Unmanned aerial vehicle trajectory optimization method and apparatus, electronic device, and storage medium

Abstract

The present disclosure relates to an unmanned aerial vehicle trajectory optimization method and apparatus, an electronic device, and a storage medium. The method comprises: obtaining an energy consumption model based on an intelligent reflecting surface during communication between a target unmanned aerial vehicle and a terminal; obtaining an initial flight trajectory of the target unmanned aerial vehicle; and optimizing the initial flight trajectory by means of the energy consumption model to obtain a target flight trajectory. According to the present disclosure, the energy consumption model during communication between the target unmanned aerial vehicle and the terminal is established under the intelligent reflecting surface, and the initial flight trajectory is optimized by means of the energy consumption model, so that the energy consumption of the target unmanned aerial vehicle during flight and communication can be reduced, and the communication quality of the target unmanned aerial vehicle can be improved by means of the intelligent reflecting surface.

Description

UAV trajectory optimization method, device, electronic device and storage medium

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This disclosure is based on and claims the priority of Chinese patent application with application number 202311531604.4 and application date November 16, 2023. The entire content of the Chinese patent application is hereby incorporated into this disclosure as a reference. Technical Field

[0003] The present disclosure relates to the field of communication technology, and in particular to a method, device, electronic device, and storage medium for optimizing the trajectory of an unmanned aerial vehicle (UAV). Background Art

[0004] With the continuous development of technology, drone types have become more diverse, able to meet a variety of needs, making drones more widely used. Because drones are not constrained by the ground, in some complex environments, wireless communication between low-altitude platforms and drones is usually carried out using high-frequency radio waves such as millimeter waves.

[0005] However, since the path loss of high-frequency radio waves such as millimeter waves is very large and they are easily blocked by obstacles, in some complex urban scenarios, when the line-of-sight link is blocked, it will seriously affect the communication link quality of the drone, resulting in poor communication quality of the drone.

[0006] Summary of the Invention

[0007] The present disclosure provides a method, device, electronic device and storage medium for optimizing the trajectory of a drone.

[0008] According to a first aspect of the present disclosure, a method for optimizing a UAV trajectory is provided, the method comprising: obtaining an energy consumption model based on an intelligent reflective surface when a target UAV communicates with a terminal; obtaining an initial flight trajectory of the target UAV; and optimizing the initial flight trajectory using the energy consumption model to obtain a target flight trajectory.

[0009] Optionally, optimizing the initial flight trajectory using the energy consumption model includes: solving the energy consumption model to obtain a solution result; and optimizing the initial flight trajectory based on the solution result.

[0010] Optionally, the method further includes: obtaining transmission parameters between the target UAV and the terminal and a backscatter coefficient of the smart reflective surface; and generating a target flight trajectory based on the transmission parameters, the backscatter coefficient and the optimized initial flight trajectory.

[0011] Optionally, the optimized initial flight trajectory is used as the first flight trajectory; generating a target flight trajectory based on the transmission parameters, the backscatter coefficient and the optimized initial flight trajectory includes: optimizing the transmission parameters and the first flight trajectory based on the backscatter coefficient to obtain target transmission parameters; and obtaining a target flight trajectory based on the target transmission parameters, the backscatter coefficient and the first flight trajectory.

[0012] Optionally, the optimized initial flight trajectory is used as the first flight trajectory; generating a target flight trajectory based on the transmission parameters, the backscatter coefficient and the optimized initial flight trajectory includes: optimizing the backscatter coefficient based on the first flight trajectory and the transmission parameters to obtain a target backscatter coefficient; and obtaining a target flight trajectory based on the target backscatter coefficient, the transmission parameters and the first flight trajectory.

[0013] Optionally, the backscatter coefficient is optimized based on the first flight trajectory and the transmission parameters, including: initializing the first flight trajectory and the transmission parameters respectively; and uniformly quantizing the phase of the smart reflective surface based on the initialized first flight trajectory and the transmission parameters to obtain a discrete phase shift value of the backscatter coefficient.

[0014] Optionally, the method also includes: obtaining target constraint conditions, the target constraint conditions including at least one of the following: power constraint of the target UAV, phase offset constraint of the smart reflective surface, amplitude reflection coefficient constraint of the smart reflective surface, mission constraint and speed constraint; establishing the energy consumption model based on the target constraint conditions.

[0015] According to a second aspect of the present disclosure, a drone trajectory optimization device is provided, comprising: a model acquisition module for acquiring an energy consumption model when a target drone based on an intelligent reflective surface communicates with a terminal; an initial flight trajectory acquisition module for acquiring an initial flight trajectory of the target drone; and a target flight trajectory acquisition module for optimizing the initial flight trajectory using the energy consumption model to obtain a target flight trajectory.

[0016] Optionally, the target flight trajectory acquisition module is specifically used to: solve the energy consumption model to obtain a solution result; and optimize the initial flight trajectory based on the solution result.

[0017] Optionally, the device also includes: a data acquisition module for acquiring the transmission parameters between the target UAV and the terminal and the backscatter coefficient of the intelligent reflective surface; and a trajectory optimization module for generating a target flight trajectory based on the transmission parameters, the backscatter coefficient and the optimized initial flight trajectory.

[0018] Optionally, the optimized initial flight trajectory is used as the first flight trajectory; the trajectory optimization module is specifically used to: optimize the transmission parameters and the first flight trajectory based on the backscatter coefficient to obtain target transmission parameters; and obtain the target flight trajectory based on the target transmission parameters, the backscatter coefficient and the first flight trajectory.

[0019] Optionally, the optimized initial flight trajectory is used as the first flight trajectory; the trajectory optimization module is specifically used to: optimize the backscatter coefficient based on the first flight trajectory and the transmission parameters to obtain the target backscatter coefficient; and obtain the target flight trajectory based on the target backscatter coefficient, the transmission parameters and the first flight trajectory.

[0020] Optionally, the trajectory optimization module is specifically used to: initialize the first flight trajectory and the transmission parameters respectively; and uniformly quantize the phase of the smart reflecting surface based on the initialized first flight trajectory and the transmission parameters to obtain a discrete phase shift value of the backscattering coefficient.

[0021] Optionally, the device also includes a model building module, which is specifically used to: obtain target constraint conditions, the target constraint conditions including at least one of the following: power constraint of the target UAV, phase offset constraint of the smart reflective surface, amplitude reflection coefficient constraint of the smart reflective surface, mission constraint and speed constraint; and establish the energy consumption model based on the target constraint conditions.

[0022] According to a third aspect of the present disclosure, an electronic device is provided, comprising: a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the above method when executing the program.

[0023] According to a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above method of the present disclosure is implemented.

[0024] The drone trajectory optimization method, device, electronic device, and storage medium provided by the disclosed embodiments obtain an energy consumption model for communication between the target drone and a terminal under a smart reflective surface, obtain the target drone's initial flight trajectory, and optimize the initial flight trajectory using the energy consumption model to obtain the target flight trajectory. By establishing an energy consumption model for communication between the target drone and the terminal under a smart reflective surface and optimizing the initial flight trajectory using the energy consumption model, the target drone's energy consumption during flight and communication can be reduced, and the smart reflective surface can also be used to improve the target drone's communication quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Further details, features and advantages of the present disclosure are disclosed in the following description of exemplary embodiments in conjunction with the accompanying drawings, in which:

[0026] FIG1 is a schematic diagram of a scenario provided by an exemplary embodiment of the present disclosure;

[0027] FIG2 is a flow chart of a method for optimizing a UAV trajectory according to an exemplary embodiment of the present disclosure;

[0028] FIG3 is a structural block diagram of a UAV trajectory optimization device provided by an exemplary embodiment of the present disclosure;

[0029] FIG4 is a structural block diagram of an electronic device provided by an exemplary embodiment of the present disclosure;

[0030] FIG5 is a structural block diagram of a computer system provided by an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0031] The following describes embodiments of the present disclosure in more detail with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as limited to the embodiments described herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of protection of the present disclosure.

[0032] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0033] The term "including" and its variations used in this document are open inclusions, that is, "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one other embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the description below. It should be noted that the concepts of "first", "second", etc. mentioned in this disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0034] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, they should be understood as "one or more".

[0035] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0036] It is understandable that before using the technical solutions disclosed in the various embodiments of this disclosure, the type, scope of use, usage scenarios, etc. of the personal information involved in this disclosure should be informed to the user and the user's authorization should be obtained in an appropriate manner in accordance with relevant laws and regulations.

[0037] For example, in response to a user's active request, a prompt message is sent to the user to clearly inform the user that the operation requested will require the acquisition and use of the user's personal information. This allows the user to independently choose whether to provide personal information to the electronic device, application, server, storage medium, or other software or hardware that performs the operations of the disclosed technical solution based on the prompt message.

[0038] As an optional but non-limiting implementation method, in response to receiving the user's active request, the method of sending a prompt message to the user can be, for example, a pop-up window, and the prompt message can be presented in the form of text in the pop-up window. In addition, the pop-up window can also carry a selection control for the user to choose "agree" or "disagree" to provide personal information to the electronic device. It is understandable that the above notification and the process of obtaining user authorization are only illustrative and do not constitute a limitation on the implementation method of the present disclosure. Other methods that meet relevant laws and regulations can also be applied to the implementation method of the present disclosure.

[0039] As drones gain popularity, they are finding widespread application across various industries. Because drones are unconstrained by the ground, wireless communications using low-altitude platforms is a highly convenient method in complex environments. However, due to the significant path loss of higher-frequency radio waves, such as millimeter waves, and their susceptibility to obstruction by obstacles, drone communication is not ideal in complex urban environments, such as those where line-of-sight links are blocked. Consequently, drone communication link quality can be poor in scenarios where line-of-sight links are blocked.

[0040] To solve the above problems, the embodiments of the present disclosure introduce smart reflective surfaces into the communication process of the drone to assist the drone's communication, thereby solving the problem of poor drone communication link quality in scenarios where the line-of-sight link is blocked.

[0041] In an embodiment of the present disclosure, for example, there are N ground users in a target area, and the N ground users may be N terminals, each of which is used to communicate with a drone. An intelligent reflective surface is installed in the target area. When the drone is flying in the target area and communicating with a ground user terminal, the drone will send a transmission signal to the ground user terminal, and the transmission signal will be reflected by the intelligent reflective surface. Therefore, while the ground user terminal receives the transmission information sent by the drone, it can also receive the transmission signal reflected by the intelligent reflective surface. After the transmission signal emitted by the drone is reflected on the intelligent reflective panel, the phase and amplitude of the signal will change. In addition, in an embodiment of the present disclosure, the phase and amplitude of the reflected signal can also be adjusted by adjusting the backscatter coefficient of each reflective unit on the intelligent reflective surface. Wherein, N is a positive integer.

[0042] Therefore, the transmission signals sent by the drone and the transmission signals reflected by the smart reflective surface can provide relevant services to user terminals in the target area.

[0043] It should be noted that the drone in the embodiments of this disclosure is an aircraft that can be controlled using its own software or a wireless remote control device. Due to its simple deployment and high maneuverability, drones have found widespread application in civilian and other fields. The smart reflective surface in the embodiments of this disclosure is a plane with a large number of passive reflective elements. By adjusting each independent element, the incident signal produces a controllable amplitude or phase change, thereby mitigating wireless channel fading and interference issues.

[0044] Specifically, the structure of the smart reflector mainly includes three sublayers and a controller: the outermost layer can be composed of numerous reflective units printed on a dielectric substrate, which directly interacts with and reflects the incident signal; the second layer can be designed as a copper plate or other metal plate, the main function of which is to prevent the signal from penetrating the reflective surface and causing signal attenuation; the third layer is a control circuit board. Through the controller, the values ​​of the capacitors, resistors, and inductors in all the reflective units of the smart reflector can be independently adjusted to adjust the amplitude or phase of the reflected signal; the controller can be an FPGA (Field-Programmable Gate Array), which can control the backscatter coefficient (including amplitude and phase) of each reflective unit. In addition, PIN diodes can be used in the smart reflector structure, with one PIN diode connected to each reflective unit. To enable the PIN diode to switch between the "on" and "off" states in the equivalent circuit, the diode bias voltage can be controlled through the DC feed line to generate a phase shift difference. Therefore, by using FPGA to set the corresponding bias voltage in the smart reflective surface, the phase shift of each reflective unit in the smart reflective surface can be achieved. On this basis, the controller can be used to jointly adjust multiple (or the entire smart reflective surface) reflective units, thereby realizing beamforming control of the reflected signal to meet the needs of different communication scenarios.

[0045] As shown in Figure 1, during the communication process between the UAV and the user terminal on the ground, it is possible to provide services to multiple user terminals in the target area through the UAV and the intelligent reflective surface. By establishing a three-dimensional coordinate system in the target area, assuming that the flight altitude of the UAV is h, the coordinates of the UAV are P(x, y, h), and the coordinates of the user terminal are D use (x i ,y i ), the coordinates of the smart reflective surface are (x, y, z).

[0046] Based on the Rayleigh fading model, assuming that the link from the UAV to the user terminal is h ud , the link from the UAV to the smart reflective surface is h ur , the link from the smart reflector to the user terminal is h ru In the embodiment of the present disclosure, the reflection matrix coefficient of the smart reflection surface can be defined as The β intelligent reflective surface is the amplitude adjustment of a certain reflective unit, β N ∈(0,1], θ is the phase offset of the smart reflector for a certain reflector unit, θ N ∈[0, 2π). The signal received by the i-th user terminal among N user terminals in a certain interval is:

[0047] Where P is the transmission power of the drone, s is the unit power signal, and n is Gaussian white noise. According to Shannon's formula, the transmission rate of the system can be deduced as:

[0048] Where B is the bandwidth and t is the time.

[0049] In the embodiment of the present disclosure, the UAV flight energy consumption modeling theory is based on the flight energy consumption of helicopters. Considering the effect of speed on power, the flight power can be modeled as:

[0050] Among them, the constants P0 and P i are the blade profile power and induced power in the hovering state, U tip is the blade tip speed, v0 is the rotor induced speed when the UAV is hovering, d0 is the airframe drag ratio, s is the rotor strength, ρ is the air density, A is the rotor disk area, and V is the flight speed. The three terms blade profile, induced, and parasite in Equation (3) represent blade profile power, induced power, and parasitic power, respectively.

[0051] Under the constraints of the drone's flight time, the disclosed embodiment combines the reflection matrix of the smart reflective panel and the drone's flight trajectory to minimize the drone's energy consumption (as represented by the min function below) to perform modeling, thereby obtaining the following model:

[0052] Among them, a in formula (4) i [t] is the transmission coefficient, indicating whether the drone communicates with user i at time t. com is the transmission power, P fly is the flight power. The first constraint in formula (4) (st0<θ i ≤2π) is constraint 1, which is the unit phase offset constraint. The second constraint in equation (4) (0<β n ≤1) is constraint 2, constraint 2 is the amplitude reflection coefficient constraint, constraint 3 is the task constraint Constraint 4 is the speed constraint (P uav [t+1]-P uav [t]≤Vδ t ). P uav [t+1] represents the flight speed of the UAV at time t+1, P uav [t] represents the flight speed of the drone at time t.

[0053] In the disclosed embodiments, services are provided to users within a target area through signals transmitted and reflected by a drone. Under the constraints of the drone's flight time, a model is developed that combines the phase shift matrix of the intelligent reflective surface and the drone's flight trajectory to minimize the drone's energy consumption. Considering this problem as a nondeterministic polynomial time (NP) hard problem, the initial trajectory of the drone is prioritized, and alternate optimization of the drone's trajectory, transmission system, and backscattering system is considered.

[0054] Specifically, the initial flight trajectory of the UAV is first optimized. In order to solve the continuous trajectory optimization problem, the embodiment of the present disclosure starts from a simple straight-line flight and hovering transmission model, and simplifies the problem into a simpler model, that is, the UAV flies between different user terminals and communicates above each user terminal. Therefore, when solving this model, it is necessary to determine the service order of each user terminal. Since this model is a typical traveling salesman problem, this process can be solved by a heuristic algorithm, for example, a genetic algorithm can be used to solve it. The basic idea of ​​the genetic algorithm is to start from the initial population, select individuals using the natural law of survival of the fittest, and generate a new generation of populations through hybridization and mutation, and evolve from generation to generation until the goal is met. The specific process is as follows:

[0055] Step 1: Initialize the population, that is, randomly generate a series of drone flight trajectories.

[0056] Step 2: Crossover: Randomly select the starting and ending positions of the two parent chromosomes and copy the current segment of one parent chromosome to the corresponding position of the offspring. The remaining segments are provided by the other parent chromosome. The parent chromosomes correspond to flight paths, and a flight path is obtained by randomly selecting the starting and ending positions.

[0057] Step 3: Population selection. In this embodiment, a tournament algorithm can be used, that is, a group is selected from each selected individual, and the optimal individual is selected according to the fitness of each individual (i.e., the objective function value) to enter the offspring population. This step is repeated until all offspring individuals are determined.

[0058] Step 4: Repeat the above steps until convergence.

[0059] By optimizing the initial trajectory, a trajectory with low energy consumption can be obtained. However, this model has significant room for optimization. Consider discretizing the trajectory and further optimizing it using alternating optimization. Specifically, the optimization process can be divided into two parts: one part fixes the backscatter coefficient and optimizes the drone trajectory and transmission parameters; the other part fixes the drone trajectory and transmission parameters and optimizes the backscatter coefficient. By alternating these two parts, a suboptimal solution for the system can be found. The alternating optimization process is as follows:

[0060] Step 1: Fix the backscatter coefficient and optimize the drone trajectory and transmission parameters. Although only the drone trajectory and transmission parameters are optimized, this part is still a non-convex problem. To solve it using convex optimization, this non-convex problem must first be converted to a convex one. Specifically, Taylor expansion can be used to convert the non-convex terms in the problem into convex terms, and then the successive convex approximation method is repeated until the system converges.

[0061] Step 2: Fix the drone's trajectory and transmission parameters and optimize the backscatter coefficient. To optimize the backscatter coefficient, the phase of the smart reflector can be uniformly quantized to produce discrete phase shift values. A sequential rotation algorithm is used to find the optimal solution. This algorithm rotates the previous solution N times to obtain the solution for this iteration. A one-dimensional search algorithm is then used to find the suboptimal solution.

[0062] In the embodiment of the present disclosure, by alternately executing step 1 and step 2 until the system converges, a solution that can be obtained is a suboptimal solution to this problem.

[0063] The disclosed embodiments first construct an initial drone trajectory, then alternately optimize the drone trajectory and backscatter coefficient to solve the energy consumption optimization problem in smart reflective surface-assisted drone communications. In a smart reflective surface-assisted drone downlink communication system, the drone communicates with multiple users within a region. Targets to be optimized in this process include the drone's trajectory, transmission parameters, and the backscatter coefficient of the smart reflective surface. To solve this NP-hard problem, the disclosed embodiments consider first obtaining an initial trajectory and then solving it through alternating optimization. The initial trajectory can be obtained using a heuristic algorithm. The alternating optimization can be divided into fixing the backscatter coefficient, optimizing the drone trajectory and transmission parameters, and then fixing the drone trajectory and transmission parameters, optimizing the backscatter coefficient, alternating between these two optimization steps until convergence, resulting in a suboptimal solution. By introducing smart reflective surfaces, the disclosed embodiments can improve the quality of drone communication links and, through the joint optimization of the drone trajectory and backscatter coefficient, reduce system energy consumption to a certain extent.

[0064] Based on the above embodiments, the present disclosure also provides a method for optimizing the trajectory of a drone, which can be performed by the electronic device in the present disclosure. As shown in FIG2 , the method may include the following steps:

[0065] In step S210, an energy consumption model based on the smart reflective surface during communication between the target UAV and the terminal is obtained.

[0066] In this embodiment, when a target drone is assisted in communication by a smart reflective surface, an energy consumption model for communication between the target drone and the terminal can be established. This energy consumption model can include the target drone's flight energy consumption and communication energy consumption. The target drone can be the drone described in the above embodiment, and the terminal can be the user terminal described in the above embodiment.

[0067] In step S220, the initial flight trajectory of the target UAV is obtained.

[0068] In step S230 , the initial flight trajectory is optimized using the energy consumption model to obtain a target flight trajectory.

[0069] In the disclosed embodiment, multiple flight trajectories can be randomly obtained, and by solving the energy consumption model, the target flight trajectory of the target UAV with the lowest energy consumption can be obtained. In the disclosed embodiment, the target constraint conditions can be obtained and the energy consumption model can be established based on the target constraint conditions. The target constraint conditions include at least one of the following: a power constraint of the target UAV, a phase offset constraint of the smart reflective surface, an amplitude reflection coefficient constraint of the smart reflective surface, a mission constraint, and a speed constraint. The energy consumption model can specifically be the model expressed by equation (4) in the above embodiment.

[0070] In the disclosed embodiments, when optimizing the initial flight trajectory using an energy consumption model, the energy consumption model can be solved to obtain a solution, and the initial flight trajectory can be optimized based on the solution. For example, a heuristic algorithm can be used to solve the energy consumption model, specifically a genetic algorithm. A population can be initialized to randomly generate a series of UAV flight trajectories. The crossover and population selection methods described in the above embodiments can be used until convergence, thereby obtaining an optimized initial flight trajectory.

[0071] In the embodiment of the present disclosure, the optimized initial flight trajectory can be used as the target flight trajectory of the target UAV, so that the energy consumption of the system can be reduced to a certain extent during the flight and communication of the target UAV in the target area.

[0072] Based on the above embodiment, further optimization can be performed on the basis of the above-mentioned optimized initial flight trajectory to obtain a target flight trajectory with lower energy consumption. For example, the transmission parameters between the target UAV and the terminal and the backscatter coefficient of the intelligent reflective surface can be obtained, and the target flight trajectory can be generated based on the transmission parameters, backscatter coefficient and the optimized initial flight trajectory. In the embodiment of the present disclosure, one or two of the transmission parameters, backscatter coefficient and the optimized initial flight trajectory can be fixed, and the other one can be optimized to achieve alternating optimization between the transmission parameters, backscatter coefficient and the optimized initial flight trajectory, and finally obtain the optimized target flight trajectory. The target flight trajectory obtained in this way can make the target UAV have lower energy consumption during flight and communication in the target area.

[0073] Specifically, in one embodiment of the present disclosure, the optimized initial flight trajectory can be used as the first flight trajectory. In the process of generating the target flight trajectory, the transmission parameters and the first flight trajectory can be optimized based on the backscatter coefficient to obtain the target transmission parameters. The target flight trajectory can then be obtained based on the target transmission parameters, the backscatter coefficient, and the first flight trajectory. In this embodiment of the present disclosure, the backscatter coefficient can be fixed to optimize the drone trajectory and transmission parameters. Specifically, Taylor expansion can be used to convert non-convex terms in the problem into convex terms, and a successive convex approximation method can be used to iterate until the system converges, thereby obtaining the optimized target flight trajectory.

[0074] In another embodiment of the present disclosure, during the process of generating the target flight trajectory, the backscatter coefficient can be optimized based on the first flight trajectory and transmission parameters to obtain the target backscatter coefficient; and the target flight trajectory can be obtained based on the target backscatter coefficient, transmission parameters, and the first flight trajectory. Specifically, the drone trajectory and transmission parameters can be initialized, and the backscatter coefficient can be optimized. For backscatter coefficient optimization, the phase of the smart reflective surface can be uniformly quantized to generate discrete phase shift values, and a sequential rotation algorithm can be used to find the optimal solution, that is, the previous solution is rotated N times to obtain the solution for this iteration, and a suboptimal solution is found using a one-dimensional search algorithm to obtain the target flight trajectory.

[0075] The drone trajectory optimization method provided by the disclosed embodiments obtains the target drone's initial flight trajectory by acquiring an energy consumption model for communication between the target drone and a terminal under a smart reflective surface. This energy consumption model is then optimized using the energy consumption model to obtain the target flight trajectory. By establishing an energy consumption model for communication between the target drone and the terminal under a smart reflective surface and optimizing the initial flight trajectory using the energy consumption model, the target drone's energy consumption during flight and communication can be reduced. Furthermore, the smart reflective surface can improve the target drone's communication quality.

[0076] In the case of dividing each functional module according to each function, an embodiment of the present disclosure provides a drone trajectory optimization device, which can be a server or a chip applied to a server. Figure 3 is a schematic block diagram of the functional modules of the drone trajectory optimization device provided by an exemplary embodiment of the present disclosure. As shown in Figure 3, the drone trajectory optimization device includes:

[0077] The model acquisition module 10 is used to obtain an energy consumption model when the target UAV communicates with the terminal based on the intelligent reflective surface;

[0078] An initial flight trajectory acquisition module 20 is used to acquire the initial flight trajectory of the target UAV;

[0079] The target flight trajectory acquisition module 30 is configured to optimize the initial flight trajectory using the energy consumption model to obtain a target flight trajectory.

[0080] In another embodiment provided by the present disclosure, the apparatus further includes:

[0081] A data acquisition module, configured to acquire transmission parameters between the target UAV and the terminal and a backscatter coefficient of the smart reflective surface;

[0082] The trajectory optimization module is used to generate a target flight trajectory based on the transmission parameters, the backscatter coefficient and the optimized initial flight trajectory.

[0083] In another embodiment provided by the present disclosure, the optimized initial flight trajectory is used as the first flight trajectory; and the trajectory optimization module is specifically configured to:

[0084] Optimizing the transmission parameters and the first flight trajectory based on the backscatter coefficient to obtain target transmission parameters;

[0085] A target flight trajectory is obtained based on the target transmission parameter, the backscatter coefficient and the first flight trajectory.

[0086] In another embodiment provided by the present disclosure, the optimized initial flight trajectory is used as the first flight trajectory; and the trajectory optimization module is specifically configured to:

[0087] Optimizing the backscatter coefficient based on the first flight trajectory and the transmission parameters to obtain a target backscatter coefficient;

[0088] A target flight trajectory is obtained based on the target backscatter coefficient, the transmission parameter and the first flight trajectory.

[0089] In another embodiment provided by the present disclosure, the trajectory optimization module is specifically configured to:

[0090] Initializing the first flight trajectory and the transmission parameters respectively;

[0091] The phase of the smart reflective surface is uniformly quantized based on the initialized first flight trajectory and the transmission parameter to obtain a discrete phase shift value of the backscattering coefficient.

[0092] In another embodiment provided by the present disclosure, the apparatus further includes a model construction module, wherein the model construction module is specifically configured to:

[0093] Obtaining target constraints, where the target constraints include at least one of the following: a power constraint of the target UAV, a phase offset constraint of the smart reflective surface, an amplitude reflection coefficient constraint of the smart reflective surface, a mission constraint, and a speed constraint;

[0094] The energy consumption model is established based on the target constraint condition.

[0095] For details about the device, please refer to the description of the corresponding embodiment of the method, which will not be repeated here.

[0096] The drone trajectory optimization device provided by the disclosed embodiments obtains the target drone's initial flight trajectory by acquiring an energy consumption model for communication between the target drone and a terminal under a smart reflective surface. This energy consumption model is then optimized using the energy consumption model to obtain the target flight trajectory. By establishing an energy consumption model for communication between the target drone and the terminal under a smart reflective surface and optimizing the initial flight trajectory using the energy consumption model, the target drone's energy consumption during flight and communication can be reduced. Furthermore, the smart reflective surface can improve the target drone's communication quality.

[0097] An embodiment of the present disclosure further provides an electronic device, comprising: at least one processor; a memory for storing instructions executable by the at least one processor; wherein the at least one processor is configured to execute the instructions to implement the above method disclosed in the embodiment of the present disclosure.

[0098] Figure 4 is a schematic diagram of the structure of an electronic device provided by an exemplary embodiment of the present disclosure. As shown in Figure 4, the electronic device 1800 includes at least one processor 1801 and a memory 1802 coupled to the processor 1801. The processor 1801 can execute the corresponding steps of the above method disclosed in the embodiment of the present disclosure.

[0099] The processor 1801 can also be referred to as a central processing unit (CPU), which can be an integrated circuit chip with signal processing capabilities. Each step in the method disclosed in the embodiments of the present disclosure can be completed by hardware integrated logic circuits in the processor 1801 or by software instructions. The processor 1801 can be a general-purpose processor, a digital signal processor (DSP), an ASIC, a field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the method disclosed in the embodiments of the present disclosure can be directly implemented as being executed by a hardware decoding processor, or by a combination of hardware and software modules in the decoding processor. The software module can be located in the memory 1802, such as a storage medium mature in the art, such as a random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, registers, etc. The processor 1801 reads the information in the memory 1802 and, in conjunction with its hardware, completes the steps of the method.

[0100] In addition, when various operations / processes according to the present disclosure are implemented via software and / or firmware, the programs constituting the software can be installed from a storage medium or a network to a computer system having a dedicated hardware structure, such as computer system 1900 shown in FIG5 . When the various programs are installed, the computer system can perform various functions, including those described above. FIG5 is a block diagram of the structure of a computer system provided by an exemplary embodiment of the present disclosure.

[0101] Computer system 1900 is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are intended to be examples only and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0102] As shown in FIG5 , computer system 1900 includes a computing unit 1901, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 1902 or a computer program loaded from a storage unit 1908 into a random access memory (RAM) 1903. Various programs and data required for the operation of computer system 1900 may also be stored in RAM 1903. Computing unit 1901, ROM 1902, and RAM 1903 are connected to each other via a bus 1904. An input / output (I / O) interface 1905 is also connected to bus 1904.

[0103] Several components within computer system 1900 are connected to I / O interface 1905, including an input unit 1906, an output unit 1907, a storage unit 1908, and a communication unit 1909. Input unit 1906 can be any type of device capable of inputting information into computer system 1900. Input unit 1906 can receive input numeric or character information and generate key input signals related to user settings and / or function control of an electronic device. Output unit 1907 can be any type of device capable of presenting information and may include, but is not limited to, a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. Storage unit 1908 may include, but is not limited to, a magnetic disk or an optical disk. Communication unit 1909 allows computer system 1900 to exchange information / data with other devices over a network, such as the Internet, and may include, but is not limited to, a modem, a network card, an infrared communication device, a wireless communication transceiver and / or chipset, such as a Bluetooth™ device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0104] The computing unit 1901 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the computing unit 1901 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, etc. The computing unit 1901 performs the various methods and processes described above. For example, in some embodiments, the above-mentioned methods disclosed in the embodiments of the present disclosure may be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as a storage unit 1908. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 1900 via the ROM 1902 and / or the communication unit 1909. In some embodiments, the computing unit 1901 may be configured to perform the above-mentioned methods disclosed in the embodiments of the present disclosure by any other appropriate means (e.g., by means of firmware).

[0105] The present disclosure also provides a computer-readable storage medium, wherein when the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the above-mentioned method disclosed in the present disclosure. In the embodiment of the present disclosure, the computer-readable storage medium can be a non-transitory computer-readable storage medium.

[0106] The computer-readable storage medium in the embodiments of the present disclosure can be a tangible medium that can contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. The above-mentioned computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the above. More specifically, the above-mentioned computer-readable storage medium can include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0107] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0108] The embodiments of the present disclosure further provide a computer program product, including a computer program, wherein when the computer program is executed by a processor, the method disclosed in the embodiments of the present disclosure is implemented.

[0109] In embodiments of the present disclosure, computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or combinations thereof, including but not limited to object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0110] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0111] The modules, components, or units described in the embodiments of the present disclosure may be implemented in software or hardware. The names of the modules, components, or units do not necessarily limit the modules, components, or units themselves.

[0112] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, and without limitation, exemplary hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0113] The above descriptions are merely some embodiments of the present disclosure and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure involved in the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but also encompasses other technical solutions formed by any combination of the above-mentioned technical features or their equivalents without departing from the above-mentioned disclosed concepts. For example, a technical solution formed by replacing the above-mentioned features with (but not limited to) technical features with similar functions disclosed in the present disclosure.

[0114] Although some specific embodiments of the present disclosure have been described in detail by way of examples, those skilled in the art will appreciate that the above examples are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Those skilled in the art will appreciate that modifications may be made to the above embodiments without departing from the scope and spirit of the present disclosure. The scope of the present disclosure is defined by the appended claims.

Claims

1. A method for optimizing a drone trajectory, the method comprising: Obtain the energy consumption model when the UAV and the terminal communicate based on the intelligent reflective surface target; Obtaining the initial flight trajectory of the target UAV; as well as The initial flight trajectory is optimized by using the energy consumption model to obtain a target flight trajectory.

2. The method according to claim 1, wherein: The optimizing the initial flight trajectory by using the energy consumption model includes: Solving the energy consumption model to obtain a solution result; and The initial flight trajectory is optimized based on the solution result.

3. The method according to claim 2, further comprising: Acquire the transmission parameters between the target UAV and the terminal and the backscatter coefficient of the smart reflective surface; as well as A target flight trajectory is generated based on the transmission parameters, the backscatter coefficient and the optimized initial flight trajectory.

4. The method according to claim 3, wherein: The optimized initial flight trajectory is used as a first flight trajectory; and based on the transmission parameter, the backscatter coefficient and the optimized initial flight trajectory, a target flight trajectory is generated, including: Based on the backscatter coefficient, optimizing the transmission parameters and the first flight trajectory to obtain target transmission parameters; and A target flight trajectory is obtained based on the target transmission parameters, the backscatter coefficient and the first flight trajectory.

5. The method according to claim 3, wherein: The optimized initial flight trajectory is used as a first flight trajectory; and based on the transmission parameter, the backscatter coefficient and the optimized initial flight trajectory, a target flight trajectory is generated, including: Based on the first flight trajectory and the transmission parameters, optimizing the backscatter coefficient to obtain a target backscatter coefficient; and A target flight trajectory is obtained based on the target backscatter coefficient, the transmission parameter and the first flight trajectory.

6. The method according to claim 5, wherein: The optimizing the backscatter coefficient based on the first flight trajectory and the transmission parameter includes: Initializing the first flight trajectory and the transmission parameters respectively; and Based on the initialized first flight trajectory and the transmission parameters, the phase of the smart reflection surface is uniformly quantized to obtain a discrete phase shift value of the backscattering coefficient.

7. The method according to any one of claims 1 to 6, further comprising: Acquire target constraint conditions, wherein the target constraint conditions include at least one of the following: a power constraint of the target UAV, a phase offset constraint of the smart reflective surface, an amplitude reflection coefficient constraint of the smart reflective surface, a mission constraint, and a speed constraint; and The energy consumption model is established based on the target constraint condition.

8. A UAV trajectory optimization device, comprising: A model acquisition module is used to obtain an energy consumption model when the UAV based on the intelligent reflective surface target communicates with the terminal; An initial flight trajectory acquisition module, used to acquire the initial flight trajectory of the target UAV; as well as The target flight trajectory acquisition module is used to optimize the initial flight trajectory through the energy consumption model to obtain the target flight trajectory.

9. An electronic device, comprising: at least one processor; as well as a memory for storing the at least one processor-executable instruction; The at least one processor is configured to execute the instructions to implement the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, wherein when instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method according to any one of claims 1 to 7.

11. A computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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