Design of UAV Cooperative Countermeasure Decision System

Yuhang Hui, Lei Li, Weixiu Yang, Xinguang Zhang and Ruiqi Sun

Beijing Institute of Space Long March Vehicle, China

Keywords: UAV Formation, Cooperative Confrontation, Task Allocation, Trajectory Planning.

Abstract: This paper designs a UAV Cooperative Countermeasure Decision System. The system focuses on the

effective integration of key technologies and algorithms such as multi-aircraft cooperative task assignment,

multi-aircraft cooperative track planning, and multi-aircraft cooperative formation control, and its

effectiveness is verified through digital simulation. This paper mainly establishes a multi-machine

cooperative task allocation algorithm based on satisfactory decision theory to achieve near-optimal task

allocation among multiple UAVs under various mission and resource constraints; a UAV path planning

algorithm based on a fast heuristic search strategy realizes fast and efficient UAV path planning and real-

time path re-planning in a dynamic environment; a multi-machine cooperative formation flight and

reconfiguration control algorithm based on induced routes is used to realize UAV formation flight formation

control; finally, the feasibility of the key technologies and algorithms of the system is verified through

visual simulation.

INTRODUCTION

With the rapid development of UAV technology,

multi-UAV cooperative combat has become one of

the important forms of combat in the future. In the

cooperative combat of multiple UAVs, it is very

important to effectively coordinate and control the

task execution and flight path planning of multiple

UAVs to improve operational efficiency.

In the past, UAV's task assignment and flight

path planning have been widely studied. In the field

of task allocation, experts and scholars have put

forward centralized task allocation mathematical

models such as multi-traveling salesman problem

(Secreat B R, 2001), vehicle routing problem

(O'Rourke K P, 2001), multi-choice knapsack

problem (Li Xiangmin, 2014), mixed integer linear

programming problem (An S, 2014), dynamic

network flow optimization model (Zhu D, 2013),

and distributed task allocation models such as multi-

agent decision theory and market mechanism

distributed Markov distribution constraints. In the

field of route planning, algorithms such as optimal

control method, roadmap method, grid method and

artificial potential field method are also proposed.

However, there are relatively few comprehensive

studies on cooperative task assignment, flight path

planning and formation control of multi-UAVs.

Therefore, in view of the cooperative confrontation

scenario of UAV formation, this paper designs a

UAV Cooperative Countermeasure Decision System

to support multi-UAV rative operations, and focuses

on the key technologies and algorithms such as multi

- UAV cooperative task assignment, multi - UAV

cooperative flight path planning and multi - UAV

cooperative formation control.

UAV COOPERATIVE

COUNTERMEASURE

DECISION SYSTEM

The UAV cooperative countermeasure decision

system aims to support multi-UAV cooperative

operations, to achieve effective coordination and

control among multiple UAVs, and to improve

operational efficiency and mission execution ability.

2.1 System Architecture

The UAV cooperative countermeasure decision

system mainly includes four modules, and each

module has the following functions

Task assignment module: responsible for

establishing the mathematical model of multi-

aircraft cooperative task assignment according to the

types and requirements of combat tasks, including

cost model and constraint model. Using the

algorithm based on satisfactory decision theory, the

Hui, Y., Li, L., Yang, W., Zhang, X. and Sun, R.

Design of UAV Cooperative Countermeasure Decision System.

DOI: 10.5220/0012274700003807

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Seminar on Artiﬁcial Intelligence, Networking and Information Technology (ANIT 2023), pages 93-99

ISBN: 978-989-758-677-4

approximate optimal task assignment of multiple

UAVs under different task and resource constraints

is realized.

Path planning module: Considering the high

dynamic and real-time requirements of UAV

cooperative operations, an UAV path planning

algorithm based on fast heuristic search strategy is

established, which can meet the constraints of threat

avoidance, platform performance, system resources

and mission requirements. The module can realize

fast and efficient path planning and real-time path

re-planning of UAV in dynamic environment.

Formation control module: aiming at improving

the cooperative penetration efficiency and task

execution efficiency of UAV, a multi-aircraft

cooperative formation flight and reconfiguration

control algorithm based on guidance routes is

established. According to the actual flight state and

the formation configuration error between UAVs,

different formation control strategies and methods

are adopted to realize rapid formation assembly,

synchronization and formation.

Simulation and verification module: this module

is used to verify the key technologies and algorithms

of the system. Collaborative countermeasure

strategy can be generated for different mission

scenarios and visual simulation can be realized.

Task allocation module

Path planning module

Formation control

module

Simulation verification

module

allocating task

Generating path

Figure 1: Framework of UAV countermeasure decision

system.

2.2 Multi-UAV Cooperative Task

Assignment

In this paper, a multi-machine cooperative target

assignment algorithm

(

Liao Mo, 2007

)

is adopted, and the

system framework of the algorithm is shown in the

figure, which consists of multi-machine target

assigners and individual UAVs. The part contained

in the oval virtual frame belongs to the management

decision-making part of UAV, which is generally

realized by UAV ground control unit.

The single-machine target manager receives the

attack target distributed by the multi-machine target

distributor, and sends this task target to the path

planner, who plans the path to execute the task; At

the same time, the single-machine target manager is

also responsible for providing a group of satisfactory

UAV individuals for the multi-machine target

distributor, and allocating parameters for each attack

target, such as path length, path threat, attack

efficiency, attack threat, etc. The calculation of these

parameters requires calling the path planner and

evaluation module.

Multi-UAV target distributor

Single UAV target

manager

Path

planner

Evaluati

module

UAV and control

navigation system

…………

…………UAV_2

UAV_N

goal set

Allocation

parameter

UAV_1

target

Path parame ter

Figure 2: Framework of Multi-machine Collaborative

Task Allocation System

The multi-machine target allocator sends the

target set and parameters to be allocated to each

single machine target manager, calculates the global

optimal allocation scheme according to the

satisfactory set and allocation parameters provided

by each single machine, determines the targets to be

attacked by each single machine, and then delegates

them to the single machine for execution.

The evaluation module of single machine gives

the parameters of UAV's destruction probability,

danger probability and so on. The path planner

calculates the flight path according to the mission

objectives and battlefield conditions of the single

aircraft, and gives the path length and threat cost.

The influence of various threats such as radar,

weapons, no-fly zone, electromagnetic zone and bad

weather zone on the battlefield is considered, and the

planned route, corresponding path length and threat

are given.

ANIT 2023 - The International Seminar on Artiﬁcial Intelligence, Networking and Information Technology

2.3 Multi-UAV Cooperative Path

Planning

Each plane corresponds to a path planner. The

coordination and control system of the flight

detachment is a large-scale system composed of

corresponding small aircraft systems, and the key to

its coordination is to determine the best time for the

detachment to reach the attack target. The solution to

this large-scale system is generally to adopt the

distributed control method of decomposition and

coordination, decompose the corresponding

problems in layers, let the planners of each aircraft

carry out relevant calculations, and use an aircraft

computer or assign a computer to undertake the

coordinated calculation task. The strategy of

decomposition is to select coordination variables and

determine coordination functions.

Take the coordinate variable ETA (Estimated

Time until Arrival), and record it as t

. The

coordination is the comprehensive cost of the team

(including fuel consumption and threat cost), with

the aim of minimizing coordination.

Suppose there are n planes attacking the same

target. For the No. i plane, there are.

Fuel cost:

𝐽



= 𝐶



𝐿



(1)

Threat cost:

𝐽



= 𝐶



















+ 𝐶





𝐿

ℎ𝑖

𝑣

𝑖



(2)

Comprehensive cost:

𝐽



= 𝐽



+ 𝐽

𝑡𝑖

(3)

Where:

L is the flight path length,

L is the

path length of the aircraft passing through the high

threat area, and

v is the aircraft speed.

is the

weighting factor of fuel cost,

C is the weighting

factor of path cost in low threat area and

C is the

weighting factor of path cost in high threat area.

The comprehensive cost of the team

𝐽=

∑

𝐽







∑

𝐶



𝐿



+ 𝐶



















+ 𝐶





















(4)

And satisfy the constraint conditions:

𝑣

, 

≤𝑣



≤𝑣

,

𝐽



≤𝐹



𝐽



≤𝑇h



(5)

𝑡



≤𝑇



Where F



is the fuel limit, Th



is the acceptable

threshold of aircraft danger, and T



is the minimum

time limit for completing the attack mission.

Co or di nation mo dule

Coordination layer

Co or dinated co mpu ting modul e

Coordinated computing moduleInitial calculation module

Initial calculation module

Offline planning layer

Figure 3: Hierarchical structure of multi-UAV coordinated

path planning.

The hierarchical decomposition coordination

structure of flight path coordination planning

process is shown in Figure 3, which can be divided

into superior coordination layer and lower path

planning layer. Where 𝑆

,

is the destination arrival

time set of the num.𝑖 plane.

The next calculation of hierarchical planning can

be put into the path planner of each UCAV, and the

calculation can be divided into two modules: initial

calculation module and coordinated calculation

module.

The multi-machine coordinated path planning

algorithm comprises the following steps:

The initial calculation module carries out path

planning according to the fuel limit F



, the

acceptable threshold of danger Th



and the minimum

time T



to complete the attack task given by the

superior S

,

, and calculates the arrival time set of

the corresponding aircraft according to different

flight speeds to get the corresponding cost J



The coordination module of the coordination

layer determines the coordination variables

according to the arrival time set of each aircraft

participating in the assembly S

,

and the

corresponding cost J



, and according to the principle

of meeting the minimum total comprehensive cost



The coordination variables t



are sent to the

lower planning layer, and the coordination

calculation module in the planning layer plans the

flight route of the corresponding aircraft and

calculates the corresponding flight speed and

minimum comprehensive cost.

Design of UAV Cooperative Countermeasure Decision System

Have an

impact?

End

Module for receive co mmand s a nd chan

environment information

Check the influence of

changing environment on the

original route.

Path planning module

Speed re gulating

module

Relationship betwee n

arriva l ti me and limited

time

Planning results

Send it to the superior task

planner

And the lower pilot

Hover planning

module

The limited arrival time is too long.

Require accurate arrival

Meet the time requirement

Not enough time

Relevant information is sent to the

superior task planner.

(Path len gt h, crossing time an d distance,

minimum required time)

Figure 4: Coordinated path planner algorithm flow.

When it is necessary to re-coordinate the multi-

aircraft path planning due to environmental changes,

if the planned route of an aircraft cannot meet the

constraints, it should be chosen to give up

participating in the multi-aircraft assembly and

return to a designated waypoint instead.

If the minimum ETA of the 𝑁𝑜. 𝑖 plane is 𝑡



the ETA time of the team 𝑡



is:

𝑡



= 𝑚𝑎𝑥{𝑡



(

𝑖

)



(6)

2.4 Multi-Aircraft Cooperative

Formation Flight

This paper adopts a multi-UAV formation control

method based on induced route (

WU, 2016),

assuming that each UAV knows its expected

position in the formation before the formation task

begins. In the formation mode, the captain obtains

the expected route information and tracks the flight

of the route, and the wingman calculates his own

control instructions through the state of the captain

and the expected position information of the local

plane.

In the formation control algorithm based on

guidance route, the wingman's own control

command is a local guidance route, and the

wingman can track its expected position by tracking

this guidance route in real time. The expected

position of wingman in the formation can be

described by the lateral distance 𝐷



, forward

distance 𝐷



and height difference 𝐷



of the

relatively long aircraft, which is positive to the right

and backward, as shown in Figure 6.

长机

僚机僚机

lead aircraft

wing

plane

wing

plane

Figure 5: Coordinated path planner algorithm flow.

Figure 6: UAV expected position description.

According to the difference of position error

between the current position of wingman and its

expected position, different strategies are adopted to

generate the guidance route to realize the UAV

approaching its expected position quickly. When the

wingman is far away from its expected position, the

guidance route starts from the current position of the

wingman and ends at the expected position (as

shown in Figure 7.a), so that the wingman can fly to

the expected position quickly; When the wingman is

close to its expected position, the guidance route

passes through the expected position of the wingman

and is parallel to the current heading of the leader

(as shown in Figure 7.b), so as to guide the wingman

to approach the expected position smoothly. The

relay switching mode is adopted for the switching of

the two guidance routes, that is, the position error of

ANIT 2023 - The International Seminar on Artiﬁcial Intelligence, Networking and Information Technology

the wingman when switching from the oriented

guidance route to the parallel guidance route is

smaller than that when switching from the parallel

guidance route to the oriented guidance route.

Leader

Wingman

wingman desired

position

(a) towards the guidance route

Leader

Wingman

wingman desired

position

(b) Parallel induced routes

Figure 7: Guidance route generation strategy.

Suppose the current position of wingman is

(

𝑥



, 𝑦



, ℎ



)

, its speed and heading angle are (𝑣



, 𝜑



its expected position and speed are (𝑥



, 𝑦



, ℎ



, 𝑣



the current position of leader is (𝑥



, 𝑦



, ℎ



), and its

speed and heading angle are

(

𝑣



, 𝜑



)

. Then the

coordinates and speeds of two reference points on

the guidance route are calculated as follows



𝑥



= 𝑥



+ 𝑑



(𝑥



−𝑥



)+𝛥𝑙𝑐𝑜𝑠( 𝜑



)

𝑦



= 𝑦



+ 𝑑



(𝑦



−𝑦



)+𝛥𝑙𝑠𝑖𝑛( 𝜑



)

ℎ



= ℎ



𝑣



= 𝑣



(7)



𝑥



= 𝑥



+ 𝛥𝑙𝑐𝑜𝑠( 𝜑



)

𝑦



= 𝑦



+ 𝛥𝑙𝑠𝑖𝑛( 𝜑



)

ℎ



= ℎ



+ 𝑘



(ℎ



−ℎ



)

𝑣



= 𝑣



(8)

Where the fixed distance 𝛥𝑙 between two

reference points is long enough, and 𝑑



is a variable

with a value between 0 and 1, whose value

determines the position of the guidance route, and

the guidance route can be translated between the

current position and the expected position of the

wingman by changing the value of 𝑑



. During the

formation of the formation, the value of the 𝑑



Waypoint 2

wingman

desired

position

Wingman

induction

position

Wingman's

actual location

Leader

Figure 8: Generation principle of parallel induced route.

gradually increased from 0 to 1, guiding the

wingman to gradually approach its expected

position. The heading angle of UAV is defined as 0

in the east direction and positive in the

counterclockwise direction. The expected position of

wingman (𝑥



, 𝑦



, ℎ



) can be described by the

expected relative distance from the leader:



𝑥



= 𝑥



+ 𝐷



𝑠𝑖𝑛( 𝜑



) −𝐷



𝑐𝑜𝑠( 𝜑



)

𝑦



= 𝑦



−𝐷



𝑐𝑜𝑠( 𝜑



) −𝐷



𝑠𝑖𝑛( 𝜑



)

ℎ



= ℎ



+ 𝐷



(9)

The expected wingman speed 𝑣



is related to the

relative distance of the long wingman along the

induced route 𝑑



, the speed of the long wingman

and the speed difference of the long wingman.

{

𝑣



= 𝑘



𝑑



+ 𝑣



+ 𝑘



(𝑣



−𝑣



𝑐𝑜𝑠( 𝜑



−𝜑



))

(10)

The control method of wingman tracking

induced route is the same as that of long plane

tracking given route, which is realized by UAV

autopilot. The control of UAV is decoupled into

three aspects: longitudinal channel control, lateral

channel control and throttle lever position control.

Classical PID control is adopted, and the normal

overload command, roll angle speed command and

throttle increment command are obtained according

to the input route command, which are used as the

input of UAV model.

SIMULATION SCENARIO AND

VERIFICATION

3.1 Simulation Scenario

The blue UAV cooperates against the red target area,

and the target distribution in the red target area is

shown in Figure 9. Each assembly point can be

equipped with 16 UAVs, which are divided into two

categories: A is an electronic warfare UAV and B is

Design of UAV Cooperative Countermeasure Decision System

a strike UAV, including 12 UAVs in Class A and 4

UAVs in Class B.. Class A UAVs need to fly in

formation from the assembly point and perform

jamming tasks, while Class B UAVs need to strike

targets at fixed points. The schematic diagram is as

follows:

Flight path

target

Departure point

Figure 9: Schematic diagram of countermeasure scenario.

3.2 Simulation Results and

Implementation

As shown below, the main interface of the system is

mainly composed of five parts, namely, planning

state display module, indicating state display

module, simulation control module, two-

dimensional situation display module and three-

dimensional curve display module.

Planning status

display

indication

status display

Simulate Control

Button s

3D curve

display

Two-dimensional

situation display

Figure 10: Countermeasure scenario.

1) 32 UAVs interfere with and attack 4 targets

There are two kinds of battlefield threats in scenario

1, one is radar target and the other is enemy weapon

target, including 3 enemy radar targets and 1 enemy

weapon target. There are two UAV groups in Blue

(12 jamming UAVs and 4 attacking UAVs each),

which take off from different locations and gather in

designated airspace. After the collection is

completed, form a designated formation and fly in

formation. When the cluster reaches the target area,

the cluster is separated, and then the respective

cluster tasks are performed. In this scenario, there

are four targets and eight attacking drones, and the

task assignment of the targets to the drones is

completed by collaborative task assignment

algorithm to minimize the cost and maximize the

benefits.

Figure 11: Simulation result 1.

2) 32 UAVs interfere with and attack 7 targets

There are two kinds of battlefield threats in scenario

2, one is radar target and the other is enemy weapon

target, among which there are 6 enemy radar targets

and 1 enemy weapon target. There are two UAV

groups in Blue (12 jamming UAVs and 4 attacking

UAVs each), which take off from different locations

and gather in designated airspace. After the

collection is completed, form a designated formation

and fly in formation. When the cluster reaches the

target area, the cluster is separated, and then the

respective cluster tasks are performed. In this

scenario, there are 7 targets and 8 attacking drones,

and the task assignment of the targets to the drones

is completed by collaborative task assignment

algorithm to minimize the cost and maximize the

benefits.

Figure 12: Simulation result 2.

CONCLUSION

In this paper, a decision-making system of UAV

cooperative confrontation is designed, and the key

ANIT 2023 - The International Seminar on Artiﬁcial Intelligence, Networking and Information Technology

technologies and algorithms such as multi-UAV

cooperative task assignment, multi-UAV

cooperative flight path planning and multi-UAV

cooperative formation control are studied

emphatically. The simulation results show that the

system is effective and feasible in multi-UAV

cooperative operations. The system provides a useful

reference for cooperative countermeasure decision-

making of UAV formation, and provides important

support for improving operational efficiency and

mission execution ability.

REFERENCES

Secreat B R .Traveling Salesman Problem for Surveillance

Mission Using Particle Swarm Optimization[M].

BiblioScholar, 2001. ISBN-13: 9781288324309

O'Rourke K P , Carlton W B , Bailey T G ,et al.Dynamic

Routing of Unmanned Aerial Vehicles Using Reactive

Tabu Search[J]. Military Operations Research, 2001,

6(1):5-30.

Li Xiangmin, Yan Ji, Liu Bo, et al. A Survey of Multi-

Agents Cooperative Task Allocation Research [J].

Computer & Digital Engineering, 2014, 42 (12): 8.

https://doi.org/10.3969/j.issn1672-9722.2014.12.049

An S , Kim H J .Simultaneous task assignment and path

planning using mixed-integer linear programming and

potential field method[C]// International Conference

on Control. IEEE, 2014. https://doi.org/

10.1109/iccas.2013.6704241

Zhu D , Huang H , Yang S X .Dynamic Task Assignment

and Path Planning of Multi-AUV System Based on an

Improved Self-Organizing Map and Velocity

Synthesis Method in Thre e-Dimensional Underwater

Workspace[J]. IEEE Transactions on Cybernetics,

2013, 43(2): 504-514. https://doi.org/10.1109/

TSMCB.2012.2210212

Liao Mo, Chen Zongji. Multi-machine cooperative target

assignment algorithm based on satisfactory decision

[J]. Journal of Beijing University of Aeronautics and

Astronautics, 2007,33 (1): 5. https://doi.org/

10.3969/j.issn.1001-5965.2007.01.019

WU J C , ZHOU R , DONG Z N , et al . Formation flight

control method of multiple UAVs based onguidance

route[J]. Journal of Beijing University of Aeronautics

and Astronsutics, 2016, 42(7): 1518-1525. https://

doi.org/10.13700/j.bh.1001-5965.2015.0458

Design of UAV Cooperative Countermeasure Decision System