CN110166110A - Unmanned plane paths planning method based on edge calculations - Google Patents
Unmanned plane paths planning method based on edge calculations Download PDFInfo
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- CN110166110A CN110166110A CN201910428032.4A CN201910428032A CN110166110A CN 110166110 A CN110166110 A CN 110166110A CN 201910428032 A CN201910428032 A CN 201910428032A CN 110166110 A CN110166110 A CN 110166110A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B7/00—Radio transmission systems, i.e. using radiation field
- H04B7/14—Relay systems
- H04B7/15—Active relay systems
- H04B7/185—Space-based or airborne stations; Stations for satellite systems
- H04B7/18502—Airborne stations
- H04B7/18506—Communications with or from aircraft, i.e. aeronautical mobile service
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimising operational condition
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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
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
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Abstract
The invention discloses based on unmanned plane method for optimizing route in edge calculations.In the 5G epoch, users' mobile end when executing some delay-sensitive programs, in view of the limitation of itself computing capability and the finiteness of the energy content of battery, it needs the application data for calculating processing to be sent to and carries out data processing positioned at the server of network edge acies, to reduce data processing delay and energy saving.The present invention considers for edge calculations server rack to be set on unmanned plane, provides Data Migration processing service come fast approaching user using the high-speed mobile performance of unmanned plane for it.Further, the present invention considers TDMA communication mode, a kind of optimization algorithm is designed to optimize to user-association and unmanned plane during flying path, so that maximizing user on the basis of meeting QoS of customer and unmanned plane during flying energy consumption moves to the calculating data volume on unmanned plane server.
Description
Technical field
The present invention relates to wireless communication technology field, in particular in a kind of edge calculations, nobody based on energy limit
The method of machine path planning.
Background technique
With the development of the 5G communication technology, the application of intelligent sliding moved end is just gradually grown, and so many mobile phone application is produced
Raw flood tide network data and calculation amount also propose high requirement to calculating service and network delay.Traditional cloud meter
Network is calculated since its interval user distance is remote and often congestion occurs, so that some delay-sensitive applications are in mobile client hardly possible
With operation.Currently, a kind of emerging calculating concept is just being gradually appeared in the public visual field, it is exactly edge calculations.
Edge calculations are by assuming server in network edge acies, so that it is away from closer to the user, and this greatly reduces use
The transmission data at family postpone, and greatly reduce since its distributed structure/architecture makes network a possibility that congestion occur.User can be with
Required calculating task to be processed is uploaded to Edge Server in a very short period of time to calculate, in server by utilizing, it is remote high
While the processor of user's processing speed calculates user task, energy also is saved for the equipment of users' mobile end, because
In general the energy that user uploads data is far smaller than energy consumed by its own processor.
Certainly, it is contemplated that general static edge calculation server deployment consumption, so that it is difficult to dispose in remote mountain areas.
Therefore, the present invention considers to make it possible to using UAV flight's edge calculations server using unmanned plane high-speed mobile performance
High-speed mobile is that user unloads calculating task back and forth between users.
The only limitation in view of user fairness is largely studied based on unmanned plane path planning at present, and as one
A mobile base station, the energy consumption of unmanned plane are extremely critical problems in need of consideration.Once unmanned plane will not have because electricity exhausts
There are enough energy to maintain it to fly and provide communication for user and calculate and service.The present invention considers unmanned machine battery
The limitation of energy, it is ensured that unmanned plane is in the case where calculating energy consumption and flight energy consumption is less than the energy content of battery, by optimizing unmanned plane
The flight path of being associated between user and unmanned plane comes so that the total handling capacity of system is maximum, to greatly improve nothing
Man-machine efficiency.
Summary of the invention
The edge calculations system for considering a kind of UAV flight's calculation server, in the limit of energy and QoS of customer
Under system, the present invention maximizes the handling capacity of system by optimizing user-association and unmanned plane during flying track simultaneously.
Consider K terrestrial user, using coordinate representation respectively in ground location is (xk,yk).Have frame carrying on user in vain
The unmanned plane of calculation server provides calculating service for terrestrial user, and unmanned plane height is fixed as H.Entire unmanned plane is user's clothes
The when a length of T of business, whole cycle T is divided for N number of equal time slot, and each slot length isAssuming that unmanned plane is in each time slot
In position remain unchanged, then unmanned plane is represented by (x [n], y [n], H) in the position of n-th of time slot.Unmanned plane time uses
TDMA mode and user communicate, i.e., can only communicate with a user in each time slot unmanned plane, if associated variable αk
[n]=1 indicates that unmanned plane carries out communication association in n-th of time slot and user k.
When unmanned plane establishes communication connection between n and user k, user's uploading rate Wherein p0For user emission power, ρ is every m user's gain, σ2For noise power.One
The upload task amount of single user k in periodConsider QoS of customer and
Fairness, single user's task amount need to meet the i.e. S of minimum discharging quantity limitationk≤S0, wherein S0For constant, one week of user is indicated
It is minimum in phase to upload task amount.
On the other hand, the flight energy consumption of unmanned plane can be indicated with kinetic energy formula, with flying for each time slot of unmanned plane
Scanning frequency rate is directly proportional, and the calculating energy of unmanned plane is directly proportional to the task amount that user uploads, it is desirable that in an airborne period
The flight energy of unmanned plane and the summation for calculating energy consumption are less than the energy content of battery of unmanned plane.
As a result, in order to maximize the total handling capacity of unmanned plane, we are in the limitation and nothing for considering user's minimum discharging quantity
Under the premise of man-machine energy content of battery limitation, Lai Youhua user-association variable and unmanned plane path.
Detailed description of the invention
Fig. 1 is algorithm flow schematic diagram.
Fig. 2 is path optimization's algorithm simulating result figure of the UAV system based on edge calculations.
Fig. 3 is the handling capacity of unmanned plane path planning algorithm proposed by the invention and the comparison diagram of other mechanism.
Specific embodiment
First stage:
The method includes: firstly, initializing unmanned plane during flying paths, in the limitation of user's minimum throughout and the unmanned plane energy content of battery
Limitation under total handling capacity maximized by optimization user-association this binary variable.The problem is an integer programming
The energy consumption of each time slot of the knapsack problem of type, i.e. unmanned plane is equivalent to article volume, and uploading task amount is Item Value, should
How problem plans optimal unmanned plane energy using obtaining high efficiency.In order to solve this integer programming problem,
The present invention first relaxes integer variable for continuous variable, and thus problem becomes a simple linear programming problem.Solve this
One continuous variable linear programming problem carries out the approximation that rounds up to obtained solution, finally obtains the binary system for needing to optimize
Associated variable.
Second stage:
Handy family associated variable acquired on last stage is brought into former problem to optimize the flight path of unmanned plane.It is bringing into
After user-association variable, due to optimization aim equation, the nonconvex property of minimum user throughput and energy limit function, the problem
Become a non-convex optimization problem.Firstly for target equation and handling capacity equation is minimized, the present invention passes through Taylor expansion
Mode using its lower bound come approximate substitution.And for calculate energy, the present invention by way of the continuous approximation of the Ritz Li Pu come
Find its upper bound and approximate substitution.Then these approximate functions are brought into former problem, then for unmanned plane path optimization the problem of
Become a convex optimization problem, the present invention optimizes unmanned plane path by interior point method to maximize total handling capacity.
Phase III:
Two above stage etch is repeated, user-association variable is constantly optimized by fixed unmanned plane path, it is then solid
User-association variable is determined to optimize unmanned plane path, and final until required throughput value is stablized, (i.e. the difference of two suboptimization is exhausted
Set threshold value is less than to value), the unmanned plane user-association variable and unmanned plane during flying track optimized at this time is
Required result.
Fourth stage:
It is emulated according to optimization algorithm above with matlab, draws out unmanned plane during flying trajectory diagram and final handling capacity
The figure compared with other mechanism.
Fig. 2 is path optimization's algorithm simulating result figure of the UAV system based on edge calculations, it can be seen from the figure that
As the flight path that the unmanned plane energy content of battery constantly increases unmanned plane constantly expands, user is become closer to, can finally be arrived
Make the uploading rate between user and unmanned plane maximum up to the pros side of user.And when energy is less, unmanned plane can be preferential
Nearest user service is selected, and path can be relatively close to the several users being closer.It can thus be seen that institute of the present invention
Thus the unmanned plane optimization algorithm of design can make rational planning for unmanned plane path well according to the existing energy content of battery situation of unmanned plane
So that efficiency is maximum.
Fig. 3 is the handling capacity result of unmanned plane path planning algorithm proposed by the invention final optimization pass at different frequencies
With the comparison diagram of other mechanism.It can read a book from figure, optimization algorithm proposed by the invention is compared to other mechanism identical
Under the conditions of unmanned plane throughput hoisting about 1 16.
Claims (4)
1. the unmanned plane paths planning method based on edge calculations characterized by comprising
(1) flight path of unmanned plane is initialized first,
(2) user's QoS requirement and unmanned plane energy limit are considered, problem is converted into integer programming problem, according to
The unmanned plane path fixed is by linear relaxation algorithm optimization user-association variable, so that total throughout is maximum,
(3) the user-association variable that step (2) is optimized is brought into problem, by the method for approximation to function by objective function equation
Convex function is converted to, by interior point method optimization unmanned plane path so that total throughout is maximum,
(4) step (2)-(4) are repeated until acquiring handy family handling capacity obtains Xie Wending.
2. as described in claim 1, it includes calculating the limitation of energy and flight energy that unmanned plane, which obtains energy limit, is fallen into a trap
It is directly proportional with processing data volume to calculate energy, each moment flying speed of flight energy and unmanned plane is related.
3. as described in claim 1, the handling capacity of unmanned plane depends on channel capacity, and channel capacity according to calculation formula and
Unmanned plane and user distance, which obtain, square to be inversely proportional, and unmanned plane can be by improving uploading rate close to user.
4. as described in claim 1, unmanned plane is communicated by the way of TDMA with the communication mode of user, i.e., unmanned plane is each
And user time slot can only establish communication connection with user, and can consider that the position of each time slot unmanned plane is constant, i.e.,
Between transmission rate be constant.
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| CN111479226A (en) * | 2020-05-22 | 2020-07-31 | 南京邮电大学 | Unmanned aerial vehicle communication system and unmanned aerial vehicle communication optimization method based on user track |
| CN111552313A (en) * | 2020-04-29 | 2020-08-18 | 南京理工大学 | Multi-unmanned aerial vehicle path planning method based on edge calculation dynamic task arrival |
| CN112649002A (en) * | 2020-12-11 | 2021-04-13 | 南京邮电大学 | Wild protection unmanned aerial vehicle monitoring system based on edge calculation |
| CN113378369A (en) * | 2021-06-04 | 2021-09-10 | 重庆邮电大学 | Path planning and task scheduling method based on unmanned aerial vehicle calculation unloading |
| CN113507492A (en) * | 2021-05-07 | 2021-10-15 | 东北大学 | Internet of things three-dimensional space intelligent positioning and distributed edge calculation support method |
| CN114020024A (en) * | 2021-11-05 | 2022-02-08 | 南京理工大学 | Unmanned aerial vehicle path planning method based on Monte Carlo tree search |
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| CN111479226B (en) * | 2020-05-22 | 2022-03-11 | 南京邮电大学 | Unmanned aerial vehicle communication system and unmanned aerial vehicle communication optimization method based on user track |
| CN112649002A (en) * | 2020-12-11 | 2021-04-13 | 南京邮电大学 | Wild protection unmanned aerial vehicle monitoring system based on edge calculation |
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| CN113507492A (en) * | 2021-05-07 | 2021-10-15 | 东北大学 | Internet of things three-dimensional space intelligent positioning and distributed edge calculation support method |
| CN113507492B (en) * | 2021-05-07 | 2022-07-05 | 东北大学 | Internet of things three-dimensional space intelligent positioning and distributed edge calculation support method |
| CN113378369A (en) * | 2021-06-04 | 2021-09-10 | 重庆邮电大学 | Path planning and task scheduling method based on unmanned aerial vehicle calculation unloading |
| CN113378369B (en) * | 2021-06-04 | 2023-05-30 | 重庆邮电大学 | Path planning and task scheduling method based on unmanned aerial vehicle calculation unloading |
| CN114020024A (en) * | 2021-11-05 | 2022-02-08 | 南京理工大学 | Unmanned aerial vehicle path planning method based on Monte Carlo tree search |
| CN114020024B (en) * | 2021-11-05 | 2023-03-31 | 南京理工大学 | Unmanned aerial vehicle path planning method based on Monte Carlo tree search |
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Application publication date: 20190823 |