CN107894712A - A kind of energy distributing method of laser power supply unmanned plane track optimizing and power of communications - Google Patents

Abstract

The present invention is applicable to the improvement field of energy distribution technology, and provides an energy distribution method for trajectory optimization and communication power of a laser-powered unmanned aerial vehicle. The energy distribution method includes the following steps: S1. ₁ is a circle with a radius of n and ₁ laps to obtain enough net energy; S2, the UAV obtains enough net energy and then flies into the sensor along the tangent l ₁ l ₂ ; S3, the UAV takes r ₂ as the radius on the sensor The circular flight n ₂ circles transmits information to the sensor. Select the double-circle trajectory as the initial trajectory, and use Algorithm 1 to obtain the optimal UAV trajectory and the optimal power allocation on this trajectory to maximize throughput. After each iteration, the throughput will increase. The method is simple and clear, effectively prolongs the endurance of the UAV, and maximizes the throughput of the channel by reasonably optimizing the trajectory and transmission power, effectively improving the communication performance of the UAV.

Description

An energy distribution method for trajectory optimization and communication power of a laser-powered UAV

technical field

The invention belongs to the field of energy distribution technology improvement, in particular to an energy distribution method for trajectory optimization and communication power of a laser-powered unmanned aerial vehicle.

Background technique

The drone transmits data to ground sensors. However, the endurance of drones is limited. Wireless charging can provide longer usage time for energy-constrained devices. In order to improve the endurance of the UAV, we assume that there is a UAV powered by laser beam charging in the system. As shown in Figure 1: during the flight energy consumption process of the UAV, it needs to obtain energy from the laser beam emitted by the laser source, and at the same time use the received energy to supply energy to the ground sensors to support the information transmission between them. Due to the maneuverability of the UAV, the energy harvested by the UAV and the rate at which information is received by ground sensors varies with the distance of the flight trajectory. In order to maximize the downlink communication throughput of UAV, the problem of trajectory optimization and downlink communication power allocation of UAV is studied.

Contents of the invention

The purpose of the present invention is to provide a laser-powered unmanned aerial vehicle trajectory optimization and communication power energy distribution method, aimed at solving the above technical problems.

The present invention is achieved in this way, an energy distribution method for trajectory optimization and communication power of a laser-powered unmanned aerial vehicle, the energy distribution method includes the following steps:

S1. The UAV flies n ₁ circles above the laser source with r ₁ as the radius to obtain enough net energy. The net energy is:

S2. After obtaining enough net energy, the drone accelerates uniformly along the l ₁ l ₂ tangent and flies above the sensor. Simply consider the energy consumption of uniform straight-line flight:

S3. The UAV flies n ₂ circles on the sensor with a radius of r ₂ to transmit information to the sensor, and the energy consumption is:

Among them, V ₁ and V ₂ are the velocity of the UAV on the trajectory radius r ₁ and radius r ₂ respectively, V ₁₂ is the average speed on the trajectory l ₁ l ₂ , C=ηA _r QK, η is the energy conversion efficiency, _Ar is the area of the receiving lens, Q is the entire transmission optical receiving efficiency, and K is another loss factor, is the transmission power of the laser source to the UAV, 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 emission, H is the height of the UAV from the ground, Δθ ₁ is the angle The size of the transmission, c ₁ and c ₂ are two parameters related to the weight of the drone, the area of the wing, the air density, etc., l ₁₂ is the length value of the trajectory l ₁ l ₂ , g is the acceleration of gravity, is the average value of the power transmitted by the drone to the sensor.

A further technical solution of the present invention is: said step S1 also includes the following steps:

S11. Use the total energy obtained by the UAV from the laser source to subtract the flight energy consumption of the UAV flight to obtain the net energy, and the net energy is

in, T is flight time.

A further technical solution of the present invention is: said step S11 also includes the following steps:

S111, according to the functional 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:

A further technical solution of the present invention is: S112. Bring the acquired flight speed of the drone into the flight energy consumption function to obtain the energy consumption of the flight trajectory of the drone with r as the radius, and the energy consumption of the flight trajectory is:

Wherein, V ^* ≤V _max .

A further technical solution of the present invention is: the total energy consumption of the drone includes flight energy consumption and communication energy consumption.

The beneficial effects of the present invention are: select the double-circle trajectory as the initial trajectory, use Algorithm 1 to obtain the optimal UAV trajectory and the optimal power distribution on this trajectory to maximize throughput, after each iteration, the throughput will be improve. The method is simple and clear, effectively prolongs the endurance of the UAV, and maximizes the throughput of the channel by reasonably optimizing the trajectory and transmission power, effectively improving the communication performance of the UAV.

Description of drawings

Figure 1 is a schematic diagram of a UAV acquiring energy from a laser source and transmitting information with a ground sensor at the same time.

Figure 2 is a schematic diagram of the UAV acquiring energy in a circle with a radius of _r1 and transmitting information in a circle with a radius of _r2 .

Figure 3 is a schematic diagram of the initial trajectory of the UAV.

Detailed ways

Fig. 1-3 shows a kind of laser-powered unmanned aerial vehicle trajectory optimization and the energy allocation method of communication power provided by the present invention, and described energy allocation method comprises the following steps:

S1. The UAV flies n ₁ circles above the laser source with r ₁ as the radius to obtain enough net energy. The net energy is:

S2. After obtaining enough net energy, the drone accelerates uniformly along the l ₁ l ₂ tangent and flies above the sensor. Simply consider the energy consumption of uniform straight-line flight:

S3. The UAV flies n ₂ circles on the sensor with a radius of r ₂ to transmit information to the sensor, and the energy consumption is:

Among them, V ₁ and V ₂ are the velocity of the UAV on the trajectory radius r ₁ and radius r ₂ respectively, V ₁₂ is the average speed on the trajectory l ₁ l ₂ , C=ηA _r QK, η is the energy conversion efficiency, _Ar is the area of the receiving lens, Q is the entire transmission optical receiving efficiency, and K is another loss factor, is the transmission power of the laser source to the UAV, 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 emission, H is the height of the UAV from the ground, Δθ ₁ is the angle The size of the transmission, c ₁ and c ₂ are two parameters related to the weight of the drone, the area of the wing, the air density, etc., l ₁₂ is the length value of the trajectory l ₁ l ₂ , g is the acceleration of gravity, is the average value of the power transmitted by the drone to the sensor.

Said step S1 also includes the following steps:

S11. Use the total energy obtained by the UAV from the laser source to subtract the flight energy consumption of the UAV flight to obtain the net energy, and the net energy is

in, T is flight time.

Said step S11 also includes the following steps:

S111, according to the functional 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:

S112. Bring the acquired flight speed of the drone into the flight energy consumption function formula to obtain the energy consumption of the flight trajectory of the drone with r as the radius, and the energy consumption of the flight trajectory is:

Wherein, V ^* ≤V _max .

The total energy consumption of the drone includes flight energy consumption and communication energy consumption.

Assume that the drone is flying horizontally at a constant altitude H during the time frame T. 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 UAV change with time, expressed as (x(t), y(t), H), 0≤t≤T. There are no constraints to consider the initial and final position of the drone.

The information transmission from the UAV to the sensor node should be completed within the time range T, which is also the maximum flight time of the UAV. For the convenience of calculation, we divide the time T into N+1 equal time slots, and each time slot δ _t is small enough. Therefore, the position of the drone, the energy harvested and the energy consumption can be regarded as constant in each time slot. UAV trajectory can be expressed as q[n]=(x[n], y[n]) ^T , n∈{0,...,N}, where (x[0], y[0]) means The initial position of the drone. The location of the ground sensor can be expressed as μ=(L,0)T. The speed of the UAV is ||υ[n]||∈[0, V _max ] and υ[0] represents the initial speed of the UAV. The Euclidean distances from the UAV to the laser source and the ground sensor in the nth time interval are respectively and The acceleration of the drone is expressed as ||a[n]||∈[0, a _max ].

Assume that the transmission power of the laser source to the UAV in each time slot is The drone is fitted with a large battery to store harvested energy. In order to improve the endurance of drones, the energy harvested must be greater than the total energy consumed.

The laser energy received by the UAV in time slot n is _Ph [n]

where η is the energy conversion efficiency, _Ar is the area of the receiving lens, _D is the size of the laser beam when it is emitted, and _Δθ is the size of the angular spread. (D ₁ +d _b [n]Δθ ₁ ) ² overall represents the area of the laser beam at the distance d _b [n]. Q is the overall transmission optical reception efficiency, and K is another loss factor with a value of 1 for a laser source. a is the attenuation coefficient of the atmospheric propagation medium, the unit is m ^-1 . Define C=ηA _r QK, the amount _Ph of total harvest is

The energy consumption of UAV includes two parts: one is flight energy consumption P _f , and the other is communication energy consumption P _m . The total flight energy consumption is expressed as

in

c ₁ and c ₂ are two parameters related to the weight of the drone, the area of the wing, and the density of the air. g is the gravitational acceleration, the value is (9.8m/s ² ). m is the drone mass including all payloads.

The total communication energy consumption is expressed as

where p[n] is the energy transmitted by the drone to the ground sensor in time slot n. Therefore, the entire energy consumption of the UAV is calculated as P _c =P _f +P _m (4)

We assume that the communication channel is a direct line-of-sight, and the channel power conforms to the free-space path loss model, as

where β is the channel power and its value depends on the antenna gain etc. d _s [n] is the distance from the drone to the sensor within time slot n. The maximum instantaneous transmission rate from UAV to sensor node at time n

Among them, σ ² represents the noise power, and γ = β/σ ² represents the signal-to-noise ratio (SNR).

Throughput R _sum is used to evaluate the performance of information transmission, calculated as

Our aim is to optimize the trajectory And the transmission power p[n] of UAV-to-ground sensor communication to maximize the information transmission throughput, while the transmission power is used as a part of energy consumption, because of the limitation of receiving energy, this value is limited. To satisfy these metrics, we strike a balance between them. The problem can be modeled as:

stP _m + P _f ≤ P _h , (7)

υ[n]=υ, [n-1]+a[n-1]δ _t , n∈{1,...,N}, (8)

a _max ≥ ||a[n]||, n∈{1,...,N} (10)

υ _max ≥||υ[n]||, n∈{0,...,N}, (11)

p[n] ≥ 0, n ∈ {1,...,N}, (13)

(12) represents the average constraint condition of the energy transmitted by the UAV, where p is the average value of the power transmitted by the UAV to the sensor.

Before addressing the general problem, first consider how the UAV can gain more net energy (energy harvested minus energy expended). Consider the simple case of a drone flying at a constant speed v along a circular trajectory with a radius r centered on a laser source. Obviously, the smaller the radius r, the more energy is obtained, but the UAV consumes more energy in order to maintain more heading changes, and vice versa.

The speed of the drone is a constant, ie, ||υ[n]||=V, the acceleration a[n] is perpendicular to the speed, ie, a[n] ^T υ[n]=0. Furthermore, to maintain circular trajectories, we get ||a[n]|| ² = V ² /r. Therefore, UAV drive energy consumption (2) can be expressed as

The total harvested energy _Ph can be expressed as

By doing so, the problem reduces to an optimization problem involving two variables, r and V.

This problem can be expressed as P _h -P _f :

To solve problem (16), first notice that _Ph is independent of the variable UAV speed. Therefore, in order to obtain the minimum energy consumption P _f , the optimal speed at this time is:

When V ^* ≤ V _max , the corresponding flight energy consumption of the UAV is also simplified into a univariate function containing only r

(16) Simplified into a univariate function optimization problem as follows

This problem is non-convex, so there may be multiple local optima. So we can use one-dimensional search to get the optimal solution.

A UAV trajectory is designed in one case to maximize the information transmission throughput and meet the energy consumption constraints. The laser source and the information receiving end are respectively in the centers of the two circles. As shown in Figure 2, the UAV flies n ₁ circles in a circle with a radius of r ₁ to obtain enough energy, then flies into a circle with a radius r ₂ along the tangential direction l ₁ l ₂ , and flies n ₂ circles to transmit information. The flight speed of the UAV is the speed V ^* (17) that minimizes energy consumption.

The net energy gained in the first circle is

We can know from the previous part that we can use 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 UAV.

The energy consumption on the trajectory l ₁ l ₂ can be considered as a straight-line flight with uniform acceleration, and the value is (2)

The average speed of the drone is The trajectory l ₁ l ₂ is tangent to the two circles, through similar triangles we can get the length of l ₁ l ₂ as

The energy consumption on a circle of radius _r2 is

The radius is obtained when the energy efficiency (throughput divided by energy consumption) is the highest

is the power value transmitted by the UAV to the sensor. The problem can be expressed as follows

stE(r ₁ )-E(l ₁ l ₂ )-E(r ₂ )≥0 (23)

n ₁ ≥ 0, n _{2 ≥} 0 (25)

Since the function is linear with n ₁ and n ₂ , this problem can be solved easily. As shown in Figure 3.

The goal is to maximize downlink throughput. In this part, we can use an iterative method to consider the UAV trajectory and power allocation in general, where the water injection algorithm and continuous convex programming can be used to solve two sub-problems (P1.1) and ( P1.2).

A. Power allocation problem under a given trajectory?

The first consideration is how to allocate p[n] to maximize the throughput given the UAV trajectory.

p[n] ≥ 0, n ∈ {1,...,N} (28)

By Lagrangian and KKT conditions, we can get

λ is the Lagrange factor.

Original constraints:

Double constraint: λ≥0

Relaxation condition:

Regarding p[n], a derivative of (29) is

At this time, the best power allocation value p ^* [n] can be obtained

in

B. Trajectory optimization problem for a given power?

Next we consider the use of continuous convex programming to optimize trajectories given the UAV power allocation.

(8)-(11).

Note that the upper bound of flight energy consumption (2) is

here Indicates the change in the kinetic energy of the drone.

The lower bound of the net energy constraint (32) is

Introducing slack variables ζ _n , τ _n , we remodel (P1.2) and get

ζ _n ≥ 0, (37)

τ _n ≥ 0, (39)

(8)-(11)

(8)-(11),

must at this time and τ _n =||υ[n]||, otherwise, the value of ζ _n can be reduced all the time 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 harvested energy and flight energy consumption in (P2) are convex with respect to {q[n], ζ _n } and {υ[n], a[n], τ _n }, respectively.

Because ||q[n]|| ² is a convex and differentiable function with respect to q[n], for any given value of a trajectory {q _j [n]}, we can get

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. In addition, 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].

Define new constraints,

Since ψ _lb (q[n]) is linearly dependent on q[n], it is a convex function.

||υ[n]|| ² is also a convex differentiable function for υ[n], for a known value {υ _j [n]}, we get

When υ[n]=υ _j [n], the equation holds. The function ||υ[n]|| ² and its lower bound function ψ _lb (υ[n]) have the same gradient value 2υ _j [n] on the known value υ _j [n].

Define new constraints,

Since ψ _lb (υ[n]) is linearly dependent on υ[n], it is a convex function.

To address the non-concavity of the objective function, for a known value q _j [n], define the function

in

R _lb (q[n]) is a concave function with respect to q[n]. we got

When q[n]=q _j [n], the equation holds, and R _sum and R _lb (q[n]) have the same gradient.

Therefore, this problem can be transformed into

ζ _n ≥ 0, (51)

τ _n ≥ 0, (53)

(8)-(11).

Therefore, the original non-convex problem (P1.2) can be solved by iterative optimization (P2'), in each iteration, { _qj [n], _υj [n]} is updated. The complete algorithm for solving the entire problem is summarized below.

Optimization of Trajectory and Power Allocation Problems

1: Initialize {q[0],υ[0]} such that j=0.

2: Loop.

3: Fix the trajectory of the UAV, and use the water injection algorithm to find the optimal distribution power p ^* [n] of the UAV to the ground sensor.

4: Fixed UAV power allocation, update UAV trajectory {q _* [n],υ ^* [n]} with continuous convex programming based on known trajectories {q _j [n],υj ^[ n]}.

5: Until convergence or the maximum number of iterations has been reached.

Select the double-circle trajectory as the initial trajectory, and use Algorithm 1 to obtain the optimal UAV trajectory and the optimal power allocation on this trajectory to maximize throughput.

The above descriptions are only preferred embodiments of the present invention, and are not intended to limit the present invention. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present invention should be included in the protection of the present invention. within range.

Claims (5)

1. a laser energy unmanned aerial vehicle track optimization and the energy distribution method of communication power, it is characterized in that, described energy distribution method comprises the following steps: S1. The UAV flies n ₁ circles above the laser source with r ₁ as the radius to obtain enough net energy. The net energy is: S2. After obtaining enough net energy, the drone accelerates uniformly along the l ₁ l ₂ tangent and flies above the sensor. The energy consumption is: S3. The UAV flies n ₂ circles on the sensor with a radius of r ₂ to transmit information to the sensor, and the energy consumption is: Among them, V ₁ and V ₂ are the velocity of the UAV on the trajectory radius r ₁ and radius r ₂ respectively, V ₁₂ is the average speed on the trajectory l ₁ l ₂ , C=ηA _r QK, η is the energy conversion efficiency, _Ar is the area of the receiving lens, Q is the entire transmission optical receiving efficiency, and K is another loss factor, is the transmission power of the laser source to the UAV, 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 emission, H is the height of the UAV from the ground, Δθ ₁ is the angle The size of the transmission, c ₁ and c ₂ are two parameters related to the weight of the drone, the area of the wing, the air density, etc., l ₁₂ is the length value of the trajectory l ₁ l ₂ , g is the acceleration of gravity, is the average value of the power transmitted by the drone to the sensor. 2. The energy distribution method according to claim 1, wherein said step S1 further comprises the following steps: S11. Use the total energy obtained by the UAV from the laser source to subtract the flight energy consumption of the UAV flight to obtain the net energy, and the net energy is in, T is flight time. 3. The energy distribution method according to claim 2, wherein said step S11 further comprises the following steps: S111, according to the functional 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: . 4. The energy distribution method according to claim 2, wherein said step S11 further comprises the following steps: S112. Bring the acquired flight speed of the drone into the flight energy consumption function formula to obtain the energy consumption of the flight trajectory of the drone with r as the radius, and the energy consumption of the flight trajectory is: Wherein, V ^* ≤V _max . 5. The energy distribution method according to claim 2, wherein the total energy consumption of the drone includes flight energy consumption and communication energy consumption.