CN116878520B - Unmanned aerial vehicle path planning method - Google Patents

Unmanned aerial vehicle path planning method Download PDF

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CN116878520B
CN116878520B CN202311145706.2A CN202311145706A CN116878520B CN 116878520 B CN116878520 B CN 116878520B CN 202311145706 A CN202311145706 A CN 202311145706A CN 116878520 B CN116878520 B CN 116878520B
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aerial vehicle
unmanned aerial
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CN116878520A (en
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王思野
刘怡伶
赵雪莹
徐文波
司中威
麦吉
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Beijing University of Posts and Telecommunications
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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
    • G01C21/20—Instruments for performing navigational calculations
    • 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 an unmanned aerial vehicle path planning method, which enables the average energy efficiency of an unmanned aerial vehicle assisted by a minimum reconfigurable intelligent reflecting surface among a plurality of receiving nodes to be maximized. According to the invention, an initial preset path is taken as a reference local point, the problem is processed into a problem of limited complexity planning through discretization of service time, the original problem is converted into a convex problem with an optimal solution through reasonable scaling and approximation of a target, the target problem is subjected to solution optimization to obtain a suboptimal solution of the original problem, and an unmanned plane flight path realizing energy efficiency of fair communication is obtained.

Description

Unmanned aerial vehicle path planning method
Technical Field
The invention relates to the field of display communication, in particular to an unmanned aerial vehicle path planning method.
Background
Due to the superior mobility and flexibility of the unmanned aerial vehicle, the space-to-ground channel of the unmanned aerial vehicle can take a line-of-sight link as a leading part, and the unmanned aerial vehicle achieves better channel quality under the condition of reducing the influence of building blocking and multipath effects; however, the limited on-board battery of the unmanned aerial vehicle limits, too high energy consumption will significantly reduce the service time, and therefore energy efficiency maximization needs to be pursued when unmanned aerial vehicle flight path planning is considered.
The conventional unmanned aerial vehicle path planning algorithm generally focuses on maximization of unmanned aerial vehicle communication transmission rate or minimization of flight energy consumption, and the two modes can bring about sharp reduction of energy efficiency for a fixed-wing unmanned aerial vehicle: the former requires the drone to hover as slowly as possible near the transmitter, which can lead to a huge energy consumption, while the latter increases with communication time, the throughput tends to a fixed value but the energy consumption increases linearly with time, so the energy efficiency of the drone will continue to decrease and eventually tends to 0. The unmanned aerial vehicle (Unmanned Aerial Vehicle, UAV) assisted by the reconfigurable intelligent surface (Reconfigurable Intelligent Surface, RIS) has wide application prospect, the cost of the constituent unit of the unmanned aerial vehicle (RIS-UAV) assisted by the reconfigurable intelligent reflection surface is lower, no extra energy consumption is generated compared with the traditional relay mode, no extra interference elimination technology is needed, and the unmanned aerial vehicle is used as a mobile node, can freely move in the air according to communication requirements, can be deployed and evacuated quickly, so that the RIS-UAV system has stronger flexibility and expandability. In addition, the communication distance between the drone and the ground station and the mobile receiving node is relatively close, the transmission power is relatively low, and thus the RIS-UAV system may further reduce the energy consumption of the communication system.
Disclosure of Invention
In order to solve the limitations and defects existing in the prior art, the invention provides an unmanned aerial vehicle path planning method, which comprises the following steps:
s1, determining channel conditions, forming a model according to throughput and energy consumption of an unmanned aerial vehicle relay system, and determining unmanned aerial vehicle energy efficiency expression;
s2, obtaining constraint conditions of the flight path of the unmanned aerial vehicle within service time;
s3, initializing unmanned aerial vehicle service scheduling, flight path local points and flight speed local points, wherein the flight path local points comprise horizontal path local points and flight height local points;
s4, determining the optimal phase shift of the reconfigurable intelligent reflecting surface according to the unmanned aerial vehicle service schedule, the number of structural units of the reconfigurable intelligent reflecting surface and the number of receiving nodes;
s5, obtaining the unmanned aerial vehicle service schedule by solving a linear programming problem according to the local points and the phase shifts of the flight paths, and updating the result into a new unmanned aerial vehicle service schedule;
s6, optimizing a horizontal path by solving a convex problem according to the local points of the flight path, the phase shift and the unmanned aerial vehicle service schedule, and marking the result as a new local point of the horizontal path;
s7, optimizing the flight altitude by solving the convex problem according to the flight path local points, the phase shift and the unmanned aerial vehicle service schedule, and marking the result as a new flight altitude local point;
s8, judging whether the change of the energy efficiency is smaller than a preset threshold value or not;
if the judging result is that the change of the energy efficiency is larger than a preset threshold value, executing step S4; if the judgment result is that the change of the energy efficiency is smaller than the preset threshold value, executing step S9;
and step S9, outputting the optimal phase shift, the unmanned aerial vehicle service schedule, the horizontal path local point and the flying height local point.
Optionally, the channels include a double-side line-of-sight channel and a single-side line-of-sight channel, and the energy efficiency of the unmanned aerial vehicle is expressed as a minimum value of average energy efficiency of communication between the unmanned aerial vehicle and each receiving node within a preset communication time, and the minimum value is used for comprehensively distributing communication resources of the multi-node system.
Optionally, the constraint conditions of the flight path of the unmanned aerial vehicle include flight start point coordinates, flight end point coordinates, flight altitude, flight speed, maximum flight altitude, minimum flight altitude, maximum flight speed, minimum flight speed and maximum flight acceleration;
all the constraint conditions of the speed are that the speed is decomposed into component constraint in the horizontal direction and component constraint in the vertical direction;
all constraints on the acceleration are to decompose the acceleration into a component constraint in the horizontal direction and a component constraint in the vertical direction.
Optionally, the step of step S4 includes:
an optimal phase shift of the reconfigurable intelligent reflective surface is determined by maximizing the channel gain.
Optionally, the step S6 includes:
performing discretization on the horizontal path target type;
performing convex approximation processing on the processed horizontal path target type and the constraint condition of the flight path of the unmanned aerial vehicle at the local point of the flight path;
solving the horizontal path target problem after processing, and updating the result to be the horizontal path local point of the next iteration.
Optionally, the step S7 includes:
discretizing the flying height target type;
performing convex approximation processing on the processed flying height target type and the constraint condition of the flying path of the unmanned aerial vehicle at the local point of the flying path;
and solving the problem of the flying height target after processing, and updating the result to be the flying height local point of the next iteration.
Optionally, the communication time is discretized intoNEach time slotδ t =T /N，And (3) withk th Receiving node atn th The expression of the channel gain of the time slot is as follows:
wherein the unmanned aerial vehicle is provided withMA uniform linear array of individual reflective elements,is the firstiThe array units are at the firstnThe phase shift of the individual time slots, the phase shift matrix being defined as +.>，λIs the wavelength of the communication carrier, ">Is at a reference distanceD=1mThe channel power gain coefficient at which,for the distance between the reconfigurable intelligent reflective surface assisted drone and the ground base station,λrepresenting the carrier wavelength(s),dindicating antenna spacing +.>A cosine representing the angle of arrival of the unmanned aerial vehicle assisted from the reconfigurable intelligent reflective surface with the ground base station,/->Representation of the reconfigurable intelligent reflective surface assisted drone to thkThe distance of the individual moving vehicles is such that,representation of the reconfigurable intelligent reflective surface assisted drone to thkA cosine of the angle of arrival of the individual moving vehicles;
when the phase shift complex value of the different reflecting elements is zero, the channel gain reaches a maximum value, andk th receiving node atn th The expression for the optimal phase shift for a slot is as follows:
wherein,representing scheduling(s)>Representing that the reconfigurable intelligent reflecting surface assisted unmanned aerial vehicle is in time slotnServing ofk th The vehicle is moved.
The invention has the following beneficial effects:
the invention provides an unmanned aerial vehicle path planning method, which enables the average energy efficiency of an unmanned aerial vehicle assisted by a minimum reconfigurable intelligent reflecting surface among a plurality of receiving nodes to be maximized. According to the invention, an initial preset path is taken as a reference local point, the problem is processed into a problem of limited complexity planning through discretization of service time, the original problem is converted into a convex problem with an optimal solution through reasonable scaling and approximation of a target, the target problem is subjected to solution optimization to obtain a suboptimal solution of the original problem, and an unmanned plane flight path realizing energy efficiency of fair communication is obtained.
The unmanned aerial vehicle path planning method provided by the invention pays attention to fair communication of a multi-node system, introduces auxiliary relay communication of a reconfigurable intelligent reflecting surface, determines flight constraint and preset channel conditions before flight, determines optimal phase shift according to the number of structural units of the reconfigurable intelligent reflecting surface and the flight path, and obtains optimal channel conditions; determining service scheduling of the multi-node system on the premise of fair communication according to the flight track and the optimal phase shift; and determining the horizontal flight path and the flight height of the unmanned aerial vehicle with the maximum energy efficiency step by step according to the obtained optimal phase shift and scheduling. According to the unmanned aerial vehicle path planning method provided by the invention, the information transmission rate and the flight energy consumption of unmanned aerial vehicle communication are comprehensively considered, and compared with a traditional transmission rate maximization scheme, the flight energy consumption is further reduced; compared with the traditional scheme only considering energy consumption, the communication quality is further improved. .
Drawings
Fig. 1 is a schematic diagram of a communication system of an unmanned aerial vehicle according to an embodiment of the present invention.
Fig. 2 is a flowchart of a method for planning a path of an unmanned aerial vehicle according to an embodiment of the present invention.
Detailed Description
In order to enable those skilled in the art to better understand the technical scheme of the invention, the unmanned aerial vehicle path planning method provided by the invention is described in detail below with reference to the accompanying drawings.
Example 1
Compared with a ground base station system, unmanned aerial vehicle communication needs to additionally consider energy consumption generated by flight, so unmanned aerial vehicle communication time is limited by the capacity of an onboard battery, and the energy efficiency problem is worth focusing on. In the existing fixed-wing unmanned aerial vehicle path planning algorithm, the problem of maximizing information throughput, minimizing flight energy consumption or shortest path of a fixed-wing unmanned aerial vehicle communication system is generally focused on. However, path planning with only a single index as a goal cannot balance transmission rate and energy consumption priority. Considering only the maximum transmission rate results in a significant increase in the fixed wing drone energy consumption, versus only the minimum energy consumption which sacrifices the drone communication channel quality.
The reconfigurable intelligent reflection surface structure unit has lower cost, does not generate extra energy consumption and does not need extra interference elimination technology compared with the traditional relay mode, and the energy efficiency of the unmanned aerial vehicle relay system can be further improved by taking the reconfigurable intelligent surface (Reconfigurable Intelligent Surface, RIS) as a passive relay.
The embodiment provides an unmanned aerial vehicle path planning method, which introduces an unmanned aerial vehicle (RIS-UAV) assisted by a reconfigurable intelligent reflecting surface, and can realize the maximization of the relay energy efficiency of the unmanned aerial vehicle while guaranteeing fair communication in a multi-node system.
The embodiment provides a fixed wing unmanned aerial vehicle path planning method for auxiliary communication of a reconfigurable intelligent reflecting surface, and aims at maximizing the minimum RIS-UAV average energy efficiency among a plurality of receiving nodes. In the embodiment, an initial preset path is used as a reference local point, and the problem is processed into a limited complexity planning problem by discretizing service time; the target problem is converted into a convex problem with an optimal solution by reasonably scaling and approaching the target problem, the target problem is subjected to solution optimization to obtain a suboptimal solution of the original problem, and an unmanned plane flight path realizing energy efficiency of fair communication is obtained.
The present embodiment determines channel conditions, models the relay system throughput and energy consumption, and determines energy efficiency expressions. In the embodiment, the constraint on the flight path of the unmanned aerial vehicle is determined in the service time. The present embodiment initializes service schedules, flight path local points (including horizontal paths and altitudes), flight speed local points.
The present embodiment repeats the following steps until the energy efficiency change is less than the threshold:
determining the optimal phase shift of the reconfigurable intelligent reflecting surface according to unmanned aerial vehicle service scheduling, the number of reconfigurable intelligent reflecting surface structural units and the number of receiving nodes;
according to the phase shift and the flight path local points, acquiring unmanned aerial vehicle service scheduling by solving the linear programming problem, and updating the result into new scheduling;
according to the phase shift, the scheduling and the flight path local points, optimizing the horizontal flight path by solving the convex problem, and marking the optimizing result as a new horizontal flight path local point;
and optimizing the flying height by solving the convex problem according to the phase shift, the scheduling and the path local points, and marking the optimizing result as a new flying height local point.
Specifically, the above channel modeling is generally considered as the following: receiving and transmitting a double-side line-of-sight channel and a single-side line-of-sight channel (a channel between the unmanned aerial vehicle and the transmitter, and a channel between the unmanned aerial vehicle and the receiver), wherein one is a probability line-of-sight channel, and the other is a line-of-sight channel. Further, unmanned energy efficiency is expressed as a minimum value of average energy efficiency of communication with each receiving node in a communication time to comprehensively consider fair communication of the multi-node system. Such constraints on unmanned aerial vehicle flight include, but are not limited to: coordinates of a flight start point and a flight end point, flight height and flight speed; determining maximum and minimum flying heights, maximum and minimum flying speeds and maximum flying accelerations; all the constraints of the speed and the acceleration are constraints that the speed and the acceleration are decomposed into components in the horizontal direction and the vertical direction. The reconfigurable intelligent surface (Reconfigurable Intelligent Surface, RIS) phase shift determination is determined by maximizing channel gain, wherein the line-of-sight probability is independent of the result, and negligible effect is treated as line-of-sight channel condition processing; the unmanned aerial vehicle path planning method further comprises the following steps: 1) Discretizing the target type; 2) Performing convex approximation processing on the target and constraint conditions at local points; 3) Solving the processed target problem, and updating the result to be the next round of iterative local points.
According to the unmanned aerial vehicle path planning method, fair communication of a multi-node system is concerned, auxiliary relay communication of a reconfigurable intelligent reflecting surface is introduced, flight constraint and preset channel conditions are determined before flight, and optimal phase shift is determined according to the number of structural units of the reconfigurable intelligent reflecting surface and the flight path, so that optimal channel conditions are obtained; determining service scheduling of the multi-node system on the premise of fair communication according to the flight track and the optimal phase shift; and determining the horizontal flight path and the flight height of the unmanned aerial vehicle with the maximum energy efficiency step by step according to the obtained optimal phase shift and scheduling. According to the unmanned aerial vehicle path planning method provided by the embodiment, the information transmission rate and the flight energy consumption of unmanned aerial vehicle communication are comprehensively considered, and compared with a traditional transmission rate maximization scheme, the flight energy consumption is further reduced; compared with the traditional scheme only considering energy consumption, the communication quality is further improved.
The present embodiment makes the following settings: initial positionq 0 Final positionq f Communication timeTDiscrete point numberNHorizontal initial velocityv 0 Horizontal final speedv f Initial vertical velocityv h0 Final vertical velocityv hf Minimum vertical velocityV hmin Maximum vertical velocityV hmax Maximum vertical accelerationa hmax Maximum height limitH max Minimum height limitH min Minimum horizontal velocityV min Maximum horizontal velocityV max Maximum horizontal accelerationa max Ground base station coordinatesb 0 The kth mobile receiving node coordinates are. In addition, the unmanned aerial vehicle-transmitter channel is a B-R channel, the unmanned aerial vehicle-receiver channel is an R-V channel, the R-V channel is a probability line-of-sight channel, and the B-R channel is a unilateral line-of-sight channel of the line-of-sight channel.
The present embodiment assumes that the Doppler effect due to unmanned movement is perfectly compensated and the communication time is discretized intoNEach time slotδ t =T /N. The channel gains of the B-R link and the R-V link with the kth receiving node can be expressed as follows, whereThe loss coefficients for non-line-of-sight channels are as follows:
the present embodiment gives a fixed heighth j In the case of (a), the energy efficiency of communication with the kth receiving node is defined as follows:
wherein,for the distance of the kth Moving Vehicle (MV) to the RIS-UAV,d kg is ground distance->For the elevation angle of the drone to the MV,c 1 andc 2 is a constant delta related to factors such as air density, wing area, unmanned aerial vehicle quality, etc K Is the amount of change in kinetic energy.
The embodiment is based on a fixed trackqSchedulingA k The RIS phase shift is determined. Since the phase shift problem is independent of the line-of-sight probability at a fixed flight altitude, the line-of-sight probability effect is ignored and the double line-of-sight channel condition is analogized for processing. And (3) withk th Receiving node atn th The channel gain of a slot is expressed as follows:
wherein,is the elevation angle cosine of the kth MV of the RIS-UAV, the ground base station and the mobile receiving node. It is readily observed that the overall channel gain can be maximized when the phase shift complex values of the different reflective elements are zero. Thus, is connected withk th Receiving node atn th The optimal phase shift for a slot is as follows:
the embodiment is based on a fixed trackqRIS phase shift determination schedulingA k . To obtain scheduling, the presentExamples use is made ofr lb Definition of the definitionR k The new constraint is thus as follows:
binary variableRelaxation to->Continuous variable within. The schedule may then be determined by solving the following problem given the local trajectory and phase shift, and the rounding method may be used to arrive at an integer solution as follows:
the present embodiment is based on fixed schedulingA k RIS phase shift determination trajectoryq. Under the condition of fixed height, continuous convex approximation is carried out by introducing a relaxation variable and utilizing Taylor first-order expansion of an objective function at a local point, so that the non-convex problem and the non-convex constraint are converted into convex. Introduction oft n To relaxIntroducing a relaxation variable->And->To relax->Andwherein->And->，/>For the euclidean distance between the RIS-UAV and the base station,is the ground projected distance between the RIS-UAV and the mobile receiving node. For non-convex constraint and objective function, at local point +.>、/>、/>Performing a taylor first order expansion approximation can result in an equivalent but convex problem as follows:
wherein,
the present embodiment is based on fixed schedulingA k RIS phase shift, horizontal trajectoryqThe fly height is determined. The present embodiment obtains a horizontal pathqAnd (3) the、/>For flying height based on fixed horizontal pathhAnd (5) optimizing. Sequential convex optimization and successive convex approximation of non-convex constraints, non-convex objective functions (similar to the previousIntroduction oft h To relax->And the objective function molecule is at the local point +.>、/>Performing first-order Taylor expansion and optimizing the lower limit to obtain an equivalent convex optimization problem, wherein the equivalent convex optimization problem is expressed as follows:
after determining the 2D path, the elevation angle is expressed asThe minimum throughput between the receiving nodes is as follows:
the unmanned aerial vehicle path planning that this embodiment provided is as follows:
step S101, initializing local pointsIteration number->；
Step S102, calculating local points；
Step S103, determining an optimal phase shift；
Step S104, for local pointsSolving P1 to obtain a schedule, and recording the result as；
Step S105, for local pointsSolving for P2 and noting the result as；
Step S106, for local pointsSolving for P3 and noting the result as +.>；
Step S107, update；
Repeating steps S103 to S107 until the energy efficiency increment is less than the thresholdε。
In the present embodiment of the present invention,Mfor the number of reflection units,Nin order to be the number of time slots,is the firstiThe array units are at the firstnPhase shift of individual time slots>In order to phase shift the matrix of the phase shift,λfor the wavelength of the communication carrier wave,ρfor reference distanceD=1mThe channel power gain coefficient at which,dfor antenna spacing, phi is the phase shift,b 0 is the coordinates of the base station on the ground,Kin order to move the number of vehicles,indicate->The vehicle moving the vehicle at the firstnThe location of the time slot of each time slot,representation of reconfigurable intelligent reflective surface assisted unmanned aerial vehicle on the firstnThe position of the time slots.
The embodiment provides a unmanned aerial vehicle path planning method, which enables the average energy efficiency of the unmanned aerial vehicle assisted by the minimum reconfigurable intelligent reflecting surface among a plurality of receiving nodes to be maximized. According to the embodiment, an initial preset path is used as a reference local point, the problem is processed into a problem of limited complexity planning through discretization of service time, the original problem is converted into a convex problem with an optimal solution through reasonable scaling and approximation of a target problem, the target problem is subjected to solution optimization to obtain a suboptimal solution of the original problem, and an unmanned plane flight path realizing energy efficiency of fair communication is obtained.
It is to be understood that the above embodiments are merely illustrative of the application of the principles of the present invention, but not in limitation thereof. Various modifications and improvements may be made by those skilled in the art without departing from the spirit and substance of the invention, and are also considered to be within the scope of the invention.

Claims (3)

1. A method for unmanned aerial vehicle path planning, comprising:
s1, determining channel conditions, forming a model according to throughput and energy consumption of an unmanned aerial vehicle relay system, and determining unmanned aerial vehicle energy efficiency expression;
s2, obtaining constraint conditions of the flight path of the unmanned aerial vehicle within service time;
s3, initializing unmanned aerial vehicle service scheduling, flight path local points and flight speed local points, wherein the flight path local points comprise horizontal path local points and flight height local points;
s4, determining the optimal phase shift of the reconfigurable intelligent reflecting surface according to the unmanned aerial vehicle service schedule, the number of structural units of the reconfigurable intelligent reflecting surface and the number of receiving nodes;
s5, obtaining the unmanned aerial vehicle service schedule by solving a linear programming problem according to the flight path local point, the flight speed local point, the unmanned aerial vehicle service schedule and the optimal phase shift, and updating the result into a new unmanned aerial vehicle service schedule;
s6, optimizing a horizontal path by solving a convex problem according to the flight path local point, the flight speed local point, the optimal phase shift and the new unmanned aerial vehicle service schedule, and marking the result as a new horizontal path local point;
s7, optimizing the flying height by solving the convex problem according to the new horizontal path local point, the optimal phase shift and the new unmanned aerial vehicle service schedule, and marking the result as a new flying height local point;
s8, judging whether the change of the energy efficiency is smaller than a preset threshold value or not;
if the judging result is that the change of the energy efficiency is larger than a preset threshold value, executing step S4; if the judgment result is that the change of the energy efficiency is smaller than the preset threshold value, executing step S9;
step S9, outputting the optimal phase shift, the new unmanned aerial vehicle service schedule, the new horizontal path local point and the new flying height local point;
the channels include a double-sided line-of-sight channel and a single-sided line-of-sight channel, the unmanned aerial vehicle energy efficiency is expressed as a minimum value of average energy efficiency of communication between the unmanned aerial vehicle and each receiving node within a preset communication time, the method is used for comprehensively distributing communication resources of a multi-node system, wherein an unmanned plane-transmitter channel is a B-R channel, an unmanned plane-receiver channel is an R-V channel, the R-V channel is a probability line-of-sight channel, and the B-R channel is a unilateral line-of-sight channel of the line-of-sight channel;
the constraint conditions of the flight path of the unmanned aerial vehicle comprise flight starting point coordinates, flight ending point coordinates, flight height, flight speed, maximum flight height, minimum flight height, maximum flight speed, minimum flight speed and maximum flight acceleration;
all the constraint conditions of the speed are that the speed is decomposed into component constraint in the horizontal direction and component constraint in the vertical direction;
all constraint conditions of the acceleration are that the acceleration is decomposed into component constraint in the horizontal direction and component constraint in the vertical direction;
performing discretization on the horizontal path target type;
performing convex approximation processing on the processed horizontal path target type and the constraint condition of the flight path of the unmanned aerial vehicle at the local point of the flight path;
solving the horizontal path target problem after processing, and updating the result to be the horizontal path local point of the next iteration;
discretizing the flying height target type;
performing convex approximation processing on the processed flying height target type and the constraint condition of the flying path of the unmanned aerial vehicle at the local point of the flying path;
and solving the problem of the flying height target after processing, and updating the result to be the flying height local point of the next iteration.
2. The unmanned aerial vehicle path planning method of claim 1, wherein step S4 comprises:
an optimal phase shift of the reconfigurable intelligent reflective surface is determined by maximizing the channel gain.
3. The unmanned aerial vehicle path planning method of claim 2, wherein the communication time is discretized intoNEach time slotδ t = T/ N，With the kth receiving node at the kthnThe expression of the channel gain for each slot is as follows:，
wherein the unmanned aerial vehicle is provided withMA uniform linear array of individual reflective elements,is the firstiThe array units are at the firstnThe phase shift of the time slot with the kth receiving node, the phase shift matrix being defined as，λIs the wavelength of the communication carrier, ">Is at a reference distanceD=1mChannel power gain coefficient at->For the distance between the reconfigurable intelligent reflective surface assisted drone and the ground base station,dindicating the antenna separation, phi is the phase shift,b 0 is the coordinates of the base station on the ground,indicating that the kth moving vehicle is at the kthnThe location of the time slot of each time slot,representation of reconfigurable intelligent reflective surface assisted unmanned aerial vehicle on the firstnThe position of the time slots>A cosine representing the angle of arrival of the reconfigurable intelligent reflecting surface-assisted drone to the ground base station, +.>Representation of the reconfigurable intelligent reflective surface assisted drone to thkDistance of individual moving vehicles,/->Representation of the reconfigurable intelligent reflective surface assisted drone to thkPersonal mobile vehicleIs equal to the chord of the arrival angle;
when the phase shift complex value of the different reflecting elements is zero, the channel gain reaches the maximum value and is at the kth receiving nodenThe expression for the optimal phase shift for each slot is as follows:
，
wherein,representing scheduling(s)>Representing that the reconfigurable intelligent reflecting surface assisted unmanned aerial vehicle is in time slotnServing ofk th The moving vehicles, K, are the number of moving vehicles.
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