WO2022160554A1 - 高能效无人机绿色数据采集系统设计方法 - Google Patents

高能效无人机绿色数据采集系统设计方法 Download PDF

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WO2022160554A1
WO2022160554A1 PCT/CN2021/099267 CN2021099267W WO2022160554A1 WO 2022160554 A1 WO2022160554 A1 WO 2022160554A1 CN 2021099267 W CN2021099267 W CN 2021099267W WO 2022160554 A1 WO2022160554 A1 WO 2022160554A1
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uav
convex
sensor
iteration
optimization
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PCT/CN2021/099267
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French (fr)
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王天颢
赵楠
逄小玮
邹德岳
陈炳才
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大连理工大学
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/38—Services specially adapted for particular environments, situations or purposes for collecting sensor information
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64C—AEROPLANES; HELICOPTERS
    • B64C39/00—Aircraft not otherwise provided for
    • B64C39/02—Aircraft not otherwise provided for characterised by special use
    • B64C39/024—Aircraft not otherwise provided for characterised by special use of the remote controlled vehicle type, i.e. RPV
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U10/00—Type of UAV
    • B64U10/10—Rotorcrafts
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00—Systems involving the use of models or simulators of said systems
    • G05B17/02—Systems involving the use of models or simulators of said systems electric
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0446—Resources in time domain, e.g. slots or frames
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W72/00—Local resource management
    • H04W72/04—Wireless resource allocation
    • H04W72/044—Wireless resource allocation based on the type of the allocated resource
    • H04W72/0473—Wireless resource allocation based on the type of the allocated resource the resource being transmission power
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2101/00—UAVs specially adapted for particular uses or applications
    • B64U2101/20—UAVs specially adapted for particular uses or applications for use as communications relays, e.g. high-altitude platforms
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B64—AIRCRAFT; AVIATION; COSMONAUTICS
    • B64U—UNMANNED AERIAL VEHICLES [UAV]; EQUIPMENT THEREFOR
    • B64U2201/00—UAVs characterised by their flight controls
    • B64U2201/10—UAVs characterised by their flight controls autonomous, i.e. by navigating independently from ground or air stations, e.g. by using inertial navigation systems [INS]
    • 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

Definitions

  • the invention belongs to the technical field of data acquisition and optimization of UAV uplink communication, relates to a design scheme for efficient green communication between a single UAV and a ground sensor, and specifically refers to the joint optimization of flight trajectory, sensor wake-up scheduling and timing of UAVs during data collection. gap method, so as to achieve the purpose of maximizing the energy efficiency of the system.
  • UAV is an emerging technology that is widely used in military, public and civilian fields due to its high mobility and low cost. With the development of Internet and Internet of Things technologies in the future, UAVs can assist in meeting communication requirements such as massive connections and high information rates.
  • the application scenarios of UAV-assisted information dissemination and data collection have been extensively studied.
  • the information of wireless sensor network is transmitted to the data center through multiple hops.
  • Each sensor node not only sends its own data, but also forwards the data of other nodes.
  • the energy of some sensor nodes may be quickly exhausted, and the network link It is intermittent, and it is difficult to ensure the fairness of users.
  • As an auxiliary mobile node each sensor can directly send information to the UAV, ensuring the fairness of users.
  • the general Line of Sight (LOS) channel conditions between the UAV and the sensor also make the information transmission rate faster, and the high mobility of the UAV is more suitable for wireless communication in complex environments.
  • LOS Line of Sight
  • UAV communication systems are widely used, the limited on-board energy of UAVs fundamentally limits the endurance and communication time of UAVs, so it is very important to maximize energy efficiency in UAV green communication.
  • Energy efficiency is defined as the transmitted information bits per unit of energy consumption, which can directly increase the amount of information that the drone can communicate before it needs to be recalled, taking into account the quality of communication service and the energy consumption of the drone. With the aim of maximizing energy efficiency, the present invention appropriately designs the parameters in the system.
  • the purpose of the present invention is to solve the problem of high-energy-efficiency green communication in the UAV data acquisition system.
  • the drone In a network in which a single drone communicates with one ground sensor upstream, the drone periodically receives data, and the specific scheme is shown in schematic diagram 1.
  • the UAV flight trajectory W, the sensor wake-up schedule S and the flight time slot t By jointly optimizing the UAV flight trajectory W, the sensor wake-up schedule S and the flight time slot t, the amount of information transmitted and the energy consumption of the sensor can meet the system requirements, while the energy efficiency EE of the system is maximized.
  • a design method for a green data acquisition system for a high-energy-efficiency unmanned aerial vehicle comprising the following steps:
  • the first step is to construct the system optimization objective:
  • a UAV serves a group of I ground sensors by means of Time Division Multiple Access (TDMA), and the sensors are randomly distributed. Both the drone and the sensor are only equipped with a single antenna, and the sensors will not interfere with each other during the service process.
  • TDMA Time Division Multiple Access
  • the UAV flies at a fixed height H, the maximum flight speed is V m , and the total period is T.
  • the supporting energy of each sensor is E i , i ⁇ SI
  • the amount of data to be transmitted is B i , i ⁇ SI.
  • the channel quality only depends on the distance between the UAV and the sensor, and the power gain at a unit reference distance is expressed as ⁇ 0 .
  • the channel power gain h i [n] of sensor i in time slot n conforms to the free space path loss model, namely d i [n] is the distance between the drone and the sensor i in the three-dimensional space.
  • the binary variable S i [n] ⁇ 0,1 ⁇ is defined to describe the sensor wake-up scheduling.
  • the information transmission rate between the drone and the i-th sensor in the n-th time slot can be expressed as:
  • ⁇ 2 is the additive white Gaussian noise (AWGN) at the receiving end of the UAV
  • P A is the transmission power of the ground sensor during communication.
  • the unit is bps/Hz. Then the total amount of information transmitted in one cycle (N timeslots) of UAV service It can be expressed as:
  • the propulsion power P(V) of the UAV is mainly related to the flight speed V. It can be expressed as:
  • the propulsion power consists of blade power, parasitic power and traction power. Due to the time discretization, the speed of the nth slot can be approximately expressed as ⁇ n is defined as the flight distance of the nth time slot. Then the propulsion power P prop [n] of the nth time slot can be approximately written as:
  • the system optimization objective can be expressed as:
  • the optimization problem is constructed according to the energy efficiency formula in the first step.
  • the optimization goal is to maximize EE( ⁇ W ⁇ , ⁇ t ⁇ , ⁇ S ⁇ ).
  • the constraints include the UAV trajectory constraint, the sensor wake-up scheduling constraint, Sensor energy constraints and data volume constraints, construct the following optimization problem:
  • Equations (7b)-(7d) are trajectory constraints
  • V m is the maximum speed of the UAV
  • the UAV will return to the initial position after one flight.
  • Equations (7e) and (7f) are sensor wake-up scheduling constraints.
  • Equation (7g) is the sensor data volume constraint
  • B i is the data volume to be transmitted by sensor i.
  • Equation (7h) is the sensor energy constraint
  • E i is the maximum energy supported by sensor i per cycle.
  • the above optimization problem is a non-convex fractional optimization problem. Based on the block coordinate descent method and the continuous convex approximation technique, the original problem (7) can be decomposed into two approximate sub-convex fractional problems, and the Dinkelbach algorithm is used to finally obtain the suboptimal problem. untie.
  • the third step is to decompose the original problem into two sub-problems according to the block coordinate descent method.
  • the continuous convex approximation technique is used to approximately convert the two non-convex problems into two convex optimization problems, and design algorithms to solve them, as follows:
  • the subproblem is a non-convex optimization problem about the wake-up schedule S and time slot t.
  • S relax S to a continuous variable in the interval [0,1].
  • the auxiliary variable z[n] is introduced to satisfy which is Replacing the third term of the propulsion power P prop [n] in equation (4) with z[n] can obtain the propulsion power of the UAV under this sub-problem
  • auxiliary variable R_t[i] to satisfy
  • the continuous convex approximation technique is applied to the non-convex constraints, and the hyperbolic constraints are converted into SOCP, and the original non-convex subproblem is approximated as a convex problem, which can be expressed as:
  • subproblem (8) is the propulsion power after introducing the auxiliary variable z[n], and is a convex function of t and z[n];
  • R_t lb [i] is the lower bound of the first-order Taylor expansion of the auxiliary variable R_t[i] 2 , which is about R_t[i ] linear function;
  • the first-order Taylor expansion lower bound of which is linearly related to t.
  • the constraints of sub-problem (8) are all convex constraints, and the optimization objective (8a) is a standard concave-convex fractional programming problem with concave numerator and convex denominator, which can be solved by the existing Dinkelbach algorithm and convex optimization tool CVX. It should be noted that since the continuous convex approximation technique reduces the constraint range, the optimal solution obtained by the approximated convex problem is the lower bound of the optimal solution of the atomic problem.
  • the subproblem is a non-convex optimization problem about the UAV trajectory W.
  • the auxiliary variable y[n] is introduced to satisfy which is The UAV propulsion power under this sub-problem can be obtained by replacing the third term of the propulsion power P prop [n] in formula (4) with y[n]
  • the continuous convex approximation technique is applied to the non-convex constraints, and the original non-convex sub-problem is approximated as a convex problem, which can be expressed as:
  • subproblem (9) is the propulsion power after introducing the auxiliary variable y[n], and is a convex function about w[n]; is the information transfer rate
  • is a concave function on w[n].
  • the solution method of sub-problem (9) is the same as that of sub-problem (8), which can be solved by Dinkelbach algorithm and convex optimization tool CVX.
  • the optimal solution obtained by the approximated convex problem is still a lower bound on the optimal solution of the atomic problem.
  • the present invention proposes an overall iterative algorithm.
  • the wake-up schedule S, time slot t, and UAV trajectory W are alternately optimized by solving subproblems (8) and (9).
  • the solution obtained in each iteration is used as the input for the next iteration.
  • the iteration termination condition is that the increase of the optimization value of a certain iteration and the previous iteration is less than the set threshold.
  • the specific algorithm flow is as follows:
  • the beneficial effects of the invention are: by jointly optimizing the flight trajectory of the unmanned aerial vehicle, the sensor wake-up scheduling and the time slot, how to realize the energy-saving communication of the data collection of the unmanned aerial vehicle is given with the energy efficiency as the index, and the method for maximizing the energy efficiency is given. Refer to the value method.
  • Figure 1 is a schematic diagram of the uplink communication of single UAV data acquisition.
  • Figure 2 is a flight trajectory diagram when the transmission data amount B is 50bps/Hz and 130bps/hz respectively.
  • Figure 3 is the flight speed diagram when the transmission data amount B is 50bps/Hz and 130bps/hz respectively.
  • Fig. 4 is the influence of the transmission data amount B on the flight period of three different schemes.
  • Figure 5 shows the effect of the amount of transmitted data B on the energy efficiency of three different schemes.
  • Figure 6 shows the effect of sensor energy E on the flight cycle for three different scenarios.
  • Figure 7 shows the effect of sensor energy E on the energy efficiency of three different scenarios.
  • the drone serves 6 ground sensors, the sensors are randomly distributed.
  • the speed V min at which the propulsion power P(V) is minimized in the formula (3) is 10.0125 m/s.
  • the flight trajectory of the UAV is relatively smooth, and the flight speed does not change much and fluctuates around the energy minimum speed V min .
  • B becomes larger to 130bps/Hz the flight distance and flight time of the UAV become larger.
  • the drones both hovered near User 2 and User 4 for a period of time. This is to transmit more information while maintaining good channel quality while flying at a minimum speed of energy, thus circling around the user.
  • Figures 4 and 5 show the flight cycle and energy efficiency versus B, respectively. It can be seen that although the flight period is longer than that of the second solution, the energy efficiency advantage is obvious. Scheme 1 is more maneuverable than scheme 3, and can fly to a suitable location for communication, so the cycle is shorter and the energy efficiency is higher. When B increases, the flight period of the three schemes increases, and the energy efficiency of scheme one decreases. This is because the UAV needs more time to transmit data to meet the growth of B demand, and in order to achieve high energy efficiency, the total amount of transmitted data There is a trade-off between energy consumption E( ⁇ W ⁇ , ⁇ t ⁇ ), when the period increases and E( ⁇ W ⁇ , ⁇ t ⁇ ) will increase. When E( ⁇ W ⁇ , ⁇ t ⁇ ) grows faster, the energy efficiency decreases.
  • E( ⁇ W ⁇ , ⁇ t ⁇ ) grows faster, the energy efficiency decreases.
  • Figures 6 and 7 further compare the flight cycle and energy efficiency versus E for the three schemes.
  • E increases, the energy efficiency of all three schemes increases. This is because as E increases, the sensor has more energy to transmit data, in the total amount of transmitted data and energy consumption E( ⁇ W ⁇ , ⁇ t ⁇ ) trade-off The growth is faster, so the energy efficiency increases. It is further illustrated that the present invention can effectively realize the green communication of the UAV with high energy efficiency.

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Abstract

一种高能效无人机绿色数据采集系统设计方法，属于无人机上行通信的数据采集优化领域。首先，构建系统优化目标，在一个单无人机与地面传感器上行通信的网络中，无人机周期性的接收数据。其次，根据构建优化问题，优化目标是最大化EE({W},{t},{S})。最后，基于块坐标下降法和连续凸近似技术，将原问题分解为两个近似的子凹凸分式问题，得到次优解；并提出一种整体迭代算法：在每轮迭代中，通过求解子问题，唤醒调度S、时隙t和无人机轨迹W被交替优化。每轮迭代中得到的解作为下一轮迭代的输入。本发明能够联合优化无人机飞行轨迹、传感器唤醒调度和飞行时隙，保证传感器的传输信息量和消耗能量满足系统要求，同时最大化系统的能量效率。

Description

高能效无人机绿色数据采集系统设计方法 技术领域
本发明属于无人机上行通信的数据采集优化技术领域，涉及单无人机与地面传感器高效绿色通信的设计方案，具体是指无人机在数据采集时联合优化飞行轨迹、传感器唤醒调度和时隙的方法，从而达到最大化系统能量效率的目的。
背景技术
无人机是一种新兴的技术，由于其具有高机动性和低成本而被广泛应用在军事，公共和民用领域。随着未来互联网和物联网技术的发展，无人机可以辅助满足海量连接和高信息速率等通信需求，无人机辅助信息传播与数据采集应用场景得到了广泛的研究。传统上，无线传感器网络的信息通过多跳传输到数据中心，每个传感器节点不仅要发送自己的数据，还要转发其他节点的数据，导致某些传感器节点的能量可能很快耗尽，网络链接是间歇性的，同时也难以保证用户的公平性。而无人机作为辅助的移动节点，每个传感器都可以直接向无人机发送信息，保证了用户的公平性。此外，无人机与传感器之间一般情况下的视距(Line of Sight,LOS)信道条件也让信息传输速率更快，无人机的高机动性也更适合复杂环境下的无线通信。
尽管无人机通信系统应用广泛，但无人机有限的机载能量从根本上限制了无人机的续航和通信时间，因此无人机绿色通信中最大化能量效率是非常重要的。能量效率定义为单位能耗的传输信息比特，这可以直接增大无人机在需要召回前所能通信的信息量，兼顾了通信服务质量和无人机能量消耗。本发明以最大化能量效率为目标，对系统中的参数进行了合适的设计。
发明内容
本发明的目的是为了解决无人机数据采集系统中的高能效绿色通信问题。在一个单无人机与I个地面传感器上行通信的网络中，无人机周期性的接收数据，具体方案如示意图1所示。通过联合优化无人机飞行轨迹W、传感器唤醒调度S和飞行时隙t，以保证传感器的传输信息量和消耗能量满足系统要求，同时最大化系统的能量效率EE。
为了达到上述目的，本发明采用的技术方案如下：
一种高能效无人机绿色数据采集系统设计方法，包括以下步骤：
第一步，构建系统优化目标：
(1)一架无人机通过时分多址(TDMA)的方式为一组I个地面传感器服务，传感器随机分布。无人机和传感器都只配备单根天线，服务过程中传感器之间不会互相干扰。
(2)无人机以固定高度H飞行，最大飞行速度V m，总周期为T，通过时间离散化方法 将周期T离散化为N个时隙，每个时隙长度
Figure PCTCN2021099267-appb-000001
则在时隙n时无人机的坐标为w[n]＝[x(n),y(n)] T∈R 2×1，其中x(n),y(n)分别是无人机坐标与纵坐标，R 2×1为二维向量空间。对于随机分布的传感器集合SI＝{1,2,......,I}，传感器i的坐标固定为L i＝[x i,y i] T∈R 2×1,i∈SI，每个传感器支持能量为E i,i∈SI，需要传输的数据量为B i,i∈SI。假设无人机到地面为视距链路通信，信道质量仅取决于无人机与传感器之间的距离，单位参考距离下的功率增益表示为ρ 0。则传感器i在时隙n的信道功率增益h i[n]符合自由空间路径损耗模型，即
Figure PCTCN2021099267-appb-000002
d i[n]为三维立体空间上无人机与传感器i的距离。
(3)假定无人机在一个时隙只服务一个传感器，定义二进制变量S i[n]∈{0,1}，用于说明传感器唤醒调度。当S i[n]＝1时，表示无人机与第i个传感器在第n个时隙建立通信。当S i[n]＝0时，表示无人机与第i个传感器在第n个时隙没有通信。此时无人机与第i个传感器在第n个时隙的信息传输速率
Figure PCTCN2021099267-appb-000003
可表示为：
Figure PCTCN2021099267-appb-000004
其中，σ 2为无人机接收端的加性高斯白噪声(AWGN)，P A为地面传感器在通信时的传输功率。
Figure PCTCN2021099267-appb-000005
的单位为bps/Hz。则无人机服务一个周期(N个时隙)内传输的总信息量
Figure PCTCN2021099267-appb-000006
可以表示为：
Figure PCTCN2021099267-appb-000007
对于旋翼无人机，当参数一定时，无人机的推进功率P(V)主要与飞行速度V有关。可以表示为：
Figure PCTCN2021099267-appb-000008
所述推进功率由叶片功率、寄生功率和牵引功率三部分组成。由于进行了时间离散化， 因此第n个时隙的速度可以近似表示为
Figure PCTCN2021099267-appb-000009
Δ n定义为第n个时隙的飞行距离。则第n个时隙的推进功率P prop[n]可以由下式近似写出：
Figure PCTCN2021099267-appb-000010
式中，P 0和P i分别是悬停状态下的叶片功率和牵引功率；Ω是叶片角速度；r是转子半径；d 0代表机身阻力比；ρ是空气密度；s是转子实度；A是转子盘面积；v 0是转子平均诱导速度。上述参数均为常数。则无人机服务一个周期消耗的总推进能量E可以表示为：
Figure PCTCN2021099267-appb-000011
根据能量效率定义，系统优化目标可以表示为：
Figure PCTCN2021099267-appb-000012
第二步，根据第一步的能量效率公式构建优化问题，优化目标是最大化EE({W},{t},{S})，约束条件包括无人机轨迹约束，传感器唤醒调度约束，传感器能量约束和数据量约束，构建下述优化问题：
Figure PCTCN2021099267-appb-000013
s.t.w[1]＝w[N]                                       (7b)
||w[n+1]-w[n]|| 2≤γH 2,n＝1,......N-1                  (7c)
||w[n+1]-w[n]||≤V mt,n＝1,......N-1                    (7d)
Figure PCTCN2021099267-appb-000014
Figure PCTCN2021099267-appb-000015
Figure PCTCN2021099267-appb-000016
Figure PCTCN2021099267-appb-000017
在该优化问题中，公式(7b)-(7d)是轨迹约束，V m是无人机最大速度，无人机飞行一周要回到初始位置。公式(7e)和(7f)是传感器唤醒调度约束。公式(7g)是传感器数据量约束，B i是传感器i要传输的数据量。公式(7h)是传感器能量约束，E i是传感器i每个周期 支持的最大能量。
上述优化问题是一个非凸分式优化问题，基于块坐标下降法和连续凸近似技术，可以将原问题(7)分解为两个近似的子凹凸分式问题，并用Dinkelbach算法，最终得到次优解。
第三步，根据块坐标下降法将原问题分解为两个子问题。针对两个子问题，应用连续凸近似技术分别将两个非凸问题近似转换为两个凸优化问题，并设计算法求解，具体如下
(1)唤醒调度S和时隙t的优化子问题
固定无人机轨迹W，则子问题是关于唤醒调度S和时隙t的非凸优化问题。首先，对于二进制变量S，将S松弛为区间[0,1]内的连续变量。然后引入辅助变量z[n]，满足
Figure PCTCN2021099267-appb-000018
即
Figure PCTCN2021099267-appb-000019
用z[n]替换式(4)中推进功率P prop[n]第三项可以得到该子问题下的无人机推进功率
Figure PCTCN2021099267-appb-000020
引入辅助变量R_t[i]满足
Figure PCTCN2021099267-appb-000021
引入辅助变量后针对非凸约束条件应用连续凸近似技术，针对双曲约束条件转化为SOCP，将原非凸子问题近似为凸问题，可以表示为：
Figure PCTCN2021099267-appb-000022
s.t.||w[n+1]-w[n]||≤V mt,n＝1,......N-1        (8b)
Figure PCTCN2021099267-appb-000023
Figure PCTCN2021099267-appb-000024
Figure PCTCN2021099267-appb-000025
Figure PCTCN2021099267-appb-000026
Figure PCTCN2021099267-appb-000027
Figure PCTCN2021099267-appb-000028
子问题(8)中，
Figure PCTCN2021099267-appb-000029
是引入辅助变量z[n]后的推进功率，是关于t和z[n]的凸函数；R_t lb[i]是辅助变量R_t[i] 2的一阶泰勒展开下界，是关于R_t[i]的线性函数；
Figure PCTCN2021099267-appb-000030
是
Figure PCTCN2021099267-appb-000031
的一阶泰勒展开下界，与t是线性关系。则子问题(8)的约束条件均为凸约束，优化目标(8a)为分子为凹分母为凸的标准凹凸分式规划问题，可以用已有的Dinkelbach算法和凸优化工具 CVX求解。需要注意由于连续凸近似技术将约束范围缩小，近似后的凸问题得到的最优解是原子问题最优解的下界。
(2)无人机轨迹W的优化子问题
固定唤醒调度S和时隙t，则子问题是关于无人机轨迹W的非凸优化问题。引入辅助变量y[n]，满足
Figure PCTCN2021099267-appb-000032
即
Figure PCTCN2021099267-appb-000033
用y[n]替换公式(4)中推进功率P prop[n]第三项可以得到该子问题下的无人机推进功率
Figure PCTCN2021099267-appb-000034
引入辅助变量后针对非凸约束条件应用连续凸近似技术，将原非凸子问题近似为凸问题，可以表示为：
Figure PCTCN2021099267-appb-000035
s.t.w[1]＝w[N]                                       (9b)
||w[n+1]-w[n]|| 2≤γH 2,n＝1,......N-1                  (9c)
||w[n+1]-w[n]||≤V mt,n＝1,......N-1                    (9d)
Figure PCTCN2021099267-appb-000036
Figure PCTCN2021099267-appb-000037
Figure PCTCN2021099267-appb-000038
子问题(9)中，
Figure PCTCN2021099267-appb-000039
是引入辅助变量y[n]后的推进功率，是关于w[n]的凸函数；
Figure PCTCN2021099267-appb-000040
是信息传输速率
Figure PCTCN2021099267-appb-000041
关于||w[n]-L i||的一阶泰勒展开下界，是关于w[n]的凹函数。子问题(9)的求解方法同子问题(8)，可以用Dinkelbach算法和凸优化工具CVX求解。近似后的凸问题得到的最优解仍然是原子问题最优解的下界。
(4)整体迭代算法设计
基于上面的结果，本发明提出一种整体迭代算法。在每轮迭代中，通过求解子问题(8)和子问题(9)，唤醒调度S、时隙t和无人机轨迹W被交替优化。每轮迭代中得到的解作为下一轮迭代的输入。迭代终止条件为某轮迭代和上一轮迭代优化值的增加小于设定阈值。具体算法流程如下：
4.1)设置迭代终止阈值ε，初始轨迹w 0和迭代索引r＝0。
4.2)在第r+1次迭代时，由第r次迭代得到的轨迹w r，求解子问题(8)得到第r+1次迭代子问题(8)的优化结果，即唤醒调度S r+1和时隙t r+1。
4.3)由给定的w r、S r+1和t r+1，求解子问题(9)得到第r+1次迭代子问题(9)的优化结果，即轨迹w r+1。
4.4)若优化目标值的增加大于阈值ε，则更新迭代索引r＝r+1。跳回步骤4.2)进行下一轮迭代。若目标值的增加小于阈值ε，则终止迭代。
本发明的有益效果是：通过联合优化无人机飞行轨迹、传感器唤醒调度和时隙，以能量效率为指标给出了如何实现无人机数据采集的节能通信，为最大化能量效率给出了参考取值方法。
附图说明
图1是单无人机数据采集上行通信示意图。
图2是传输数据量B分别为50bps/Hz和130bps/hz时飞行轨迹图。
图3是传输数据量B分别为50bps/Hz和130bps/hz时飞行速度图。
图4是传输数据量B对三种不同方案飞行周期的影响。
图5是传输数据量B对三种不同方案能量效率的影响。
图6是传感器能量E对三种不同方案飞行周期的影响。
图7是传感器能量E对三种不同方案能量效率的影响。
具体实施方式
下面结合附图和实施例详细说明本发明
实施例一
假设无人机服务6个地面传感器，传感器随机分布。无人机以固定高度H＝100m飞行，最大飞行速度V m＝50m/s，一个周期T固定分为N＝60个时隙。传感器坐标用矩阵表示为L＝[-1100,500；-425,400；600,1100；200,200；800,-400；-700,-600] T。无人机接收端的加性高斯白噪声σ 2＝-110dBm，参考距离的功率增益ρ 0＝-60dB，地面传感器的传输功率P A＝0.1W。若无人机飞到传感器上方，信道功率增益
Figure PCTCN2021099267-appb-000042
此时公式(1)中的信息传输速率
Figure PCTCN2021099267-appb-000043
是最大的，最大
Figure PCTCN2021099267-appb-000044
对于公式(3)中的参数，本实施例一取经典旋翼无人机的参数值：叶片角速度Ω＝300r/s；转子半径r＝0.4m；机身阻力比d 0＝0.6；空气密度ρ＝1.225kg/m 3；转子实度s＝0.05；转子盘面积A＝0.503m 2；转子平均诱导速度v 0＝4.03m/s。此时公式(3)中使推进功率P(V)最小的速度V min＝10.0125m/s。
在此场景下，本发明假设每个传感器需要传输的数据量相同，能量约束也相同。即B i＝B，E i＝E。将以上参数带入优化问题(7)中求解，可以得到本发明提出的最大化能量效率的轨迹设计，如图2所示，对应的飞行速度如图3所示。无人机飞行轨迹较为平滑，飞行速度变化不大且都在能量最小速度V min附近波动。当B＝50bps/Hz时，无人机只在一个小范围内飞行。当B变大为130bps/Hz时，无人机飞行距离和飞行时间变大。无人机均在用户2和用户4附近徘徊一段时间。这是为了保持较好的信道质量传输更多的信息同时以能量最小速度飞行，因此会在用户附近盘旋。
实施例2
根据实施例1的设计场景，为了展示本发明的优越性，本节提出另外两种基准方案并比较性能。方案一：能量效率最大化方案(本发明)。方案二：飞-悬停方案。方案三：固定圆形轨迹下的能量效率最大化方案。
图4和图5分别展示了飞行周期和能量效率随B的变化曲线。可以看出方案一相比方案二虽然飞行周期更长，但能量效率优势明显。方案一相比方案三无人机机动性更高，可以飞到合适的位置通信，因此周期更短且能量效率更高。当B增大时，三种方案的飞行周期都增大，方案一的能量效率减小。这是因为无人机需要更多的时间传输数据来满足B需求的增长，而为了实现高能效，总传输数据量
Figure PCTCN2021099267-appb-000045
和能量消耗E({W},{t})之间需要权衡，周期增大时
Figure PCTCN2021099267-appb-000046
和E({W},{t})都会增大。当E({W},{t})增长更快时，能量效率降低。
图6和图7进一步比较了三种方案的飞行周期和能量效率随E的变化曲线。当E增大时，三种方案的能量效率都增加。这是因为随着E增大，传感器有更多的能量传输数据，在总传输数据量
Figure PCTCN2021099267-appb-000047
和能量消耗E({W},{t})权衡之间
Figure PCTCN2021099267-appb-000048
增长更快，因此能量效率增大。进一步说明了本发明能够有效实现高能效的无人机绿色通信。
以上所述实施例仅表达本发明的实施方式，但并不能因此而理解为对本发明专利的范围的限制，应当指出，对于本领域的技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进，这些均属于本发明的保护范围。

Claims (1)

  1. 一种高能效无人机绿色数据采集系统设计方法，其特征在于，包括以下步骤：
    第一步，构建系统优化目标：
    (1)一架无人机通过时分多址的方式为一组I个地面传感器服务，传感器随机分布；
    (2)无人机以固定高度H飞行，最大飞行速度V m，总周期为T，通过时间离散化方法将周期T离散化为N个时隙，每个时隙长度
    Figure PCTCN2021099267-appb-100001
    则在时隙n时无人机的坐标为w[n]＝[x(n),y(n)] T∈R 2×1，其中x(n),y(n)分别是无人机坐标与纵坐标，R 2×1为二维向量空间；对于随机分布的传感器集合SI＝{1,2,......,I}，传感器i的坐标固定为L i＝[x i,y i] T∈R 2×1,i∈SI，每个传感器支持能量为E i,i∈SI，需要传输的数据量为B i,i∈SI；假设无人机到地面为视距链路通信，信道质量仅取决于无人机与传感器之间的距离，单位参考距离下的功率增益表示为ρ 0；则传感器i在时隙n的信道功率增益h i[n]符合自由空间路径损耗模型，即
    Figure PCTCN2021099267-appb-100002
    d i[n]为三维立体空间上无人机与传感器i的距离；
    (3)假定无人机在一个时隙只服务一个传感器，定义二进制变量S i[n]∈{0,1}，表示传感器唤醒调度；当S i[n]＝1时，表示无人机与第i个传感器在第n个时隙建立通信；当S i[n]＝0时，表示无人机与第i个传感器在第n个时隙没有通信；此时无人机与第i个传感器在第n个时隙的信息传输速率
    Figure PCTCN2021099267-appb-100003
    表示为：
    Figure PCTCN2021099267-appb-100004
    其中，σ 2为无人机接收端的加性高斯白噪声，P A为地面传感器在通信时的传输功率；则无人机服务一个周期(N个时隙)内传输的总信息量
    Figure PCTCN2021099267-appb-100005
    表示为：
    Figure PCTCN2021099267-appb-100006
    对于旋翼无人机，当参数一定时，无人机的推进功率P(V)主要与飞行速度V有关，所述推进功率由叶片功率、寄生功率和牵引功率三部分组成，表示为：
    Figure PCTCN2021099267-appb-100007
    第n个时隙的速度近似表示为
    Figure PCTCN2021099267-appb-100008
    Δ n定义为第n个时隙的飞行距离；则第n个时隙的推进功率P prop[n]由下式近似写出：
    Figure PCTCN2021099267-appb-100009
    式中，P 0和P i分别是悬停状态下的叶片功率和牵引功率；Ω是叶片角速度；r是转子半径；d 0代表机身阻力比；ρ是空气密度；s是转子实度；A是转子盘面积；v 0是转子平均诱导速度；上述参数均为常数；则无人机服务一个周期消耗的总推进能量E表示为：
    Figure PCTCN2021099267-appb-100010
    根据能量效率定义，系统优化目标表示为：
    Figure PCTCN2021099267-appb-100011
    第二步，根据第一步的能量效率公式构建优化问题，优化目标是最大化EE({W},{t},{S})，约束条件包括无人机轨迹约束，传感器唤醒调度约束，传感器能量约束和数据量约束，构建下述优化问题：
    Figure PCTCN2021099267-appb-100012
    s.t. w[1]＝w[N]    (7b)
    ||w[n+1]-w[n]|| 2≤γH 2,n＝1,......N-1    (7c)
    ||w[n+1]-w[n]||≤V mt,n＝1,......N-1    (7d)
    Figure PCTCN2021099267-appb-100013
    Figure PCTCN2021099267-appb-100014
    Figure PCTCN2021099267-appb-100015
    Figure PCTCN2021099267-appb-100016
    在上述优化问题中，公式(7b)-(7d)是轨迹约束，V m是无人机最大速度，无人机飞行一周要回到初始位置；公式(7e)和(7f)是传感器唤醒调度约束；公式(7g)是传感器数据量约束，B i是传感器i要传输的数据量；公式(7h)是传感器能量约束，E i是传感器i每个周期支持的最大能量；
    第三步，根据块坐标下降法将原问题(7)分解为两个子问题；针对两个子问题，采用连续凸近似技术分别将两个非凸问题近似转换为两个凸优化问题，进行求解，具体如下：
    (1)唤醒调度S和时隙t的优化子问题
    固定无人机轨迹W，则子问题是关于唤醒调度S和时隙t的非凸优化问题；首先，对于二进制变量S，将S松弛为区间[0,1]内的连续变量；然后引入辅助变量z[n]，满足
    Figure PCTCN2021099267-appb-100017
    即
    Figure PCTCN2021099267-appb-100018
    用z[n]替换式(4)中推进功率P prop[n]第三项得到该子问题下的无人机推进功率
    Figure PCTCN2021099267-appb-100019
    引入辅助变量R_t[i]满足
    Figure PCTCN2021099267-appb-100020
    引入辅助变量后针对非凸约束条件应用连续凸近似技术，针对双曲约束条件转化为SOCP，将原非凸子问题近似为凸问题，表示为：
    Figure PCTCN2021099267-appb-100021
    s.t. ||w[n+1]-w[n]||≤V mt,n＝1,......N-1    (8b)
    Figure PCTCN2021099267-appb-100022
    Figure PCTCN2021099267-appb-100023
    Figure PCTCN2021099267-appb-100024
    Figure PCTCN2021099267-appb-100025
    Figure PCTCN2021099267-appb-100026
    子问题(8)中，
    Figure PCTCN2021099267-appb-100027
    是引入辅助变量z[n]后的推进功率，是关于t和z[n]的凸函数；R_t lb[i]是辅助变量R_t[i] 2的一阶泰勒展开下界，是关于R_t[i]的线性函数；
    Figure PCTCN2021099267-appb-100028
    是
    Figure PCTCN2021099267-appb-100029
    的一阶 泰勒展开下界，与t是线性关系；则子问题(8)的约束条件均为凸约束，优化目标(8a)为分子为凹、分母为凸的标准凹凸分式规划问题，进行求解；由于连续凸近似技术将约束范围缩小，近似后的凸问题得到的最优解是原子问题最优解的下界；
    (2)无人机轨迹W的优化子问题
    固定唤醒调度S和时隙t，则子问题是关于无人机轨迹W的非凸优化问题；引入辅助变量y[n]，满足
    Figure PCTCN2021099267-appb-100030
    即
    Figure PCTCN2021099267-appb-100031
    用y[n]替换公式(4)中推进功率P prop[n]第三项可以得到该子问题下的无人机推进功率
    Figure PCTCN2021099267-appb-100032
    引入辅助变量后针对非凸约束条件应用连续凸近似技术，将原非凸子问题近似为凸问题，表示为：
    Figure PCTCN2021099267-appb-100033
    s.t. w[1]＝w[N]    (9b)
    ||w[n+1]-w[n]|| 2≤γH 2,n＝1,......N-1    (9c)
    ||w[n+1]-w[n]||≤V mt,n＝1,......N-1    (9d)
    Figure PCTCN2021099267-appb-100034
    Figure PCTCN2021099267-appb-100035
    子问题(9)中，
    Figure PCTCN2021099267-appb-100036
    是引入辅助变量y[n]后的推进功率，是关于w[n]的凸函数；
    Figure PCTCN2021099267-appb-100037
    是信息传输速率
    Figure PCTCN2021099267-appb-100038
    关于||w[n]-L i||的一阶泰勒展开下界，是关于w[n]的凹函数；子问题(9)的求解方法同子问题(8)；近似后的凸问题得到的最优解仍然是原子问题最优解的下界；
    (4)整体迭代算法设计
    在每轮迭代中，通过求解子问题(8)和子问题(9)，唤醒调度S、时隙t和无人机轨迹W被交替优化；每轮迭代中得到的解作为下一轮迭代的输入；迭代终止条件为某轮迭代和上一轮迭代优化值的增加小于设定阈值；具体如下：
    4.1)设置迭代终止阈值ε，初始轨迹w 0和迭代索引r＝0；
    4.2)在第r+1次迭代时，由第r次迭代得到的轨迹w r，求解子问题(8)得到第r+1次迭代子问题(8)的优化结果，即唤醒调度S r+1和时隙t r+1；
    4.3)由给定的w r、S r+1和t r+1，求解子问题(9)得到第r+1次迭代子问题(9)的优化结果，即轨迹w r+1；
    4.4)若优化目标值的增加大于阈值ε，则更新迭代索引r＝r+1；跳回步骤4.2)进行下一轮迭代；若目标值的增加小于阈值ε，则终止迭代。
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