WO2019071961A1 - Method for allocating energy for track optimization and communication power in unmanned aerial vehicle having laser energy supply - Google Patents

Method for allocating energy for track optimization and communication power in unmanned aerial vehicle having laser energy supply Download PDF

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WO2019071961A1
WO2019071961A1 PCT/CN2018/089492 CN2018089492W WO2019071961A1 WO 2019071961 A1 WO2019071961 A1 WO 2019071961A1 CN 2018089492 W CN2018089492 W CN 2018089492W WO 2019071961 A1 WO2019071961 A1 WO 2019071961A1
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energy
drone
flight
energy consumption
radius
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伍楷舜
欧阳颉
车越岭
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深圳大学
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance

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  • the invention belongs to the field of energy distribution technology improvement, and particularly relates to a laser energy supply drone trajectory optimization and an energy distribution method of communication power.
  • the drone transmits data to the ground sensor.
  • the endurance of the drone is limited. Wireless charging provides longer life for energy-constrained devices.
  • the UAV In order to improve the endurance of the drone, we assume that there is a drone in the system that uses laser beam charging to drive. As shown in Figure 1: In the process of flight energy consumption, the UAV needs to obtain energy from the laser beam emitted by the laser source, and at the same time use the received energy to power the ground sensor to support the information transmission between them. Due to the maneuverability of the drone, the energy harvested by the drone and the rate at which the ground sensor receives information changes as the distance of the flight path changes. In order to maximize the downlink communication throughput of the UAV, the trajectory optimization of the UAV and the downlink communication power allocation problem are studied.
  • the present invention is achieved by a laser powered drone trajectory optimization and communication power energy distribution method, the energy distribution method comprising the following steps:
  • the drone acquires sufficient net energy by flying n 1 circle above the laser source with a radius of r 1 .
  • the net energy is:
  • the drone acquires sufficient net energy and then accelerates into the top of the sensor along the l 1 l 2 tangent line, simply considering the uniform linear flight energy consumption:
  • V 1 and V 2 are the speeds of the drone on the trajectory radius at r 1 and radius r 2 respectively.
  • V 12 is the average velocity on the trajectory l 1 l 2
  • C ⁇ A r QK
  • ⁇ is the energy conversion efficiency
  • a r is the area of the receiving lens
  • Q is the entire transmission optical receiving efficiency
  • K is another loss factor.
  • Is the transmission power of the laser source to the drone e is the natural logarithm
  • a is the attenuation coefficient of the atmospheric propagation medium
  • D 1 is the size of the laser beam at the time of launch
  • H is the height of the drone from the ground
  • ⁇ 1 is the angle
  • c 1 and c 2 are two parameters related to the weight of the drone, the wing area, the air density, etc.
  • l 12 is the length value of the locus l 1 l 2
  • g is the gravitational acceleration. It is the average value of the power transmitted by the drone to the sensor.
  • step S1 further includes the following steps:
  • T is the flight time.
  • step S11 further includes the following steps:
  • the flight speed is obtained when the flight energy consumption is minimum, and the flight speed is:
  • a further technical solution of the present invention is: S112, bringing the acquired flight speed of the drone into the flight energy function function to obtain the energy consumption of the flight path of the drone with radius r, and the flight track energy consumption is: Where V * ⁇ V max .
  • a further technical solution of the present invention is that the total energy consumption of the drone includes flight energy consumption and communication energy consumption.
  • the beneficial effects of the invention are: selecting the double circular trajectory as the initial trajectory, using algorithm 1 to obtain the optimal drone trajectory and the optimal power allocation on the trajectory to maximize the throughput, and after each iteration, the throughput will be improve.
  • the method is simple and clear, and effectively extends the endurance capability of the drone, and maximizes the throughput of the channel by reasonably optimizing the trajectory and transmission power, thereby effectively improving the communication performance of the drone.
  • FIG. 1 is a schematic diagram of information transmission between a drone and a ground sensor while acquiring energy from a laser source.
  • FIG. 2 is a schematic diagram of the drone acquiring information in a circle having a radius r 1 and transmitting information in a circle having a radius r 2 .
  • Figure 3 is a schematic illustration of the initial trajectory of the drone.
  • 1-3 illustrate a laser powered drone trajectory optimization and communication power energy distribution method provided by the present invention, the energy distribution method comprising the following steps:
  • the drone acquires sufficient net energy by flying n 1 circle above the laser source with a radius of r 1 .
  • the net energy is:
  • the drone acquires sufficient net energy and then accelerates into the top of the sensor along the l 1 l 2 tangent line, simply considering the uniform linear flight energy consumption:
  • V 1 and V 2 are the speeds of the drone on the trajectory radius at r 1 and radius r 2 respectively.
  • V 12 is the average velocity on the trajectory l 1 l 2
  • C ⁇ A r QK
  • ⁇ is the energy conversion efficiency
  • a r is the area of the receiving lens
  • Q is the entire transmission optical receiving efficiency
  • K is another loss factor.
  • Is the transmission power of the laser source to the drone e is the natural logarithm
  • a is the attenuation coefficient of the atmospheric propagation medium
  • D 1 is the size of the laser beam at the time of launch
  • H is the height of the drone from the ground
  • ⁇ 1 is the angle
  • c 1 and c 2 are two parameters related to the weight of the drone, the wing area, the air density, etc.
  • l 12 is the length value of the locus l 1 l 2
  • g is the gravitational acceleration. It is the average value of the power transmitted by the drone to the sensor.
  • the step S1 further includes the following steps:
  • T is the flight time.
  • the step S11 further includes the following steps:
  • the flight speed is obtained when the flight energy consumption is minimum, and the flight speed is:
  • the total energy consumption of the drone includes flight energy consumption and communication energy consumption.
  • the drone flies horizontally at a constant height H.
  • the position coordinates of the laser source and the ground sensor are (0, 0, 0) and (L, 0, 0), respectively.
  • the position coordinates of the drone change with time, expressed as (x(t), y(t), H), 0 ⁇ t ⁇ T. There are no restrictions on the initial and final position of the drone.
  • the transmission of information from the drone to the sensor node should be completed within the time range T, which is also the maximum flight time of the drone.
  • T time range
  • T the time range
  • ⁇ t the time interval
  • the speed of the drone is P ⁇ [n] P ⁇ [0, V max ] and ⁇ [0] represents the initial speed of the drone.
  • the Euclidean distance of the drone to the laser source and the ground sensor during the nth time interval is with The acceleration of the drone is expressed as P a[n]P ⁇ [0, a max ].
  • the transmission power of the laser source to the drone in each time slot is The drone is equipped with a large battery to store the harvested energy. In order to improve the endurance of the drone, the harvested energy must be greater than the total energy consumed.
  • the laser energy received by the drone in slot n is P h [n]
  • ⁇ is the energy conversion efficiency
  • a r is the area of the receiving lens
  • D 1 is the size of the laser beam at the time of emission
  • ⁇ 1 is the magnitude of the angular spread.
  • (D 1 + d b [n] V ⁇ 1 ) 2 represents the area of the laser beam at a distance d b [n].
  • Q is the overall transmission optical reception efficiency
  • K is another loss factor, which is 1 for the laser source.
  • a is the attenuation coefficient of the atmospheric propagation medium in m -1 .
  • UAV energy consumption comprises two parts: a flight energy P f, and the other is communication energy P m.
  • the total flight energy consumption is expressed as
  • c 1 and c 2 are two parameters related to the weight of the drone, the wing area, the air density, and the like.
  • g is the gravitational acceleration and has a value of (9.8 m/s 2 ).
  • m is the mass of the drone including all loads.
  • the total communication energy consumption is expressed as
  • ⁇ is the channel power, and its value depends on the antenna gain and the like.
  • d s [n] is the distance from the drone to the sensor in slot n. Maximum instantaneous transmission rate from the drone to the sensor node at time n
  • (12) represents the average constraint of the energy transmitted by the drone, where p is the average value of the power transmitted by the drone to the sensor.
  • the drone Before solving the general problem, first consider how the drone can get more net energy (harvested energy minus energy consumed). Considering such a simple case, the drone flies at a constant velocity v along a circular trajectory with a radius r centered on the laser source. Obviously, the smaller the radius r, the more energy is gained, but the drone consumes more energy to maintain more heading changes, and vice versa.
  • the total acquired energy Ph can be expressed as
  • a UAV trajectory is designed to maximize information transmission throughput and meet energy consumption constraints.
  • the laser source and the information receiving end are respectively at the center of the two circles.
  • the drone flies n 1 turn in a circle of radius r 1 to obtain sufficient energy and then flies into the circle of radius r 2 along the tangential direction l 1 l 2 and flies n 2 turns to transmit information.
  • the flight speed of the drone is the speed V * (17) that minimizes energy consumption.
  • the energy consumption in the trajectory l 1 l 2 can be considered as a uniform acceleration straight flight, the value is (2)
  • the average speed of the drone is The trajectory l 1 l 2 is tangent to the two circles, and through the similar triangle we can get the length of l 1 l 2 as
  • the first question to consider is how to allocate p[n] to maximize throughput given a drone trajectory.
  • ⁇ is the Lagrangian factor.
  • R lb (q[n]) is a concave function for q[n].
  • the fixed drone is allocated power, and the drone trajectory ⁇ q * [n], v * [n] ⁇ is updated with a continuous convex plan based on the known trajectory ⁇ q j [n], v j [n] ⁇ .
  • the double circular trajectory is selected as the initial trajectory, and the optimal drone trajectory and the optimal power allocation on this trajectory are obtained by Algorithm 1 to maximize the throughput.

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Abstract

A method for allocating energy for track optimization and communication power in an unmanned aerial vehicle having a laser energy supply, the energy allocation method comprising the following steps: S1. an unmanned aerial vehicle flying n1 circles having a radius of r1 above a laser source to obtain sufficient net energy; S2. after obtaining sufficient net energy, the unmanned aerial vehicle flying along a tangent line of l1l2 to be above a sensor; S3. the unmanned aerial vehicle flying n2 circles having a radius of r2 above the sensor and transmitting information to the sensor. By means of reasonably optimizing the track and transmission power, the method maximizes channel throughput and effectively improves flight duration and communication performance of the unmanned aerial vehicle.

Description

一种激光供能无人机轨迹优化和通信功率的能量分配方法Laser energy supply drone trajectory optimization and energy distribution method of communication power 技术领域Technical field
本发明属于能量分配技术改进领域，尤其涉及一种激光供能无人机轨迹优化和通信功率的能量分配方法。The invention belongs to the field of energy distribution technology improvement, and particularly relates to a laser energy supply drone trajectory optimization and an energy distribution method of communication power.
背景技术Background technique
无人机将数据传输给地面传感器。然而，无人机的续航能力是有限的。无线充电能为能源受限设备提供更长的使用时间。为了提高无人机的续航能力，我们假设***中有一个利用激光波束充电驱动的无人机。如图1所示:无人机在飞行能耗的过程中，需从激光源发射的激光束获取能量，同时利用接收的能量给地面传感器供能来支持它们之间的信息传输。由于无人机的机动性，无人机收获的能量和地面传感器接收信息的速率随着飞行轨迹距离的改变而变化。为了最大化无人机的下行链路通信吞吐量，研究了无人机的轨迹优化和下行链路通信功率分配问题。The drone transmits data to the ground sensor. However, the endurance of the drone is limited. Wireless charging provides longer life for energy-constrained devices. In order to improve the endurance of the drone, we assume that there is a drone in the system that uses laser beam charging to drive. As shown in Figure 1: In the process of flight energy consumption, the UAV needs to obtain energy from the laser beam emitted by the laser source, and at the same time use the received energy to power the ground sensor to support the information transmission between them. Due to the maneuverability of the drone, the energy harvested by the drone and the rate at which the ground sensor receives information changes as the distance of the flight path changes. In order to maximize the downlink communication throughput of the UAV, the trajectory optimization of the UAV and the downlink communication power allocation problem are studied.
发明内容Summary of the invention
本发明的目的在于提供一种激光供能无人机轨迹优化和通信功率的能量分配方法，旨在解决上述的技术问题。It is an object of the present invention to provide a method for optimizing the trajectory of a laser powered drone and an energy distribution method for communication power, aiming at solving the above technical problems.
本发明是这样实现的，一种激光供能无人机轨迹优化和通信功率的能量分配方法，所述能量分配方法包括以下步骤：The present invention is achieved by a laser powered drone trajectory optimization and communication power energy distribution method, the energy distribution method comprising the following steps:
S1、无人机在激光源上方以r 1为半径的圆飞行n 1圈获取足够的净能量，其净能量为： S1. The drone acquires sufficient net energy by flying n 1 circle above the laser source with a radius of r 1 . The net energy is:
Figure PCTCN2018089492-appb-000001
Figure PCTCN2018089492-appb-000001
S2、无人机获取足够的净能量后沿l 1l 2切线匀加速飞入传感器上方，简单考虑成匀速直线飞行能耗：
Figure PCTCN2018089492-appb-000002
S2, the drone acquires sufficient net energy and then accelerates into the top of the sensor along the l 1 l 2 tangent line, simply considering the uniform linear flight energy consumption:
Figure PCTCN2018089492-appb-000002
S3、无人机在传感器上以r 2为半径的圆飞行n 2圈传输信息给传感器，能量消耗为： S3, UAV sensor on the circle radius r 2 n 2 flight transfer information to the sensor coil, the energy consumption is:
Figure PCTCN2018089492-appb-000003
Figure PCTCN2018089492-appb-000003
其中，V 1、V 2分别是无人机在轨迹半径在r 1和半径r 2上的速度大小，
Figure PCTCN2018089492-appb-000004
V 12是在轨迹l 1l 2上的平均速度，C＝ηA rQK，η是能量转换效率，A r是接收透镜的面积，Q是整个的传输光学接收效率，K是另一个损失因子，
Figure PCTCN2018089492-appb-000005
是激光源给无人机的传输功率，e是自然对数，a是大气传播介质的衰减系数，D 1是发射时激光束的大小，H是无人机距离地面的高度，Δθ 1是角传播的大小，c 1和c 2是跟无人机的重量、机翼面积、空气密度等相关的两个参数，l 12是轨迹l 1l 2的长度值，g是重力加速度，
Figure PCTCN2018089492-appb-000006
是无人机传输给传感器功率的平均值。 Where V 1 and V 2 are the speeds of the drone on the trajectory radius at r 1 and radius r 2 respectively.
Figure PCTCN2018089492-appb-000004
V 12 is the average velocity on the trajectory l 1 l 2 , C = ηA r QK, η is the energy conversion efficiency, A r is the area of the receiving lens, Q is the entire transmission optical receiving efficiency, and K is another loss factor.
Figure PCTCN2018089492-appb-000005
Is the transmission power of the laser source to the drone, e is the natural logarithm, a is the attenuation coefficient of the atmospheric propagation medium, D 1 is the size of the laser beam at the time of launch, H is the height of the drone from the ground, Δθ 1 is the angle The size of the propagation, c 1 and c 2 are two parameters related to the weight of the drone, the wing area, the air density, etc., l 12 is the length value of the locus l 1 l 2 , and g is the gravitational acceleration.
Figure PCTCN2018089492-appb-000006
It is the average value of the power transmitted by the drone to the sensor.
本发明的进一步技术方案是：所述步骤S1还包括以下步骤：According to a further technical solution of the present invention, the step S1 further includes the following steps:
S11、利用无人机从激光源获取的总能量减去无人机飞行消耗的飞行能耗得到净能量，其净能量为S11. Using the total energy obtained by the drone from the laser source minus the flight energy consumed by the drone flight, the net energy is obtained, and the net energy is
Figure PCTCN2018089492-appb-000007
Figure PCTCN2018089492-appb-000007
其中，
Figure PCTCN2018089492-appb-000008
T是飞行时间。 among them,
Figure PCTCN2018089492-appb-000008
T is the flight time.
本发明的进一步技术方案是：所述步骤S11还包括以下步骤：According to a further technical solution of the present invention, the step S11 further includes the following steps:
S111、根据飞行速度与飞行能耗的函数关系在飞行能耗最小时求得飞行速度，其飞行速度为：
Figure PCTCN2018089492-appb-000009
S111. According to the relationship between the flight speed and the flight energy consumption, the flight speed is obtained when the flight energy consumption is minimum, and the flight speed is:
Figure PCTCN2018089492-appb-000009
本发明的进一步技术方案是：S112、将获取的无人机飞行速度带入飞行能耗函数式得到无人机以r为半径的飞行轨迹能耗，其飞行轨迹能耗为：
Figure PCTCN2018089492-appb-000010
其中，V *≤V max。 A further technical solution of the present invention is: S112, bringing the acquired flight speed of the drone into the flight energy function function to obtain the energy consumption of the flight path of the drone with radius r, and the flight track energy consumption is:
Figure PCTCN2018089492-appb-000010
Where V * ≤ V max .
本发明的进一步技术方案是：所述无人机的总能耗包括飞行能耗和通信能耗。A further technical solution of the present invention is that the total energy consumption of the drone includes flight energy consumption and communication energy consumption.
本发明的有益效果是：选择双圆轨迹当作初始轨迹，用算法1得到最优的无人机轨迹和此轨迹上最优的功率分配来最大化吞吐量，每次迭代后，吞吐量都会提高。该方法简单、明了，有效的延长了无人机的续航能力，并且通过合理优化轨迹和传输功率，最大化了信道的吞吐量，有效的提升了无人机的通信性能。The beneficial effects of the invention are: selecting the double circular trajectory as the initial trajectory, using algorithm 1 to obtain the optimal drone trajectory and the optimal power allocation on the trajectory to maximize the throughput, and after each iteration, the throughput will be improve. The method is simple and clear, and effectively extends the endurance capability of the drone, and maximizes the throughput of the channel by reasonably optimizing the trajectory and transmission power, thereby effectively improving the communication performance of the drone.
附图说明DRAWINGS
图1是无人机从激光源获取能量的同时与地面传感器进行信息传输的示意图。FIG. 1 is a schematic diagram of information transmission between a drone and a ground sensor while acquiring energy from a laser source.
图2是无人机在半径为r 1的圆获取能量和在半径为r 2的圆传输信息的 示意图。 2 is a schematic diagram of the drone acquiring information in a circle having a radius r 1 and transmitting information in a circle having a radius r 2 .
图3是无人机初始轨迹的示意图。Figure 3 is a schematic illustration of the initial trajectory of the drone.
具体实施方式Detailed ways
图1-3示出了本发明提供的一种激光供能无人机轨迹优化和通信功率的能量分配方法，所述能量分配方法包括以下步骤：1-3 illustrate a laser powered drone trajectory optimization and communication power energy distribution method provided by the present invention, the energy distribution method comprising the following steps:
S1、无人机在激光源上方以r 1为半径的圆飞行n 1圈获取足够的净能量，其净能量为： S1. The drone acquires sufficient net energy by flying n 1 circle above the laser source with a radius of r 1 . The net energy is:
Figure PCTCN2018089492-appb-000011
Figure PCTCN2018089492-appb-000011
S2、无人机获取足够的净能量后沿l 1l 2切线匀加速飞入传感器上方，简单考虑成匀速直线飞行能耗：
Figure PCTCN2018089492-appb-000012
S2, the drone acquires sufficient net energy and then accelerates into the top of the sensor along the l 1 l 2 tangent line, simply considering the uniform linear flight energy consumption:
Figure PCTCN2018089492-appb-000012
S3、无人机在传感器上以r 2为半径的圆飞行n 2圈传输信息给传感器，能量消耗为： S3, UAV sensor on the circle radius r 2 n 2 flight transfer information to the sensor coil, the energy consumption is:
Figure PCTCN2018089492-appb-000013
Figure PCTCN2018089492-appb-000013
其中，V 1、V 2分别是无人机在轨迹半径在r 1和半径r 2上的速度大小，
Figure PCTCN2018089492-appb-000014
V 12是在轨迹l 1l 2上的平均速度，C＝ηA rQK，η是能量转换效率，A r是接收透镜的面积，Q是整个的传输光学接收效率，K是另一个损失因子，
Figure PCTCN2018089492-appb-000015
是激光源给无人机的传输功率，e是自然对数，a是大气传播介质的衰减系数，D 1是发射时激光束的大小，H是无人机距离地面的高度，Δθ 1 是角传播的大小，c 1和c 2是跟无人机的重量、机翼面积、空气密度等相关的两个参数，l 12是轨迹l 1l 2的长度值，g是重力加速度，
Figure PCTCN2018089492-appb-000016
是无人机传输给传感器功率的平均值。 Where V 1 and V 2 are the speeds of the drone on the trajectory radius at r 1 and radius r 2 respectively.
Figure PCTCN2018089492-appb-000014
V 12 is the average velocity on the trajectory l 1 l 2 , C = ηA r QK, η is the energy conversion efficiency, A r is the area of the receiving lens, Q is the entire transmission optical receiving efficiency, and K is another loss factor.
Figure PCTCN2018089492-appb-000015
Is the transmission power of the laser source to the drone, e is the natural logarithm, a is the attenuation coefficient of the atmospheric propagation medium, D 1 is the size of the laser beam at the time of launch, H is the height of the drone from the ground, Δθ 1 is the angle The size of the propagation, c 1 and c 2 are two parameters related to the weight of the drone, the wing area, the air density, etc., l 12 is the length value of the locus l 1 l 2 , and g is the gravitational acceleration.
Figure PCTCN2018089492-appb-000016
It is the average value of the power transmitted by the drone to the sensor.
所述步骤S1还包括以下步骤：The step S1 further includes the following steps:
S11、利用无人机从激光源获取的总能量减去无人机飞行消耗的飞行能耗得到净能量，其净能量为S11. Using the total energy obtained by the drone from the laser source minus the flight energy consumed by the drone flight, the net energy is obtained, and the net energy is
Figure PCTCN2018089492-appb-000017
Figure PCTCN2018089492-appb-000017
其中，
Figure PCTCN2018089492-appb-000018
T是飞行时间。 among them,
Figure PCTCN2018089492-appb-000018
T is the flight time.
所述步骤S11还包括以下步骤：The step S11 further includes the following steps:
S111、根据飞行速度与飞行能耗的函数关系在飞行能耗最小时求得飞行速度，其飞行速度为：
Figure PCTCN2018089492-appb-000019
S111. According to the relationship between the flight speed and the flight energy consumption, the flight speed is obtained when the flight energy consumption is minimum, and the flight speed is:
Figure PCTCN2018089492-appb-000019
S112、将获取的无人机飞行速度带入飞行能耗函数式得到无人机以r为半径的飞行轨迹能耗，其飞行轨迹能耗为：S112. Bringing the acquired UAV flight speed into the flight energy function function to obtain the energy consumption of the flight path of the UAV with radius r, and the flight path energy consumption is:
Figure PCTCN2018089492-appb-000020
其中，V*≤V max。
Figure PCTCN2018089492-appb-000020
Where V*≤V max .
所述无人机的总能耗包括飞行能耗和通信能耗。The total energy consumption of the drone includes flight energy consumption and communication energy consumption.
假设在时间范围T内，无人机在恒定的高度H上水平飞行。激光源和地面传感器的位置坐标分别是(0,0,0)和(L,0,0)。无人机的位置坐标随着时间变化而变化，表示为(x(t),y(t),H),0≤t≤T。考虑无人机的初始和最终位置没有任何限制。It is assumed that in the time range T, the drone flies horizontally at a constant height H. The position coordinates of the laser source and the ground sensor are (0, 0, 0) and (L, 0, 0), respectively. The position coordinates of the drone change with time, expressed as (x(t), y(t), H), 0≤t≤T. There are no restrictions on the initial and final position of the drone.
无人机到传感器节点的信息传输应该在时间范围T内完成，同时这也是无人机的最大飞行时间。为方便计算，我们将时间T划分成N+1个相等的时间间隙，每个时间间隙δ t足够小。因此，无人机的位置，收获的能量和能源消耗在每个时间间隙内可以看做是不变的。无人机轨迹可以表示为q[n]＝(x[n],y[n]) T,n∈{0,...,N}，其中(x[0],y[0])表示无人机的初始位置。地面传感器的位置可以表示为μ＝(L,0) T。无人机的速度为Pυ[n]P∈[0,V max]和υ[0]表示无人机的初始速度。无人机在第n个时间间隙内到激光源和地面传感器的欧氏距离分别为
Figure PCTCN2018089492-appb-000021
和
Figure PCTCN2018089492-appb-000022
无人机的加速度表示为P a[n]P∈[0,a max]。 The transmission of information from the drone to the sensor node should be completed within the time range T, which is also the maximum flight time of the drone. For the convenience of calculation, we divide the time T into N+1 equal time gaps, and each time interval δ t is sufficiently small. Therefore, the location of the drone, the harvested energy and energy consumption can be seen as constant during each time interval. The drone trajectory can be expressed as q[n]=(x[n], y[n]) T , n ∈ {0,...,N}, where (x[0], y[0]) represents The initial position of the drone. The position of the ground sensor can be expressed as μ = (L, 0) T . The speed of the drone is P υ [n] P ∈ [0, V max ] and υ [0] represents the initial speed of the drone. The Euclidean distance of the drone to the laser source and the ground sensor during the nth time interval is
Figure PCTCN2018089492-appb-000021
with
Figure PCTCN2018089492-appb-000022
The acceleration of the drone is expressed as P a[n]P ∈ [0, a max ].
假设激光源在每个时隙给无人机的传输功率为
Figure PCTCN2018089492-appb-000023
无人机安装了一个大电池来储存收获的能量。为了提高无人机的续航能力，收获的能量必须大于消耗的总能量。 Assume that the transmission power of the laser source to the drone in each time slot is
Figure PCTCN2018089492-appb-000023
The drone is equipped with a large battery to store the harvested energy. In order to improve the endurance of the drone, the harvested energy must be greater than the total energy consumed.
无人机在时隙n内接收的激光能量为P h[n] The laser energy received by the drone in slot n is P h [n]
Figure PCTCN2018089492-appb-000024
其中η是能量转换效率，A r是接收透镜的面积,D 1是发射时激光束的大小,Δθ 1是角传播的大小。(D 1+d b[n]Vθ 1) 2整个表示的是激光束在距离d b[n]的面积。Q是整个的传输光学接收效率,K是另一个损失因子，对于激光源来说值为1。a是大气传播介质的衰减系数，单位为m -1。定义C＝ηA rQK，总收获的量P h为
Figure PCTCN2018089492-appb-000024
Where η is the energy conversion efficiency, A r is the area of the receiving lens, D 1 is the size of the laser beam at the time of emission, and Δθ 1 is the magnitude of the angular spread. (D 1 + d b [n] Vθ 1 ) 2 represents the area of the laser beam at a distance d b [n]. Q is the overall transmission optical reception efficiency, and K is another loss factor, which is 1 for the laser source. a is the attenuation coefficient of the atmospheric propagation medium in m -1 . Define C=ηA r QK, the total harvested amount P h is
Figure PCTCN2018089492-appb-000025
Figure PCTCN2018089492-appb-000025
无人机的能量消耗包括两个部分:一个是飞行能耗P f，另一个是通信能 耗P m。总的飞行能耗表示为 UAV energy consumption comprises two parts: a flight energy P f, and the other is communication energy P m. The total flight energy consumption is expressed as
Figure PCTCN2018089492-appb-000026
Figure PCTCN2018089492-appb-000026
其中
Figure PCTCN2018089492-appb-000027
among them
Figure PCTCN2018089492-appb-000027
c 1和c 2是跟无人机的重量、机翼面积、空气密度等相关的两个参数。g是重力加速度，值为(9.8m/s 2)。m是包括所有载荷在内的无人机质量。 c 1 and c 2 are two parameters related to the weight of the drone, the wing area, the air density, and the like. g is the gravitational acceleration and has a value of (9.8 m/s 2 ). m is the mass of the drone including all loads.
总的通信能耗表示为
Figure PCTCN2018089492-appb-000028
The total communication energy consumption is expressed as
Figure PCTCN2018089492-appb-000028
其中p[n]是无人机在时隙n内传输给地面传感器的能量。因此，无人机整个能耗计算为P c＝P f+P m     (4) Where p[n] is the energy that the drone transmits to the ground sensor in slot n. Therefore, the entire energy consumption of the drone is calculated as P c =P f +P m (4)
我们假设通信信道是直视距，信道功率符合自由空间路径损失模型，为
Figure PCTCN2018089492-appb-000029
We assume that the communication channel is direct line of sight and the channel power conforms to the free space path loss model.
Figure PCTCN2018089492-appb-000029
其中β是信道功率，其值取决于天线增益等。d s[n]是时隙n内无人机到传感器的距离。无人机到传感器节点在n时刻的最大瞬时传输速率 Where β is the channel power, and its value depends on the antenna gain and the like. d s [n] is the distance from the drone to the sensor in slot n. Maximum instantaneous transmission rate from the drone to the sensor node at time n
Figure PCTCN2018089492-appb-000030
Figure PCTCN2018089492-appb-000030
其中σ 2代表噪声功率,γ＝β/σ 2代表的是信噪比(SNR)。 Where σ 2 represents the noise power and γ = β / σ 2 represents the signal-to-noise ratio (SNR).
吞吐量R sum被用来评估信息传输的性能，计算为
Figure PCTCN2018089492-appb-000031
Throughput R sum is used to evaluate the performance of information transmission, calculated as
Figure PCTCN2018089492-appb-000031
我们的目的是通过优化轨迹
Figure PCTCN2018089492-appb-000032
以及无人机到地面传感器通信的传输功率p[n]来最大化信息传输吞吐量，而发射功率作为能源消耗的一部分,因为接收能源的限制，这个值是有限的。为了满足这些度量标准，我们达到了它们间的平衡。问题可以建模为: Our goal is to optimize the trajectory
Figure PCTCN2018089492-appb-000032
And the transmission power p[n] of the drone to ground sensor communication to maximize the information transmission throughput, and the transmission power as part of the energy consumption, this value is limited because of the limitation of the receiving energy. To meet these metrics, we have reached a balance between them. The problem can be modeled as:
Figure PCTCN2018089492-appb-000033
Figure PCTCN2018089492-appb-000033
s.t.P m+P f≤P h，    (7) stP m +P f ≤P h , (7)
v[n]＝v[n-1]+a[n-1]δ t，n∈{1，....，N)，      (8) v[n]=v[n-1]+a[n-1]δ t ,n∈{1,....,N), (8)
Figure PCTCN2018089492-appb-000034
Figure PCTCN2018089492-appb-000034
a max≥||a[n]||，n∈{1，....，N}，          (10) a max ≥||a[n]||,n∈{1,....,N}, (10)
vmax≥||v[n]||，n∈{0，....，N}，    (11)Vmax≥||v[n]||,n∈{0,....,N}, (11)
Figure PCTCN2018089492-appb-000035
Figure PCTCN2018089492-appb-000035
p[n]≥0，n∈{1，....N}，    (13)p[n]≥0,n∈{1,....N}, (13)
(12)代表的是无人机传输能量的平均值约束条件，其中p是无人机传输给传感器功率的平均值。(12) represents the average constraint of the energy transmitted by the drone, where p is the average value of the power transmitted by the drone to the sensor.
在解决一般问题之前，首先考虑无人机如何获取到更多的净能量(收获的能量减去消耗的能量)。考虑这样一个简单的情况，无人机以恒定速度v沿着以激光源为中心半径为r的圆形轨迹飞行。显然，半径r越小，获得的能量更多，但是无人机为了保持更多的航向变化要消耗更多的能量，反之亦然。Before solving the general problem, first consider how the drone can get more net energy (harvested energy minus energy consumed). Considering such a simple case, the drone flies at a constant velocity v along a circular trajectory with a radius r centered on the laser source. Obviously, the smaller the radius r, the more energy is gained, but the drone consumes more energy to maintain more heading changes, and vice versa.
无人机速度是个常量,i.e,Pυ[n]P＝V，加速度a[n]垂直于速度,The drone speed is a constant, i.e, Pυ[n]P=V, acceleration a[n] is perpendicular to the speed,
i.e.,a[n] Tυ[n]＝0。此外，为了保持圆形轨迹，我们得到P a[n]P 2＝V 2/r。因此，无人机驱动能耗(2)可表示为 Ie, a[n] T υ[n]=0. Furthermore, in order to maintain a circular trajectory, we obtain P a[n]P 2 =V 2 /r. Therefore, the drone drive energy consumption (2) can be expressed as
Figure PCTCN2018089492-appb-000036
Figure PCTCN2018089492-appb-000036
总的获取能量P h可表示为 The total acquired energy Ph can be expressed as
Figure PCTCN2018089492-appb-000037
Figure PCTCN2018089492-appb-000037
通过这样做，这个问题简化成包含r和V两个变量的优化问题。By doing so, the problem is reduced to an optimization problem involving two variables, r and V.
这个问题可以表示为P h-P f： This problem can be expressed as P h -P f :
Figure PCTCN2018089492-appb-000038
Figure PCTCN2018089492-appb-000038
为了解决问题(16),首先注意到P h与无人机速度这个变量无关。所以，为了得到最小的能耗P f，此时最优的速度为： To solve the problem (16), first notice that P h has nothing to do with the drone speed variable. Therefore, in order to get the minimum energy consumption P f , the optimal speed at this time is:
Figure PCTCN2018089492-appb-000039
Figure PCTCN2018089492-appb-000039
当V *≤V max此时无人机相应的飞行能耗也被简化成只含有r的单变量函数 When V * ≤ V max, the corresponding flight energy consumption of the drone is also reduced to a univariate function containing only r.
Figure PCTCN2018089492-appb-000040
Figure PCTCN2018089492-appb-000040
(16)简化成单变量函数优化问题如下所示(16) Simplified into a single variable function optimization problem as shown below
Figure PCTCN2018089492-appb-000041
Figure PCTCN2018089492-appb-000041
这个问题是非凸的，因此可能有多个局部最优点。所以我们可以使用一维搜索来获得最优解。This problem is non-convex, so there may be multiple local bests. So we can use a one-dimensional search to get the optimal solution.
设计了一种情况下的无人机轨迹来最大化信息传输吞吐量和满足能量消耗约束需求。激光源和信息接收端分别在两个圆的中心。如图2所示,无人机在半径为r 1的圆飞了n 1圈获取足够的能量然后沿着切线方向l 1l 2飞入半径r 2的圆，并且飞了n 2圈来传输信息。无人机的飞行速度是使耗能最小的速度V *(17)。 A UAV trajectory is designed to maximize information transmission throughput and meet energy consumption constraints. The laser source and the information receiving end are respectively at the center of the two circles. As shown in Figure 2, the drone flies n 1 turn in a circle of radius r 1 to obtain sufficient energy and then flies into the circle of radius r 2 along the tangential direction l 1 l 2 and flies n 2 turns to transmit information. The flight speed of the drone is the speed V * (17) that minimizes energy consumption.
在第一个圆中获取的净能量是The net energy obtained in the first circle is
Figure PCTCN2018089492-appb-000042
Figure PCTCN2018089492-appb-000042
我们可以从上一部分知道可以用一维搜索来得到最佳的半径r 1来获取更多的能量。能量E(r 1)必须能够支撑无人机的飞行和通信能耗。 We can see from the previous section that you can use a one-dimensional search to get the best radius r 1 to get more energy. The energy E(r 1 ) must be able to support the flight and communication energy consumption of the drone.
在轨迹l 1l 2的能耗可以考虑成以匀加速直线飞行,值为(2) The energy consumption in the trajectory l 1 l 2 can be considered as a uniform acceleration straight flight, the value is (2)
Figure PCTCN2018089492-appb-000043
Figure PCTCN2018089492-appb-000043
无人机的平均速度是
Figure PCTCN2018089492-appb-000044
轨迹l 1l 2与两个圆相切，通过相似三角形我们可以得到l 1l 2的长度为
Figure PCTCN2018089492-appb-000045
The average speed of the drone is
Figure PCTCN2018089492-appb-000044
The trajectory l 1 l 2 is tangent to the two circles, and through the similar triangle we can get the length of l 1 l 2 as
Figure PCTCN2018089492-appb-000045
在半径为r 2的圆上的能耗是 The energy consumption on a circle with a radius of r 2 is
Figure PCTCN2018089492-appb-000046
Figure PCTCN2018089492-appb-000046
当能量效率(吞吐量除以能耗)最高时可得到半径Radius is obtained when energy efficiency (throughput divided by energy consumption) is highest
Figure PCTCN2018089492-appb-000047
是无人机传输给传感器的功率值。问题可以如下表示
Figure PCTCN2018089492-appb-000047
It is the power value that the drone transmits to the sensor. The problem can be expressed as follows
Figure PCTCN2018089492-appb-000048
Figure PCTCN2018089492-appb-000048
s.t.E(r 1)-E(l 1l 2)-E(r 2)≥0      (23) stE(r 1 )-E(l 1 l 2 )-E(r 2 )≥0 (23)
Figure PCTCN2018089492-appb-000049
Figure PCTCN2018089492-appb-000049
n 1≥0，n 2≥0       (25) n 1 ≥0,n 2 ≥0 (25)
因为函数对于n 1和n 2是线性的，这个问题可以很容易解决。如图3所示。 Since the function is linear for n 1 and n 2 , this problem can be easily solved. As shown in Figure 3.
目的是最大化下行链路吞吐量。在这一部分中，我们可以用迭代的方法来考虑一般情况下的无人机轨迹和功率分配的情况，其中注水算法和连续的凸规划可以分别用来解决两个子问题(P1.1)和(P1.2)。The goal is to maximize downlink throughput. In this section, we can use an iterative approach to consider the general situation of UAV trajectories and power allocation, where the water injection algorithm and continuous convex programming can be used to solve two sub-problems (P1.1) and P1.2).
A.给定轨迹下的功率分配问题？A. Power allocation problem under a given trajectory?
首先考虑的问题是，在给定无人机轨迹的情况下如何分配p[n]来最大化吞吐量。The first question to consider is how to allocate p[n] to maximize throughput given a drone trajectory.
Figure PCTCN2018089492-appb-000050
Figure PCTCN2018089492-appb-000050
Figure PCTCN2018089492-appb-000051
Figure PCTCN2018089492-appb-000051
p[n]≥0，n∈{1，....N}                (28)p[n]≥0,n∈{1,....N} (28)
通过拉格朗日和KKT条件，可以得到Available through Lagrange and KKT conditions
Figure PCTCN2018089492-appb-000052
Figure PCTCN2018089492-appb-000052
λ是拉格朗日因子。λ is the Lagrangian factor.
原始约束：
Figure PCTCN2018089492-appb-000053
Original constraint:
Figure PCTCN2018089492-appb-000053
双重约束：λ≥0Double constraint: λ≥0
松驰条件：
Figure PCTCN2018089492-appb-000054
Relaxation conditions:
Figure PCTCN2018089492-appb-000054
关于p[n]，(29)的一次求导为A derivation for p[n], (29) is
Figure PCTCN2018089492-appb-000055
Figure PCTCN2018089492-appb-000055
此时可以得到最佳的功率分配值p *[n] At this point, the best power allocation value p * [n] can be obtained.
Figure PCTCN2018089492-appb-000056
Figure PCTCN2018089492-appb-000056
其中
Figure PCTCN2018089492-appb-000057
among them
Figure PCTCN2018089492-appb-000057
B.给定功率下的轨迹优化问题？B. Trajectory optimization problem at a given power?
接下来我们考虑给定了无人机功率分配下时使用连续凸规划来优化轨迹。Next we consider the use of continuous convex programming to optimize the trajectory given the drone power distribution.
Figure PCTCN2018089492-appb-000058
Figure PCTCN2018089492-appb-000058
Figure PCTCN2018089492-appb-000059
Figure PCTCN2018089492-appb-000059
(8)-(11)。(8)-(11).
注意到飞行能耗(2)的上界是Note that the upper bound of flight energy consumption (2) is
Figure PCTCN2018089492-appb-000060
Figure PCTCN2018089492-appb-000060
这里
Figure PCTCN2018089492-appb-000061
表示的是无人机动能的改变。 Here
Figure PCTCN2018089492-appb-000061
It represents the change in unmanned maneuverability.
净能量约束(32)的下界为The lower bound of the net energy constraint (32) is
Figure PCTCN2018089492-appb-000062
Figure PCTCN2018089492-appb-000062
引入松弛变量ζ n，τ n，我们重新建模(P1.2)，得到 Introducing the relaxation variables ζ n , τ n , we re-model (P1.2), get
Figure PCTCN2018089492-appb-000063
Figure PCTCN2018089492-appb-000063
Figure PCTCN2018089492-appb-000064
Figure PCTCN2018089492-appb-000064
ζn≥0，              (37)Ζn≥0, (37)
Figure PCTCN2018089492-appb-000065
Figure PCTCN2018089492-appb-000065
τ n≥0，        (39) τ n ≥0, (39)
Figure PCTCN2018089492-appb-000066
Figure PCTCN2018089492-appb-000066
(8)-(11)，(8)-(11),
此时必须
Figure PCTCN2018089492-appb-000067
和τ n＝||v[n]||,否则可以一直减小ζ n的值或者增大τ n的值来保证能得一个更大的目标值。因此，(36)相当于(34)。通过这样的一个转换，(P2)中收获的能量和飞行能耗关于{q[n]，ζ n}和{v[n]，a[n]，τ n}分别都是凸的。 Must be
Figure PCTCN2018089492-appb-000067
And τ n =||v[n]||, otherwise the value of ζ n can be reduced or the value of τ n can be increased to ensure a larger target value. Therefore, (36) is equivalent to (34). Through such a transformation, the energy harvested in (P2) and the flight energy consumption are convex with respect to {q[n], ζ n } and {v[n], a[n], τ n }, respectively.
因为||q[n]|| 2关于q[n]是凸且可微的函数,对于任意给定的一个轨迹{q j[n]}值,可以得到 Since ||q[n]|| 2 is a convex and differentiable function for q[n], for any given trajectory {q j [n]} value,
Figure PCTCN2018089492-appb-000068
Figure PCTCN2018089492-appb-000068
当q[n]＝q j[n]，等式成立。注意到(41)遵循事实，一个凸可微函数的一阶泰勒展开是它的全局最小点。此外，在已知值q j[n]上，函数||q[n]|| 2和它的下界函数ψ lb(q[n])有相同的梯度值2q j[n]。 When q[n]=q j [n], the equation holds. Note that (41) follows the fact that the first-order Taylor expansion of a convex differentiable function is its global minimum point. Furthermore, on the known value q j [n], the function ||q[n]|| 2 and its lower bound function ψ lb (q[n]) have the same gradient value 2q j [n].
定义新的约束条件，Define new constraints,
Figure PCTCN2018089492-appb-000069
Figure PCTCN2018089492-appb-000069
因为ψ lb(q[n])对于q[n]是线性相关的，所以是凸函数。 Since ψ lb (q[n]) is linearly related to q[n], it is a convex function.
||v[n]|| 2对于v[n]也是凸可微函数,对于已知值{v j[n]},得到 ||v[n]|| 2 is also a convex differentiable function for v[n], for a known value {v j [n]},
Figure PCTCN2018089492-appb-000070
Figure PCTCN2018089492-appb-000070
当v[n]＝vj[n]，等式成立。函数||v[n]|| 2和它的下界函数ψ lb(v[n])在已知值v j[n]上有相同的梯度值2v j[n]。 When v[n]=vj[n], the equation holds. There function || v [n] || 2 and its lower bound function ψ lb (v [n]) in the known value v j [n] on the same gradient value 2v j [n].
定义新的约束条件，Define new constraints,
Figure PCTCN2018089492-appb-000071
Figure PCTCN2018089492-appb-000071
因为ψ lb(v[n])对于v[n]是线性相关的，所以是凸函数。 Since ψ lb (v[n]) is linearly related to v[n], it is a convex function.
为了解决目标函数的非凹性,对于已知值q j[n],定义函数 In order to solve the non-concavity of the objective function, define the function for the known value q j [n]
Figure PCTCN2018089492-appb-000072
Figure PCTCN2018089492-appb-000072
其中among them
Figure PCTCN2018089492-appb-000073
Figure PCTCN2018089492-appb-000073
Figure PCTCN2018089492-appb-000074
Figure PCTCN2018089492-appb-000074
R lb(q[n])对于q[n]是凹函数。我们得到 R lb (q[n]) is a concave function for q[n]. we got
Figure PCTCN2018089492-appb-000075
Figure PCTCN2018089492-appb-000075
当q[n]＝q j[n]，等式成立,并且R sum和R lb(q[n])有相同的梯度。 When q[n] = q j [n], the equation holds, and R sum and R lb (q[n]) have the same gradient.
因此，这个问题可以转换成Therefore, this problem can be converted into
Figure PCTCN2018089492-appb-000076
Figure PCTCN2018089492-appb-000076
Figure PCTCN2018089492-appb-000077
Figure PCTCN2018089492-appb-000077
ζn≥0，                   (51)Ζn≥0, (51)
Figure PCTCN2018089492-appb-000078
Figure PCTCN2018089492-appb-000078
τn≥0，                      (53)Τn≥0, (53)
Figure PCTCN2018089492-appb-000079
Figure PCTCN2018089492-appb-000079
(8)-(11)。(8)-(11).
因此，原始的非凸问题(P1.2)可以通过迭代优化(P2’)来解决，在每次迭代中，{q j[n]，v j[n]}会被更新。求解整个问题的完整算法总结如下。 Therefore, the original non-convex problem (P1.2) can be solved by iterative optimization (P2'), in each iteration, {q j [n], v j [n]} will be updated. The complete algorithm for solving the whole problem is summarized below.
轨迹和功率分配问题优化Track and power allocation problem optimization
1:初始化{q[0]，v[0]}，使得j＝0。1: Initialize {q[0], v[0]} so that j=0.
2:循环。2: Loop.
3:固定无人机轨迹，用注水算法求出无人机给地面传感器的最佳分配功率p *[n]。 3: Fix the UAV trajectory and use the water injection algorithm to find the optimal distribution power p * [n] of the UAV to the ground sensor.
4:固定无人机分配功率，基于已知轨迹{q j[n]，v j[n]}用连续凸规划更新无人机轨迹{q *[n]，v *[n]}。 4: The fixed drone is allocated power, and the drone trajectory {q * [n], v * [n]} is updated with a continuous convex plan based on the known trajectory {q j [n], v j [n]}.
5:直到已经达到了收敛或最大迭代次数。5: Until the convergence or maximum number of iterations has been reached.
选择双圆轨迹当作初始轨迹，用算法1得到最优的无人机轨迹和此轨迹上最优的功率分配来最大化吞吐量。The double circular trajectory is selected as the initial trajectory, and the optimal drone trajectory and the optimal power allocation on this trajectory are obtained by Algorithm 1 to maximize the throughput.
以上所述仅为本发明的较佳实施例而已，并不用以限制本发明，凡在本发明的精神和原则之内所作的任何修改、等同替换和改进等，均应包含在本发明的保护范围之内。The above is only the preferred embodiment of the present invention, and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection of the present invention. Within the scope.

Claims (5)

  1. 一种激光供能无人机轨迹优化和通信功率的能量分配方法，其特征在于，所述能量分配方法包括以下步骤：A laser-powered drone trajectory optimization and energy distribution method for communication power, characterized in that the energy distribution method comprises the following steps:
    S1、无人机在激光源上方以r 1为半径的圆飞行n 1圈获取足够的净能量，其净能量为： S1. The drone acquires sufficient net energy by flying n 1 circle above the laser source with a radius of r 1 . The net energy is:
    Figure PCTCN2018089492-appb-100001
    Figure PCTCN2018089492-appb-100001
    S2、无人机获取足够的净能量后沿l 1l 2切线匀加速飞入传感器上方，能耗：
    Figure PCTCN2018089492-appb-100002
    S2, the drone acquires enough net energy and then accelerates into the top of the sensor along the l 1 l 2 tangent line. Energy consumption:
    Figure PCTCN2018089492-appb-100002
    S3、无人机在传感器上以r 2为半径的圆飞行n 2圈传输信息给传感器，能量消耗为： S3, UAV sensor on the circle radius r 2 n 2 flight transfer information to the sensor coil, the energy consumption is:
    Figure PCTCN2018089492-appb-100003
    Figure PCTCN2018089492-appb-100003
    其中，V 1、V 2分别是无人机在轨迹半径在r 1和半径r 2上的速度大小，
    Figure PCTCN2018089492-appb-100004
    V 12是在轨迹l 1l 2上的平均速度，C＝ηA rQK，η是能量转换效率，A r是接收透镜的面积，Q是整个的传输光学接收效率，K是另一个损失因子，
    Figure PCTCN2018089492-appb-100005
    是激光源给无人机的传输功率，e是自然对数，a是大气传播介质的衰减系数，D 1是发射时激光束的大小，H是无人机距离地面的高度，Δθ 1是角传播的大小，c 1和c 2是跟无人机的重量、机翼面积、空气密度等相关的两个参数，l 12是轨迹l 1l 2的长度值，g是重力加速度，
    Figure PCTCN2018089492-appb-100006
    是无人机传输给传感器功率的平均值。     Where V 1 and V 2 are the speeds of the drone on the trajectory radius at r 1 and radius r 2 respectively.
    Figure PCTCN2018089492-appb-100004
    V 12 is the average velocity on the trajectory l 1 l 2 , C = ηA r QK, η is the energy conversion efficiency, A r is the area of the receiving lens, Q is the entire transmission optical receiving efficiency, and K is another loss factor.
    Figure PCTCN2018089492-appb-100005
    Is the transmission power of the laser source to the drone, e is the natural logarithm, a is the attenuation coefficient of the atmospheric propagation medium, D 1 is the size of the laser beam at the time of launch, H is the height of the drone from the ground, Δθ 1 is the angle The size of the propagation, c 1 and c 2 are two parameters related to the weight of the drone, the wing area, the air density, etc., l 12 is the length value of the locus l 1 l 2 , and g is the gravitational acceleration.
    Figure PCTCN2018089492-appb-100006
    It is the average value of the power transmitted by the drone to the sensor.
  2. 根据权利要求1所述的能量分配方法，其特征在于，所述步骤S1还包括以下步骤：The energy distribution method according to claim 1, wherein the step S1 further comprises the following steps:
    S11、利用无人机从激光源获取的总能量减去无人机飞行消耗的飞行能耗得到净能量，其净能量为S11. Using the total energy obtained by the drone from the laser source minus the flight energy consumed by the drone flight, the net energy is obtained, and the net energy is
    Figure PCTCN2018089492-appb-100007
    Figure PCTCN2018089492-appb-100007
    其中，
    Figure PCTCN2018089492-appb-100008
    T是飞行时间。     among them,
    Figure PCTCN2018089492-appb-100008
    T is the flight time.
  3. 根据权利要求2所述的能量分配方法，其特征在于，所述步骤S11还包括以下步骤：The energy distribution method according to claim 2, wherein the step S11 further comprises the following steps:
    S111、根据飞行速度与飞行能耗的函数关系在飞行能耗最小时求得飞行速度，其飞行速度为：
    Figure PCTCN2018089492-appb-100009
    S111. According to the relationship between the flight speed and the flight energy consumption, the flight speed is obtained when the flight energy consumption is minimum, and the flight speed is:
    Figure PCTCN2018089492-appb-100009
  4. 根据权利要求2所述的能量分配方法，其特征在于，所述步骤S11还包括以下步骤：The energy distribution method according to claim 2, wherein the step S11 further comprises the following steps:
    S112、将获取的无人机飞行速度带入飞行能耗函数式得到无人机以r为半径的飞行轨迹能耗，其飞行轨迹能耗为：S112. Bringing the acquired UAV flight speed into the flight energy function function to obtain the energy consumption of the flight path of the UAV with radius r, and the flight path energy consumption is:
    Figure PCTCN2018089492-appb-100010
    其中，V *≤V max。
    Figure PCTCN2018089492-appb-100010
    Where V * ≤ V max .
  5. 根据权利要求2所述的能量分配方法，其特征在于，所述无人机的总能耗包括飞行能耗和通信能耗。The energy distribution method according to claim 2, wherein the total energy consumption of the drone includes flight energy consumption and communication energy consumption.
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