
CN112637817B (en) * 2020-12-30 2022-01-04 珠海大横琴科技发展有限公司 Access control method and device, electronic equipment and storage medium
CN112904727B (en) * 2021-01-21 2022-06-24 四川大学 Wireless charging unmanned aerial vehicle model, optimization method and system thereof, and computer medium
CN118226888A (en) * 2024-05-22 2024-06-21 南京邮电大学 RSMA-based multi-unmanned aerial vehicle auxiliary data acquisition system optimization method

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1350244A (en) * 2000-10-25 2002-05-22 日本电气株式会社 Minimum cost path searching apparatus and minimum cost path searching method therefor
CN106774429A (en) * 2016-12-05 2017-05-31 北京邮电大学 A kind of data transmission method and system
CN107994939A (en) * 2017-12-04 2018-05-04 中国人民解放军陆军工程大学 Self-adaptive microwave communication data link based on unmanned aerial vehicle dynamic relay
CN108880662A (en) * 2018-07-16 2018-11-23 深圳大学 A kind of optimization method of wireless messages and energy transmission based on unmanned plane
CN108924791A (en) * 2018-07-13 2018-11-30 广东工业大学 A kind of wireless communications method, device, equipment and readable storage medium storing program for executing

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
EP1571515A1 (en) * 2004-03-04 2005-09-07 Leica Geosystems AG Method and apparatus for managing data relative to a worksite area

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1350244A (en) * 2000-10-25 2002-05-22 日本电气株式会社 Minimum cost path searching apparatus and minimum cost path searching method therefor
CN106774429A (en) * 2016-12-05 2017-05-31 北京邮电大学 A kind of data transmission method and system
CN107994939A (en) * 2017-12-04 2018-05-04 中国人民解放军陆军工程大学 Self-adaptive microwave communication data link based on unmanned aerial vehicle dynamic relay
CN108924791A (en) * 2018-07-13 2018-11-30 广东工业大学 A kind of wireless communications method, device, equipment and readable storage medium storing program for executing
CN108880662A (en) * 2018-07-16 2018-11-23 深圳大学 A kind of optimization method of wireless messages and energy transmission based on unmanned plane

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
下一代无人机群协同通信网络;邹玉龙;《南京邮电大学学报》;20170630;第37卷(第3期);第43-51页 *

Also Published As

Publication number Publication date
CN110147040A (en) 2019-08-20

Similar Documents

Publication Publication Date Title
CN110147040B (en) Flight trajectory and power distribution joint optimization method for energy-carrying transmission of unmanned aerial vehicle
CN110380773B (en) Trajectory optimization and resource allocation method of unmanned aerial vehicle multi-hop relay communication system
CN110381444B (en) Unmanned aerial vehicle track optimization and resource allocation method
CN109099918B (en) Unmanned aerial vehicle-assisted wireless energy transmission system and node scheduling and path planning method
CN110730031B (en) Unmanned aerial vehicle track and resource allocation joint optimization method for multi-carrier communication
CN110364031B (en) Path planning and wireless communication method for unmanned aerial vehicle cluster in ground sensor network
CN110730495B (en) Unmanned aerial vehicle data distribution optimization method under energy constraint
CN109831797B (en) Unmanned aerial vehicle base station bandwidth and track joint optimization method with limited push power
CN109753082B (en) Multi-unmanned aerial vehicle network cooperative communication method
WO2020015214A1 (en) Optimization method for wireless information and energy transmission based on unmanned aerial vehicle
CN108848465A (en) Unmanned aerial vehicle flight trajectory and resource scheduling joint optimization method oriented to data distribution
Xue et al. Maximization of data dissemination in UAV-supported Internet of Things
CN110392357A (en) A kind of resource allocation control method of unmanned plane adminicle networked communication device
CN112911537B (en) Method for minimizing task time of multi-unmanned aerial vehicle information acquisition system
CN110381445A (en) A kind of resource allocation based on unmanned plane base station system and flight path optimization method
CN111586718B (en) Fountain code design method for unmanned aerial vehicle relay communication system
CN111953407A (en) Unmanned aerial vehicle video relay system and energy consumption minimizing method thereof
CN113776531B (en) Multi-unmanned aerial vehicle autonomous navigation and task allocation algorithm of wireless self-powered communication network
CN112333648A (en) Dynamic data collection method based on unmanned aerial vehicle
CN115065976B (en) High-efficiency green three-dimensional coverage scheme for global emergency communication scene
CN116707686A (en) Minimum task time resource management method for unmanned aerial vehicle auxiliary backscatter communication system
CN116170776A (en) Unmanned aerial vehicle wireless energy supply air calculation auxiliary Internet of things data acquisition method
Du et al. Time-constrained UAV-aided data collection for IoT networks with energy harvesting
Jing et al. UAV trajectory design and bandwidth allocation for coverage maximization with energy and time constraints
CN113641184B (en) 3D path planning and resource scheduling method suitable for multifunctional communication of unmanned aerial vehicle

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
GR01 Patent grant
GR01 Patent grant