CN112923925B - Dual-mode multi-unmanned aerial vehicle collaborative track planning method for hovering and tracking ground target - Google Patents

Dual-mode multi-unmanned aerial vehicle collaborative track planning method for hovering and tracking ground target Download PDF

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CN112923925B
CN112923925B CN202110018314.4A CN202110018314A CN112923925B CN 112923925 B CN112923925 B CN 112923925B CN 202110018314 A CN202110018314 A CN 202110018314A CN 112923925 B CN112923925 B CN 112923925B
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胡超芳
曲歌
宋思涵
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Tianjin University
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Abstract

The invention discloses a multi-unmanned aerial vehicle collaborative track planning method for tracking a ground target in a dual-mode hovering mode, which comprises the steps of constructing an unmanned aerial vehicle kinematics model, a ground target motion model and a three-dimensional urban environment model; providing a switching condition of a hovering mode and an obstacle avoidance mode based on the obstacle avoidance requirements of the unmanned aerial vehicle and the high-rise building; constructing a multi-objective optimization model under a dual mode based on a multi-objective optimization algorithm of synchronous distributed model predictive control; establishing a priority model based on a relaxation satisfaction degree sequence method to obtain a generalized fuzzy satisfaction degree target planning model; constructing a multi-unmanned aerial vehicle collaborative track planning model under a dual mode based on a synchronous distributed model predictive control and fuzzy target planning method; the invention comprehensively considers performance indexes such as tracking precision, energy conservation and the like and different priority requirements thereof, and provides a reliable flight path planning scheme for a multi-unmanned aerial vehicle cooperative tracking target on the premise of ensuring controllable safety of multi-unmanned aerial vehicle cooperative flight based on a synchronous distributed model predictive control and fuzzy target planning method.

Description

Dual-mode multi-unmanned aerial vehicle collaborative track planning method for hovering and tracking ground target
Technical Field
The invention belongs to the field of flight path planning, and particularly relates to a multi-unmanned aerial vehicle collaborative flight path planning method for tracking a ground target in a dual-mode hovering mode.
Background
The unmanned aerial vehicle has small volume, high flexibility, good stability and strong adaptability, can replace human beings to finish difficult work, undertakes special missions, and is widely applied to military and civil aspects such as agricultural plant protection, forest fire extinguishing, marine rescue, logistics transportation and the like. Because the unmanned aerial vehicle does not need manned flight, the characteristic of potential risk can be avoided, the unmanned aerial vehicle is often used for executing some complex and high-risk tasks, such as real-time monitoring and tracking of ground targets in urban scenes with severe conditions and complex maps. However, building sheltering in urban environments can affect the tracking effect, and therefore in order to achieve higher tracking accuracy, the tracking task is often completed through cooperation of multiple unmanned aerial vehicles.
Taking a complex urban environment as an example, in the method, buildings are distributed in a high-low mixed mode, the speed of a moving target is changed, accurate monitoring and tracking of the unmanned aerial vehicles are achieved through cooperative work of the unmanned aerial vehicles, important research values are achieved, and the aircraft planning algorithm of the unmanned aerial vehicles is concerned. When tracking a target moving at a lower speed, the drone needs to maintain a certain cruising speed, so that the drone continuously spirals around the target to avoid stalling. When high-rise building obstacles exist in the coverage area of the unmanned aerial vehicle sensor, the obstacle avoidance is considered, and simultaneously, the tracking task is completed to the maximum extent. Therefore, the dual-mode flight path planning scheme is necessary and complete for tracking the variable speed target and adapting to the complex flight environment, and different performance index functions and constraint conditions are set according to the tracking characteristics of different modes to adapt to requirements, such as precision requirements, energy-saving requirements and the like. The establishment of the performance index model and the representation of the priority under different modes have a decisive effect on whether the optimal flight path can be obtained. In addition, considering the flight safety of the unmanned aerial vehicles, when a communication delay condition exists, how to avoid collision between the unmanned aerial vehicles is also one of the important points. Therefore, under the condition of considering multiple performance requirements of unmanned aerial vehicle tracking precision, energy conservation, collision avoidance, obstacle avoidance and the like, in order to realize the double-mode multi-unmanned aerial vehicle spiral tracking task of the ground target, the method has important theoretical significance and practical significance for researching the collaborative track planning algorithm.
The unmanned aerial vehicle has the advantages of being wide in aerial environment and few in uncertain factors, and being capable of utilizing the unmanned aerial vehicle to perform tasks which are difficult to complete on the ground. The flight path planning is one of the relevant key technologies of the unmanned aerial vehicle, and the existing special research contents are rich and the algorithms are various. However, a single unmanned aerial vehicle cannot be suitable for some complicated, crowded and sheltered terrain conditions, and therefore multiple unmanned aerial vehicles need to be adopted to cooperate to complete work. The purpose of the collaborative planning of multiple unmanned aerial vehicles is not to require a certain unmanned aerial vehicle to obtain an optimal path, but to require the overall performance of the unmanned aerial vehicle cluster to be optimal. However, the existing unmanned aerial vehicle collaborative track planning algorithm for tracking at low speed of the target is few, and the problems of obstacle avoidance and the like of the urban environment are also rarely considered, so that the method has important research significance.
Disclosure of Invention
The invention relates to cooperative track planning of multiple unmanned aerial vehicles, which is characterized in that multiple unmanned aerial vehicles track moving ground targets in an urban environment as a background, on the basis of ensuring high-precision tracking, the energy-saving problem and the safe flight problem of the unmanned aerial vehicles are considered, and a dual-mode cooperative track planning algorithm for hovering and tracking the ground targets is designed.
The invention is implemented by adopting the following technical scheme:
the multi-unmanned aerial vehicle collaborative track planning method for the dual-mode hovering tracking of the ground target comprises the following steps:
constructing an unmanned aerial vehicle kinematic model, a ground target motion model and a three-dimensional city environment model by taking ground target tracking of multiple unmanned aerial vehicles for tracking low-speed movement in a city as a background;
providing a condition for switching between a spiral mode and an obstacle avoidance mode based on the obstacle avoidance requirements of the unmanned aerial vehicle and the high-rise building;
constructing a multi-objective optimization model under a dual mode based on a multi-objective optimization algorithm of synchronous distributed model predictive control;
establishing a priority model based on a relaxation satisfaction degree sequence method to obtain a generalized fuzzy satisfaction degree target planning model;
and constructing a multi-unmanned aerial vehicle collaborative track planning model under a dual mode based on a synchronous distributed model prediction control and fuzzy target planning method.
Further, the dual-mode multi-unmanned aerial vehicle collaborative track planning method for hovering and tracking the ground target is characterized in that: providing a switching condition of a hovering mode and an obstacle avoidance mode based on the obstacle avoidance requirements of the unmanned aerial vehicle and the high-rise building; wherein:
1) Hover mode conditions: when the high-rise building is out of the visual range of all unmanned aerial vehicle sensors, i.e.
Figure BDA0002887791960000021
Meanwhile, all unmanned aerial vehicles are in a hovering mode;
2) Obstacle avoidance mode conditions: when the high-rise building is within a certain unmanned sensor visible range, that is to say
Figure BDA0002887791960000022
In order to meet the obstacle avoidance requirement, all unmanned aerial vehicles are in an obstacle avoidance mode.
Wherein R is B Is the minimum circumscribed circle radius of the bottom surface of the building, then
Figure BDA0002887791960000023
Represents half of the length of a side of the bottom surface of the building; r s Is the unmanned aerial vehicle sensor coverage area radius.
Further, the dual-mode multi-unmanned aerial vehicle collaborative track planning method for hovering and tracking the ground target is characterized in that: constructing a multi-objective optimization model under a dual mode based on a multi-objective optimization algorithm of synchronous distributed model predictive control; wherein:
a: spiral pattern
The two performance indexes of the hover distance keeping and the phase angle difference keeping are set and arranged in parallel at the first priority. In addition, the maintenance characteristics of the speed and the steering rate of the unmanned aerial vehicle model are considered, the speed input cost and the steering rate input cost are set, and two performance indexes in the aspect of energy conservation are arranged at the second priority.
The performance index in hover mode is as follows:
1) Spiral distance maintenance
2) Phase angle difference maintenance
3) Speed input cost
4) Steering rate input cost
According to the state and control limit of the unmanned aerial vehicle, the requirements of collision and obstacle avoidance set state constraint and control constraint, collision avoidance constraint, compatibility constraint and obstacle avoidance constraint. The method is characterized in that collision avoidance constraints which are usually adopted are distinguished, and based on the algorithm characteristics of synchronous distributed predictive control, each unmanned aerial vehicle can only obtain state information of other unmanned aerial vehicles at the previous sampling moment at the current sampling moment, and can only take collision avoidance actions according to the prediction result at the previous moment, and uncertain errors caused by information delay need to be constrained, so that new collision avoidance constraints and compatibility constraint conditions are designed to reduce the uncertain errors and avoid collision among the unmanned aerial vehicles.
The constraints in hover mode are as follows:
1) State constraints and control constraints
2) Restraint of avoiding collision
3) Compatibility constraints
B: obstacle avoidance mode
To ensure high accuracy tracking, a tracking error cost performance indicator is set and arranged at a first priority. In addition, considering the energy-saving requirement, two performance indexes of speed input cost and steering rate input cost are set and arranged at the second priority.
The performance indexes in the obstacle avoidance mode are as follows:
1) Tracking error cost
2) Speed input cost
3) Steering rate input cost
The constraints are as follows:
1) State constraints and control constraints:
2) Restraint of avoiding collision
3) Compatibility constraints
4) Obstacle avoidance restraint
Further, the multi-unmanned aerial vehicle collaborative track planning model in the dual mode respectively comprises a multi-unmanned aerial vehicle collaborative track planning model in a hovering mode and a multi-unmanned aerial vehicle collaborative track planning model in an obstacle avoidance mode; wherein:
the multi-unmanned aerial vehicle collaborative track planning model in the hovering mode is as follows:
Figure BDA0002887791960000041
Figure BDA0002887791960000042
Figure BDA0002887791960000043
Figure BDA0002887791960000044
Figure BDA0002887791960000045
Figure BDA0002887791960000046
n 1 ≤r 0 -0,p 1 ≤R v -r 0 ,p 2 ≤1-cosθ 0
Figure BDA0002887791960000047
n 1 ·p 1 ＝0,n 3 ·p 3 ＝0,n 4 ·p 4 ＝0；n 1 ,p 1 ,p 2 ,n 3 ,p 3 ,n 4 ,p 4 ≥0；-1≤η≤1
x i (k|k)＝x i (k),y i (k|k)＝y i (k),ψ i (k|k)＝ψ i (k),υ i (k|k)＝υ i (k),ω i (k|k)＝ω i (k)
Figure BDA0002887791960000048
Figure BDA0002887791960000049
Figure BDA00028877919600000410
Figure BDA00028877919600000411
Figure BDA00028877919600000412
P＝0,1,..H-1；i,j＝1,2,..N,i≠j；l＝1,2,..N h ；
the multi-unmanned aerial vehicle collaborative track planning model in the obstacle avoidance mode comprises the following steps:
Figure BDA0002887791960000051
Figure BDA0002887791960000052
Figure BDA0002887791960000053
Figure BDA0002887791960000054
Figure BDA0002887791960000055
n 6 ·p 6 ＝0,n 7 ·p 7 ＝0；p 5 ,n 6 ,p 6 ,n 7 ,p 7 ≥0；-1≤η≤1
x i (k|k)＝x i (k),y i (k|k)＝y i (k),ψ i (k|k)＝ψ i (k),υ i (k|k)＝υ i (k),ω i (k|k)＝ω i (k)
Figure BDA0002887791960000056
Figure BDA0002887791960000057
Figure BDA0002887791960000058
Figure BDA0002887791960000059
Figure BDA00028877919600000510
Figure BDA00028877919600000511
P＝0,1,..H-1；i,j＝1,2,..N,i≠j；l＝1,2,..N h 。
further, the switching condition of the hovering mode and the obstacle avoidance mode is a rolling solving process optimized based on a nonlinear programming method, and the method comprises the following steps:
step 1: at a sampling moment k, judging the flight mode of the unmanned aerial vehicle cluster according to the distance between the unmanned aerial vehicle and a high-rise building, if the flight mode is a hover mode, jumping to Step2, and if the flight mode is an obstacle avoidance mode, jumping to Step3;
step 2: establishing a multi-unmanned aerial vehicle collaborative track planning model in a hovering mode, solving an optimization result based on a nonlinear programming algorithm, and jumping to Step4;
step 3: establishing a multi-unmanned aerial vehicle collaborative track planning model in an obstacle avoidance mode, and solving an optimization result based on a nonlinear programming algorithm;
step 4: applying the first item of the solved optimal control sequence to the corresponding unmanned aerial vehicle; the optimal control sequence is as follows:
u υi (k+P|k),u ωi (k+P|k)
step5: and (4) sending the predicted track of the current unmanned aerial vehicle at the moment k to other unmanned aerial vehicles, and turning to Step 1 at the moment k +1 until the tracking task is finished.
Advantageous effects
In the process of planning the ground target track tracked by the multiple unmanned aerial vehicles, the invention has three main targets: enabling the unmanned aerial vehicle to accurately track and monitor the target; the safety problem of the unmanned aerial vehicle in the flight process is guaranteed; the energy lost in flight is saved as much as possible. The first point is the problem of tracking precision, namely, after the unmanned aerial vehicle group obtains the state information of the ground target, the purpose of tracking the same ground target is achieved by solving the collaborative optimal flight path. In order to meet the actual situation, the ground target always travels at a low speed, which is considered in the research, and the difficulty of tracking is undoubtedly increased. The second point is the problem of flight safety, and the key for solving the problem is to set reasonable obstacle avoidance and collision avoidance constraints, especially to consider the problem of collision avoidance among multiple unmanned aerial vehicles under the distributed model predictive control framework. The third point is the energy saving problem, and the target is used as an auxiliary target rather than a primary target of the track planning, so how to balance the proportion relation among the performance indexes also becomes one of the key problems of the track planning.
The invention provides a high-precision tracking scheme for continuously tracking a low-speed moving target in an urban environment, namely a dual-mode multi-unmanned aerial vehicle collaborative track planning algorithm for spirally tracking a ground target. On the basis that an unmanned aerial vehicle kinematics model, a ground target motion model and a three-dimensional urban environment model are known, the accurate tracking of the ground target is realized by adopting a dual-mode switching tracking strategy with a circling mode as a main mode and an obstacle avoidance mode as an auxiliary mode on the assumption that the motion track is also known. The performance indexes such as tracking precision, energy conservation and the like and different priority requirements thereof are comprehensively considered, and an efficient and reliable flight path planning scheme is provided for the multi-unmanned aerial vehicle cooperative tracking target on the premise of ensuring controllable safety of the multi-unmanned aerial vehicle cooperative flight based on the synchronous distributed model predictive control and fuzzy target planning method.
Drawings
FIG. 1 is a flow chart of the hover/obstacle avoidance model building in the present invention;
FIG. 2 is a flow chart of the rolling optimization in the present invention;
fig. 3 is a simulation diagram of the three-dimensional tracking effect in the present invention.
Detailed Description
Aiming at the problem that the conventional multi-unmanned aerial vehicle collaborative track planning is difficult to adapt to urban environment, the invention adopts a dual-mode switching tracking strategy with a hovering mode as a main mode and an obstacle avoidance mode as an auxiliary mode to realize the accurate tracking of a ground target. The dual-mode multi-unmanned aerial vehicle collaborative track planning algorithm for hovering and tracking the ground target can be used for monitoring and tracking a low-speed moving target in an urban environment. The specific process is as follows:
1. the ground target tracking of multiple unmanned aerial vehicles for tracking the middle-low-speed movement of the city is used as a background, and an unmanned aerial vehicle kinematics model, a ground target movement model and a three-dimensional city environment model are constructed.
Firstly, an unmanned aerial vehicle kinematics model is introduced, the fixed-wing unmanned aerial vehicle model adopted in the invention is a double-input unmanned aerial vehicle kinematics model with speed and course keeping functions, and compared with a single-input constant-speed unmanned aerial vehicle model which is usually adopted, the speed and the steering rate of an unmanned aerial vehicle in the model are controllable and variable, and the model is suitable for tracking complex tasks such as variable speed targets. The kinematics model of the unmanned aerial vehicle is as follows:
Figure BDA0002887791960000071
wherein i =1,2,. N, N is the number of drones used for tracking;
Figure BDA0002887791960000072
is the three-dimensional position coordinate of drone i, z i Indicating the flight altitude; psi i Is the course angle; upsilon is i And ω i Speed and steering rate, respectively; u. of υi And u ωi Is a control input; tau is υ And τ ω The delay of the drone actuator is considered.
Discretizing the model with Δ T as sampling time interval:
Figure BDA0002887791960000073
where k denotes the time k and k +1 denotes the time k + 1.
Secondly, a ground target motion model is introduced, the invention adopts the ground target motion model with speed change, and the discrete form of the model is as follows:
Figure BDA0002887791960000074
wherein the content of the first and second substances,
Figure BDA0002887791960000075
representing the state of the ground target, including its position, velocity, and acceleration; g is a transformation matrix; q (k) to N (0,Q (k)) are process noise and Q (k) is its covariance matrix.
The urban environment model is defined as:
Figure BDA0002887791960000076
wherein omega B Is the area occupied by a single building, N B Is the number of buildings, omega B ＝F((x B ,y B ),H B ) Representing the area occupied by a building as a function of the coordinates of the center and the height of its floor, Ω is the set of all buildings.
The position information of the intersection is expressed by the intersection point coordinates of the road center lines, namely:
Figure BDA0002887791960000077
wherein the content of the first and second substances,
Figure BDA0002887791960000078
coordinates representing a certain junction, N c Is the number of intersections, I represents all intersection informationA collection of information.
In order to simulate a real urban environment, a high-rise building model is added into an environment model to serve as a high-altitude barrier in the flight of an unmanned aerial vehicle, and a dark building in the figure 3 is a high-rise building and is represented as follows:
Figure BDA0002887791960000081
wherein the content of the first and second substances,
Figure BDA0002887791960000082
represents a center position coordinate above the flight height of the drone, l =1,2,. N h In the formula N h Indicating the number of high-rise buildings.
2. Based on the obstacle avoidance requirements of the unmanned aerial vehicle and the high-rise building, the switching condition of the hovering mode and the obstacle avoidance mode is provided.
When the unmanned aerial vehicle is always in a spiral state, due to the existence of high-rise buildings, the flight safety is difficult to guarantee, collision accidents are possible, the tracking precision is guaranteed on the basis that obstacles can be avoided, and therefore the tracking strategy is switched by adopting a double mode with a spiral mode as a main mode and an obstacle avoiding mode as an auxiliary mode.
1) Hover mode conditions: when the high-rise building is out of the visual range of all unmanned aerial vehicle sensors, i.e.
Figure BDA0002887791960000083
Meanwhile, all unmanned aerial vehicles are in a hovering mode;
2) Obstacle avoidance mode conditions: when the high-rise building is within a certain unmanned sensor visible range, that is to say
Figure BDA0002887791960000084
In order to meet the obstacle avoidance requirement, all unmanned aerial vehicles are in an obstacle avoidance mode.
Wherein R is B Is the minimum circumscribed circle radius of the bottom surface of the building, then
Figure BDA0002887791960000085
Represents half of the length of a side of the bottom surface of the building; r s Is the unmanned aerial vehicle sensor coverage area radius.
3. Based on a multi-objective optimization algorithm of synchronous distributed model predictive control, considering factors such as unmanned aerial vehicle state constraint and control constraint, obstacle avoidance constraint, collision avoidance constraint among unmanned aerial vehicles and the like, constructing a multi-objective optimization model under a dual mode, wherein performance indexes of a spiral mode comprise spiral distance keeping, phase angle difference keeping, speed input cost and steering rate input cost; the performance indexes of the obstacle avoidance mode comprise tracking error cost, speed input cost and steering rate input cost.
The objective is to obtain the optimal overall flight path in the prediction time domain through the airborne processor of each unmanned aerial vehicle, so that the flight path planning algorithm has foresight property and the control of each unmanned aerial vehicle has independence. The specific content of the algorithm is as follows:
a: spiral pattern
The purpose of hovering is in the control and the surveillance target, also can play the effect of escort and protection, when the visual domain within range of unmanned aerial vehicle does not have high-rise building, opens the mode of hovering. Considering that the unmanned aerial vehicles need to maintain a certain confronting distance with the target in a hovering state, and certain phase angle difference needs to be maintained between the unmanned aerial vehicles so as to keep hovering when tracking a low-speed target, two performance indexes of hovering distance keeping and phase angle difference keeping are set and are arranged in parallel at a first priority. In addition, the maintenance characteristics of the speed and the steering rate of the unmanned aerial vehicle model are considered, the speed input cost and the steering rate input cost are set, and two performance indexes in the aspect of energy conservation are arranged at the second priority.
The performance index in hover mode is as follows:
1) Spiral distance maintenance
Figure BDA0002887791960000091
Wherein the content of the first and second substances,
Figure BDA0002887791960000092
the plane distance between the k + P +1 time frame unmanned aerial vehicle i and the target obtained by k time prediction is represented, P =1,2 0 Indicating the hover distance of the drone relative to the target.
2) Phase angle difference maintenance
Figure BDA0002887791960000093
Wherein, theta ij (k + P +1k | represents a phase angle difference between the unmanned plane i and the unmanned plane j, and θ ij ＝|θ i (k+P+1|k)-θ j (k+P+1|k)|；θ 0 Indicating the desired phase angle difference.
Figure BDA0002887791960000094
Figure BDA0002887791960000095
3) Speed input cost
Figure BDA0002887791960000096
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002887791960000097
showing the cruising speed in the hover mode of the drone.
4) Steering rate input cost
Figure BDA0002887791960000098
Wherein the content of the first and second substances,
Figure BDA0002887791960000099
representing the cruise steering rate in the hover mode of the drone.
Considering firstly the tracking configuration situation between the drone and the target and drone and secondly the problem of economy reduction of the drone, two priorities are therefore defined as follows:
Figure BDA00028877919600000910
according to the state and control limit of the unmanned aerial vehicle, the requirements of collision and obstacle avoidance set state constraint and control constraint, collision avoidance constraint, compatibility constraint and obstacle avoidance constraint. The method is characterized in that collision avoidance constraints which are usually adopted are distinguished, and based on the algorithm characteristics of synchronous distributed predictive control, each unmanned aerial vehicle can only obtain state information of other unmanned aerial vehicles at the previous sampling moment at the current sampling moment, and can only take collision avoidance actions according to the prediction result at the previous moment, and uncertain errors caused by information delay need to be constrained, so that new collision avoidance constraints and compatibility constraint conditions are designed to reduce the uncertain errors and avoid collision among the unmanned aerial vehicles.
The constraints in hover mode are as follows:
1) State constraints and control constraints
The constraints on speed, steering rate and control input are:
Figure BDA0002887791960000101
Figure BDA0002887791960000102
Figure BDA0002887791960000103
Figure BDA0002887791960000104
2) Restraint of avoiding collision
Since the drone receives the location information of other drones at time k-1 at time k, the states of each drone at time k will be calculated simultaneously based on the distributed predictive control at time k. Then predicted drone position at time k-1
Figure BDA0002887791960000105
With k actual unmanned aerial vehicle position constantly
Figure BDA0002887791960000106
The uncertainty error between is:
Figure BDA0002887791960000107
then, the unmanned aerial vehicle i obtains the predicted distance between the unmanned aerial vehicles i and j according to the received position information of the unmanned aerial vehicle j at the moment k-1 as follows:
Figure BDA0002887791960000108
the boundaries of the uncertainty error are:
Figure BDA0002887791960000109
wherein the content of the first and second substances,
Figure BDA00028877919600001010
r a is the confrontation distance between the unmanned planes when the unmanned planes are in a spiral state.
The collision avoidance constraint is as follows:
Figure BDA00028877919600001011
3) Compatibility constraints
Compatibility constraints are set in order to ensure that the uncertainty error is within a certain range and gradually reduced in the rolling optimization. The maximum value of the prediction distance obtained at the moment k-1 between the unmanned aerial vehicles in the prediction time domain is as follows:
Figure BDA00028877919600001012
another boundary of uncertainty error is:
Figure BDA0002887791960000111
wherein
Figure BDA0002887791960000112
Is the desired convergence rate.
The compatibility constraints are:
Figure BDA0002887791960000113
b: obstacle avoidance mode
Keep away the aim at of barrier mode and guarantee to track precision and unmanned aerial vehicle flight safety, when having high-rise building thing when the visual domain within range of unmanned aerial vehicle, open and keep away the barrier mode. To ensure high accuracy tracking, a tracking error cost performance indicator is set and arranged at a first priority. In addition, considering the energy-saving requirement, two performance indexes of speed input cost and steering rate input cost are set and arranged at the second priority.
The performance indexes in the obstacle avoidance mode are as follows:
1) Tracking error cost
Figure BDA0002887791960000114
2) Speed input cost
Figure BDA0002887791960000115
Wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0002887791960000116
and the cruising speed of the unmanned aerial vehicle in the obstacle avoidance mode is shown.
3) Steering rate input cost
Figure BDA0002887791960000117
Wherein the content of the first and second substances,
Figure BDA0002887791960000118
and the cruise steering rate in the unmanned plane obstacle avoidance mode is represented.
Two priorities are defined as follows:
Figure BDA0002887791960000119
according to the state and control input limit of the unmanned aerial vehicle, the requirements of collision and obstacle avoidance set state constraint and control constraint, collision avoidance constraint, compatibility constraint and obstacle avoidance constraint. The constraints are as follows:
1) State constraints and control constraints
The constraints on speed, steering rate and control input are:
Figure BDA0002887791960000121
Figure BDA0002887791960000122
Figure BDA0002887791960000123
Figure BDA0002887791960000124
2) Restraint of collision avoidance
The bounds of uncertainty error are:
Figure BDA0002887791960000125
wherein the content of the first and second substances,
Figure BDA0002887791960000126
r b = L is the stand-off distance between drones when tracking.
The collision avoidance constraint is as follows:
Figure BDA0002887791960000127
3) Compatibility constraints
Figure BDA0002887791960000128
4) Obstacle avoidance restraint
Figure BDA0002887791960000129
L/2 is the safe radius of the unmanned aerial vehicle, and when the unmanned aerial vehicle keeps a safe distance from a high-rise building, the unmanned aerial vehicle can bypass the obstacle.
4. A fuzzy satisfaction target optimization method is adopted, the performance index is converted into a fuzzy membership function form, a priority model is established based on a relaxation satisfaction sequence method, and a generalized fuzzy satisfaction target planning model is obtained.
Based on a traditional target planning model, requirements for performance indexes are abstracted into a form of a membership function through a fuzzification means, and when deviation between a target function and an expected value is minimum, an optimal solution is obtained. However, the strict priority model established in the foregoing may cause difficulty in finding the optimal solution, so that the priority relationship is relaxed based on the relaxed satisfaction degree method, and the performance requirements and the priority requirements are balanced as much as possible. The method comprises the following specific contents:
the traditional target planning model is:
Figure BDA00028877919600001210
Figure BDA00028877919600001211
n e ,p e ≥0,n e ·p e ＝0
w∈G e
wherein p is e ,n e Respectively, positive and negative offset variables, m is the number of performance indicators, G e Is a domain of definition.
Then the three fuzzy relations are:
blur is less than
Figure BDA0002887791960000131
Figure BDA0002887791960000132
Figure BDA0002887791960000133
Blur greater than
Figure BDA0002887791960000134
Figure BDA0002887791960000135
Figure BDA0002887791960000136
Blur is equal to
Figure BDA00028877919600001311
Figure BDA0002887791960000137
Figure BDA0002887791960000138
Assume that the priority relationship is f d (w)≥f s (w)≥f t (w) establishing a priority model based on a relaxation satisfaction degree sequence method:
Figure BDA0002887791960000139
Figure BDA00028877919600001310
the generalized fuzzy satisfaction target planning model is as follows:
Figure BDA0002887791960000141
Figure BDA0002887791960000142
Figure BDA0002887791960000143
Figure BDA0002887791960000144
Figure BDA0002887791960000145
Figure BDA0002887791960000146
Figure BDA0002887791960000147
Figure BDA0002887791960000148
p d ,n s ,n t ,p t ≥0,n t ·p t ＝0
-1≤γ≤1,w∈G e
wherein gamma is an unknown variable, -1 is more than or equal to gamma and less than or equal to 0, which indicates that the priority relationship is satisfied, otherwise, the priority relationship is not satisfied; λ is an adjustable parameter, with increasing values representing more emphasis on priority differences.
5. And constructing a multi-unmanned aerial vehicle collaborative track planning model under the dual modes based on a synchronous distributed model predictive control and fuzzy target planning method by combining an unmanned aerial vehicle model, a performance index function, constraint conditions and priority requirements.
The fuzzy performance index function and priority under the dual mode are as follows:
a: spiral pattern
The performance index in hover mode is as follows:
1) Spiral distance maintenance
Figure BDA0002887791960000149
Figure BDA00028877919600001410
Figure BDA00028877919600001411
Wherein R is v Is the radius of the field of view of the sensor.
2) Phase angle difference maintenance
Figure BDA00028877919600001412
Figure BDA00028877919600001413
Figure BDA00028877919600001414
3) Speed input cost
Figure BDA0002887791960000151
Figure BDA0002887791960000152
Figure BDA0002887791960000153
4) Steering rate input cost
Figure BDA0002887791960000154
Figure BDA0002887791960000155
Figure BDA0002887791960000156
Based on the relaxed satisfaction order, the priority model is:
Figure BDA0002887791960000157
Figure BDA0002887791960000158
Figure BDA0002887791960000159
Figure BDA00028877919600001510
the multi-unmanned aerial vehicle collaborative track planning model in the hovering mode is as follows:
Figure BDA0002887791960000161
Figure BDA0002887791960000162
Figure BDA0002887791960000163
Figure BDA0002887791960000164
Figure BDA0002887791960000165
Figure BDA0002887791960000166
n 1 ≤r 0 -0,p 1 ≤R v -r 0 ,p 2 ≤1-cosθ 0
Figure BDA0002887791960000167
n 1 ·p 1 ＝0,n 3 ·p 3 ＝0,n 4 ·p 4 ＝0；n 1 ,p 1 ,p 2 ,n 3 ,p 3 ,n 4 ,p 4 ≥0；-1≤η≤1
x i (k|k)＝x i (k),y i (k|k)＝y i (k),ψ i (k|k)＝ψ i (k),υ i (k|k)＝υ i (k),ω i (k|k)＝ω i (k)
Figure BDA0002887791960000168
Figure BDA0002887791960000169
Figure BDA00028877919600001610
Figure BDA00028877919600001611
Figure BDA00028877919600001612
P＝0,1,..H-1；i,j＝1,2,..N,i≠j；l＝1,2,..N h
b: obstacle avoidance mode
The performance indexes in the obstacle avoidance mode are as follows:
1) Tracking error cost
Figure BDA00028877919600001613
Figure BDA00028877919600001614
Figure BDA00028877919600001615
2) Speed input cost
Figure BDA0002887791960000171
Figure BDA0002887791960000172
Figure BDA0002887791960000173
3) Steering rate input cost
Figure BDA0002887791960000174
Figure BDA0002887791960000175
Figure BDA0002887791960000176
Based on the relaxation satisfaction order, the priority model is:
Figure BDA0002887791960000177
Figure BDA0002887791960000178
the multi-unmanned aerial vehicle collaborative track planning model in the obstacle avoidance mode is as follows:
Figure BDA0002887791960000181
Figure BDA0002887791960000182
Figure BDA0002887791960000183
Figure BDA0002887791960000184
Figure BDA0002887791960000185
n 6 ·p 6 ＝0,n 7 ·p 7 ＝0；p 5 ,n 6 ,p 6 ,n 7 ,p 7 ≥0；-1≤η≤1
x i (k|k)＝x i (k),y i (k|k)＝y i (k),ψ i (k|k)＝ψ i (k),υ i (k|k)＝υ i (k),ω i (k|k)＝ω i (k)
Figure BDA0002887791960000186
Figure BDA0002887791960000187
Figure BDA0002887791960000188
Figure BDA0002887791960000189
Figure BDA00028877919600001810
Figure BDA00028877919600001811
P＝0,1,..H-1；i,j＝1,2,..N,i≠j；l＝1,2,..N h
6. and designing a rolling solving step of the optimization algorithm based on a nonlinear programming method.
The concrete rolling solving steps are as follows:
step 1: at a sampling moment k, judging the flight mode of the unmanned aerial vehicle cluster according to the distance between the unmanned aerial vehicle and a high-rise building, if the flight mode is a hover mode, jumping to Step2, and if the flight mode is an obstacle avoidance mode, jumping to Step3;
step 2: establishing a multi-unmanned aerial vehicle collaborative track planning model in a hovering mode, solving an optimization result based on a nonlinear programming algorithm, and jumping to Step4;
step 3: establishing a multi-unmanned aerial vehicle collaborative track planning model in an obstacle avoidance mode, and solving an optimization result based on a nonlinear programming algorithm;
step 4: applying the first item of the optimal control sequence to the corresponding unmanned aerial vehicle; the optimal control sequence is as follows:
u υi (k+P|k),u ωi (k+P|k)
step5: and (4) sending the predicted track of the current unmanned aerial vehicle at the moment k to other unmanned aerial vehicles, and turning to Step 1 at the moment k +1 until the tracking task is finished.
According to the embodiment of the invention, the processes of model establishment, algorithm verification and the like are completed through program writing, and the effectiveness of the dual-mode unmanned aerial vehicle collaborative track planning algorithm for spirally tracking the ground target is tested through a large amount of data and image processing. In the invention, the test is carried out by using two unmanned aerial vehicles as backgrounds for tracking the same ground target. After the data processing is completed, the results are shown in fig. 3.
Simulation results show that the algorithm has small error, high reliability and good safety when multiple unmanned aerial vehicles cooperatively track the ground low-speed target, and can complete a high-quality tracking task. Therefore, the dual-mode multi-unmanned aerial vehicle collaborative track planning algorithm for hovering and tracking the ground target has feasibility. The method ensures tracking accuracy in a dual-mode, considers the problems that unmanned aerial vehicles avoid each other and high-level obstacles, the unmanned aerial vehicles save energy and the like, and can be used for tracking ground low-speed moving targets in complex environments and the like.
The present invention is not limited to the above-described embodiments. The foregoing description of the specific embodiments is intended to describe and illustrate the technical solutions of the present invention, and the above specific embodiments are merely illustrative and not restrictive. Those skilled in the art can make various changes in form and details without departing from the spirit and scope of the invention as defined by the appended claims.

Claims (4)

1. The method for planning the cooperative flight path of the multiple unmanned aerial vehicles for hovering and tracking the ground target in the dual mode comprises the following steps:
constructing an unmanned aerial vehicle kinematic model, a ground target motion model and a three-dimensional city environment model by taking ground target tracking of multiple unmanned aerial vehicles for tracking low-speed movement in a city as a background;
providing a switching condition of a hovering mode and an obstacle avoidance mode based on the obstacle avoidance requirements of the unmanned aerial vehicle and the high-rise building;
constructing a multi-objective optimization model under a dual mode based on a multi-objective optimization algorithm of synchronous distributed model predictive control;
establishing a priority model based on a relaxation satisfaction degree sequence method to obtain a generalized fuzzy satisfaction degree target planning model;
constructing a multi-unmanned aerial vehicle collaborative track planning model under a dual mode based on a synchronous distributed model predictive control and fuzzy target planning method; wherein: the multi-unmanned aerial vehicle collaborative track planning model in the dual mode respectively comprises a multi-unmanned aerial vehicle collaborative track planning model in a hovering mode and a multi-unmanned aerial vehicle collaborative track planning model in an obstacle avoidance mode;
the multi-unmanned aerial vehicle collaborative track planning model in the hovering mode is as follows:
Figure FDA0003983679760000011
Figure FDA0003983679760000012
Figure FDA0003983679760000013
Figure FDA0003983679760000014
Figure FDA0003983679760000015
Figure FDA0003983679760000016
n 1 ≤r 0 -0,p 1 ≤R v -r 0 ,p 2 ≤1-cosθ 0
Figure FDA0003983679760000017
n 1 ·p 1 ＝0,n 3 ·p 3 ＝0,n 4 ·p 4 ＝0；n 1 ,p 1 ,p 2 ,n 3 ,p 3 ,n 4 ,p 4 ≥0；-1≤η≤1
x i (k|k)＝x i (k),y i (k|k)＝y i (k),ψ i (k|k)＝ψ i (k),υ i (k|k)＝υ i (k),ω i (k|k)＝ω i (k)
Figure FDA0003983679760000018
Figure FDA0003983679760000019
Figure FDA00039836797600000110
Figure FDA00039836797600000111
Figure FDA00039836797600000112
P＝0,1,..H-1；i,j＝1,2,..N,i≠j；l＝1,2,..N h
wherein:
Figure FDA0003983679760000021
is the three-dimensional position coordinate of drone i, z i Indicating the flight altitude; psi i Is the course angle; upsilon is i And ω i Speed and steering rate, respectively; u. of υi And u ωi Is a control input; gamma is an unknown variable, -1 is more than or equal to gamma and less than or equal to 0, the priority relation is satisfied, otherwise, the priority relation is not satisfied; λ is an adjustable parameter, with increasing values representing more emphasis on priority differences; Δ T is the sampling time interval;
Figure FDA0003983679760000022
obtaining the predicted distance between the unmanned aerial vehicles i and j for the unmanned aerial vehicle i according to the received position information of the unmanned aerial vehicle j at the moment k-1;
Figure FDA0003983679760000023
predicting drone position for k-1 time
Figure FDA0003983679760000024
With k actual unmanned aerial vehicle position constantly
Figure FDA0003983679760000025
Uncertainty error between;
Figure FDA0003983679760000026
Figure FDA0003983679760000027
is the boundary of the uncertainty error; wherein:
Figure FDA0003983679760000028
Figure FDA0003983679760000029
r a is the confrontation distance between the unmanned planes when the unmanned planes are in a spiral state,
Figure FDA00039836797600000210
is the desired convergence rate;
the performance index in hover mode is as follows:
1) Spiral distance maintenance
Figure FDA00039836797600000211
Figure FDA00039836797600000212
Figure FDA00039836797600000213
Wherein R is v Is the radius of the sensor field of view;
Figure FDA00039836797600000214
the plane distance between the unmanned aerial vehicle i and the target at the k + P +1 moment predicted at the k moment is represented, P =1,2 0 Indicating the hovering distance of the drone relative to the target;
2) Phase angle difference maintenance
Figure FDA0003983679760000031
Figure FDA0003983679760000032
Figure FDA0003983679760000033
Wherein, theta ij (k + P + 1|k) represents the phase angle difference between drone i and drone j, θ ij ＝|θ i (k+P+1|k)-θ j (k+P+1|k)|；θ 0 Representing a desired phase angle difference;
3) Speed input cost
Figure FDA0003983679760000034
Figure FDA0003983679760000035
Figure FDA0003983679760000036
Wherein the content of the first and second substances,
Figure FDA0003983679760000037
representing the cruising speed of the unmanned aerial vehicle in the hovering mode;
4) Steering rate input cost
Figure FDA0003983679760000038
Figure FDA0003983679760000039
Figure FDA00039836797600000310
Wherein the content of the first and second substances,
Figure FDA00039836797600000311
representing the cruise steering rate in the hovering mode of the unmanned aerial vehicle;
based on the relaxation satisfaction order, the priority model is:
Figure FDA0003983679760000041
Figure FDA0003983679760000042
Figure FDA0003983679760000043
Figure FDA0003983679760000044
the constraints in hover mode are as follows:
1) State constraints and control constraints
The constraints on speed, steering rate and control input are:
Figure FDA0003983679760000045
Figure FDA0003983679760000046
Figure FDA0003983679760000047
Figure FDA0003983679760000048
2) Restraint of avoiding collision
Figure FDA0003983679760000049
3) Compatibility constraints
Setting compatibility constraint to ensure that the uncertain error is within a certain range and gradually reduced in the rolling optimization;
Figure FDA00039836797600000410
the multi-unmanned aerial vehicle collaborative track planning model in the obstacle avoidance mode comprises the following steps:
Figure FDA0003983679760000051
Figure FDA0003983679760000052
Figure FDA0003983679760000053
Figure FDA0003983679760000054
Figure FDA0003983679760000055
n 6 ·p 6 ＝0,n 7 ·p 7 ＝0；p 5 ,n 6 ,p 6 ,n 7 ,p 7 ≥0；-1≤η≤1
x i (k|k)＝x i (k),y i (k|k)＝y i (k),ψ i (k|k)＝ψ i (k),υ i (k|k)＝υ i (k),ω i (k|k)＝ω i (k)
Figure FDA0003983679760000056
Figure FDA0003983679760000057
Figure FDA0003983679760000058
Figure FDA0003983679760000059
Figure FDA00039836797600000510
Figure FDA00039836797600000511
P＝0,1,..H-1；i,j＝1,2,..N,i≠j；l＝1,2,..N h
wherein r is b = L is the confrontation distance between unmanned aerial vehicles during tracking; l/2 is the safe radius of the unmanned plane; r is B Is the minimum circumscribed circle radius of the building floor;
the performance indexes in the obstacle avoidance mode are as follows:
1) Tracking error cost
Figure FDA00039836797600000512
Figure FDA00039836797600000513
Figure FDA00039836797600000514
2) Speed input cost
Figure FDA00039836797600000515
Figure FDA00039836797600000516
Figure FDA00039836797600000517
Wherein the content of the first and second substances,
Figure FDA0003983679760000061
representing the cruising speed of the unmanned aerial vehicle in an obstacle avoidance mode;
3) Steering rate input cost
Figure FDA0003983679760000062
Figure FDA0003983679760000063
Figure FDA0003983679760000064
Wherein the content of the first and second substances,
Figure FDA0003983679760000065
the cruise steering rate of the unmanned aerial vehicle in the obstacle avoidance mode is represented;
based on the relaxation satisfaction order, the priority model is:
Figure FDA0003983679760000066
Figure FDA0003983679760000067
the constraints are as follows:
1) State constraints and control constraints
The constraints on speed, steering rate and control input are:
Figure FDA0003983679760000068
Figure FDA0003983679760000069
Figure FDA00039836797600000610
Figure FDA00039836797600000611
2) Restraint of avoiding collision
Figure FDA00039836797600000612
Wherein, the first and the second end of the pipe are connected with each other,
Figure FDA00039836797600000613
3) Compatibility constraints
Figure FDA00039836797600000614
4) Obstacle avoidance restraint
Figure FDA00039836797600000615
2. The dual-mode multi-drone collaborative flight path planning method for hovering tracking of a ground target according to claim 1, characterized in that: providing a switching condition of a hovering mode and an obstacle avoidance mode based on the obstacle avoidance requirements of the unmanned aerial vehicle and the high-rise building; wherein:
1) Hover mode conditions: when the high-rise building is out of the visual range of all unmanned aerial vehicle sensors, i.e.
Figure FDA0003983679760000071
When the unmanned aerial vehicle is in the hovering mode, all the unmanned aerial vehicles are in the hovering mode;
2) Obstacle avoidance mode conditions: when the high-rise building is within a certain unmanned sensor visible range, that is to say
Figure FDA0003983679760000072
In order to meet the obstacle avoidance requirement, all unmanned aerial vehicles are in an obstacle avoidance mode;
wherein, the first and the second end of the pipe are connected with each other,
Figure FDA0003983679760000073
represents half of the length of a side of the bottom surface of the building; r s Is the unmanned aerial vehicle sensor coverage area radius.
3. The dual-mode multi-drone collaborative flight path planning method for hovering tracking of a ground target according to claim 1, characterized in that: constructing a multi-objective optimization model under a dual mode based on a multi-objective optimization algorithm of synchronous distributed model predictive control; wherein:
a: spiral pattern
Setting two performance indexes of spiral distance keeping and phase angle difference keeping, and arranging the two performance indexes in parallel at a first priority; in addition, considering the maintenance characteristics of the speed and the steering rate of the unmanned aerial vehicle model, setting speed input cost and steering rate input cost, and arranging two performance indexes in the aspect of energy saving at a second priority;
according to unmanned aerial vehicle's state and control restriction, the demand of keeping away to bump and keeping away the barrier sets up state restraint and control restraint, keeps away to bump restraint, compatibility restraint and keeps away the barrier restraint, wherein: the method is characterized in that collision avoidance constraints which are usually adopted are distinguished, on the basis of the algorithm characteristics of synchronous distributed prediction control, each unmanned aerial vehicle can only acquire state information of other unmanned aerial vehicles at the previous sampling moment at the current sampling moment, and can only take collision avoidance actions according to the prediction result at the previous moment, so that uncertain errors caused by information delay are restrained, and the uncertain errors are reduced through the collision avoidance constraints and compatibility constraint conditions;
b: obstacle avoidance mode
In order to ensure high-precision tracking, a tracking error cost performance index is set and arranged at a first priority; in addition, considering the energy-saving requirement, two performance indexes of speed input cost and steering rate input cost are set and arranged at the second priority.
4. The dual-mode multi-drone collaborative flight path planning method for hovering tracking of a ground target according to claim 1, characterized in that: the condition for switching between the hover mode and the obstacle avoidance mode is a rolling solving process optimized based on a nonlinear programming method, and the method comprises the following steps of:
step 1: at a sampling moment k, judging the flight mode of the unmanned aerial vehicle cluster according to the distance between the unmanned aerial vehicle and a high-rise building, if the flight mode is a hover mode, jumping to Step2, and if the flight mode is an obstacle avoidance mode, jumping to Step3;
step 2: establishing a multi-unmanned aerial vehicle collaborative track planning model in a hovering mode, solving an optimization result based on a nonlinear programming algorithm, and jumping to Step4;
step 3: establishing a multi-unmanned aerial vehicle collaborative track planning model in an obstacle avoidance mode, and solving an optimization result based on a nonlinear programming algorithm;
step 4: applying the first item of the solved optimal control sequence to the corresponding unmanned aerial vehicle; the optimal control sequence is as follows:
u υi (k+P|k),u ωi (k+P|k)
step5: and (4) sending the predicted track of the current unmanned aerial vehicle at the moment k to other unmanned aerial vehicles, and turning back to Step 1 at the moment k +1 until the tracking task is finished.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108536171A (en) * 2018-03-21 2018-09-14 电子科技大学 The paths planning method of multiple no-manned plane collaboration tracking under a kind of multiple constraint
CN109976386A (en) * 2019-04-04 2019-07-05 中国电子科技集团公司信息科学研究院 A kind of method and system of multiple no-manned plane collaboration tracking target
CN110262563A (en) * 2018-05-23 2019-09-20 中国海洋大学 Multiple no-manned plane collaboratively searching mesh calibration method waterborne
CN110412559A (en) * 2019-07-26 2019-11-05 西安电子科技大学 The non-coherent of distributed unmanned plane MIMO radar merges object detection method
CN110908395A (en) * 2019-11-26 2020-03-24 西北工业大学 Improved unmanned aerial vehicle flight path real-time planning method

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US9557738B2 (en) * 2015-04-21 2017-01-31 Gopro, Inc. Return path configuration for remote controlled aerial vehicle
CN108762286A (en) * 2018-05-31 2018-11-06 智飞智能装备科技东台有限公司 A kind of ground control system for the control that can fly to multiple UAVs

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108536171A (en) * 2018-03-21 2018-09-14 电子科技大学 The paths planning method of multiple no-manned plane collaboration tracking under a kind of multiple constraint
CN110262563A (en) * 2018-05-23 2019-09-20 中国海洋大学 Multiple no-manned plane collaboratively searching mesh calibration method waterborne
CN109976386A (en) * 2019-04-04 2019-07-05 中国电子科技集团公司信息科学研究院 A kind of method and system of multiple no-manned plane collaboration tracking target
CN110412559A (en) * 2019-07-26 2019-11-05 西安电子科技大学 The non-coherent of distributed unmanned plane MIMO radar merges object detection method
CN110908395A (en) * 2019-11-26 2020-03-24 西北工业大学 Improved unmanned aerial vehicle flight path real-time planning method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
Distributed synchronous cooperative tracking algorithm for ground moving target in urban by UAVs;Chaofang Hu , et al;《International Journal of Systems Science》;20201108;第1-16页 *
多无人机模糊多目标分布式地面目标协同追踪;胡超芳等;《控 制 理 论 与 应 用》;20180831;第1101-1110页 *

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