1 Northwestern Polytechnical University (NPU) 2 Universitat Autònoma de Barcelona

Model of Collaborative UAV Swarm Towards Coordination and Control Mechanisms Study

Xueping Zhu1 Zhengchun Liu2 Jun Yang1

An unmanned aerial vehicle (UAV), known in the mainstream as a drone and also referred to as an unpiloted aerial vehicle and a remotely piloted aircraft (RPA) by the International Civil Aviation Organization (ICAO), is an aircraft without a human pilot aboard.

IntroductionUAV term

Delivery

IntroductionUAV applications

Modern Technology Make it:1.Cheaper; 2.Easier & Faster to Deploy; 3.smaller and flexible.

UAV Civilian Applications: 1.Disaster management; 2.Remote area surveillance; and hazardous environment monitoring;

3.Fast package delivery; 4.Aerial photo & video.

Disaster

OutlineUAV challenges and proposed solution

sophisticated tasks, such as searching survival points, multiple target monitoring and tracking UAV swarms is foreseen

more complex control, communication and coordination mechanisms

mechanisms are difficult to test and analyze under flight dynamic conditions, especially robustness!

model UAV swarm as multi-agent system (for testing in virtual environment)

Flying UAV have many uncertainties and constraints that are difficult to model through a “If...Then...Else” statement.

Mathematic (6DoF) +

Computational (MAS principle)invisible message switch

layer

Interaction

agent model

Test-Bed,especially for robustness test +

Simulation!

Fixed-wing Rotary-wing

๏ Hand/catapult launched ๏ Long flight endurance, can cover a

lot of area๏ Difficult to fly if not fully automated ๏ Requires fully automated flight features

for full usability ๏ Minimal maintenance, modest

expenses

๏ Automated launch ๏ Limited flight endurance๏ Hover capability -> Precise 3D

inspection of stationary objects/delivery of chemicals

๏ Difficult to fly if not fully automated ๏ Requires fully automated flight features

for full usability ๏ Minimal maintenance, modest

expenses

Study ObjectFixed VS. Rotary

Fixed-wing as an example(Long flight endurance, can cover a lot of area)

heterogeneous (fixed and rotary) in the future.

the agent model

F b

aero

=

2

4C

x

Cy

Cz

3

5 qS, M b

aero

=

2

4m

x

my

mz

3

5 qSL, q =

1

2

⇢V 2(1)

2

4Fxb

Fyb

Fzb

3

5= mg0

2

4� sin ✓

cos ✓ sin�cos ✓ cos�

3

5+

2

4XYZ

3

5+

2

4P0

0

3

5(2)

Cz

= f (V,↵, �y

) = Cz0 + C↵

z

· ↵+ C�yz

· �y

(3)

.

Cz0 is the lift coe�cient when both ↵ and �z are equal to zero (the zero lift

coe�cient).

C↵z and C

�yz are related with velocity, write as: C↵

z = f (V ), C↵z = f

0(V ).

Where:

6DoF - agent modelFlight Dynamic

e.g.,

Table 1: List of aerodynamic data needed to model an UAV

Notation

Definition

m, IThe mass and moment of inertia of the rigid body.

SSum of wing area.

LThe chord length.

C�zx,y,z

-V The drag/side/lift force coe�cient derivative due to correspond-

ing horizontal tailplane to velocity.

C↵

x,y,z

-VThe drag/side/lift force coe�cient derivative due to angle of

attack to velocity.

C(x,y,z)0 The zero drag/side/lift force coe�cient.

m�zx,y,z

-VThe roll/pitch/yaw moment coe�cient derivative due to corre-

sponding horizontal tailplane to velocity.

m↵

x,y,z

-VThe roll/pitch/yaw coe�cient derivative due to angle of attack

to velocity.

m(x,y,z)0 The zero roll/pitch/yaw moment coe�cient.

6DoF - agent modelParameters for Flight Dynamic Model

Physical Parameters

Aerodynamic Force Coefficient

Moment Coefficient

˙Vb

=

1

m

2

4Fxb

Fyb

Fzb

3

5+ V

b

⇥ !, ! =

2

4pqr

3

5(1)

! = I�1

0

@

2

4M

xb

Myb

Mzb

3

5+ (I!)⇥ !

1

A , I =

0

@Ixx

�Ixy

�Ixz

�Iyx

Iyy

�Iyz

�Izx

�Izy

Izz

1

A(2)

2

4pqr

3

5=

2

4'0

0

3

5+

2

41 0 0

0 cos' sin'0 � sin' cos'

3

5

2

40

˙✓0

3

5+

2

41 0 0

0 cos' sin'0 � sin' cos'

3

5

2

4cos ✓ 0 � sin ✓0 1 0

sin ✓ 0 cos ✓

3

5

2

40

0

˙

3

5= J�1

2

4'˙✓˙

3

5

(3)

2

4'˙✓˙

3

5= J

2

4pqr

3

5=

2

41 (sin' tan ✓) (cos' tan ✓)0 cos' � sin'0 (sin' sec ✓) (cos' sec ✓)

3

5

2

4pqr

3

5(4)

↵ = arctan

Vzb

Vxb

, � = arcsin

Vbyq

Vxb

2+ V

yb

2+ V

zb

2(5)

O

O O

I

I

6DoF - agent modelKinematic - Agent State Transition

O

Model an unmanned aerial vehicle as autonomous agent. The sensors and communication devices give it possible to interact with companions and environment. The 6 degree-of-freedom (6DoF) flight dynamic model reflects constraints and uncertainties as well as state transition.

Model Concept of UAV as an Agent

Communication amongst UAVs(base of interaction)

Direct In-direct

๏ WiFi, ZigBee, Bluetooth etc; ๏ low power consumption, less latency,

free of charge, etc; ๏ without support base station; ๏ distance restriction.

๏ GSM, Satellite etc; ๏ high power consumption, long latency,

pay by bytes; ๏ with ground support base station; ๏ almost no distance restriction.

Communication amongst UAVs(crucial for agents to coordinate properly)

Proper combination could be better; communication ways should be carefully considered;

Communication Mode / Media

message types descriptionbroadcast an agent share its current state to a set of agents

actively; query an agent send a message to a set of agents to request

their states;sync an agent (e.g. group leader) send a sync request

message to a group of agents, then all the agents who receive the sync request will broadcast their current state to all the other group members.

Interaction message amongst UAVs

As for implementation, we implemented a message switch layer (MSL) work in an observer role (not exist in real situation, only for implementation, all information about simulation world is accessible by observer), all the communication amongst UAVs will pass through this layer and MSL will record all these messages for post-simulation analysis. This provides a flexible way for control mechanism designer to optimize the communication policies in micro-level behavior.

A proof-of-concept Case Study

Demo. Application

z(x, y) =nX

i=1

h

i

⇤ exp �✓x� c

xi

g

xi

◆2

�✓y � c

yi

g

yi

◆2!

Environment Model:

.

h

i

controls height of peak i;

c

xi and c

yi mark central position of peak i;

g

xi and g

yi control peak gradient in x and y orientation respectively.

Where:

is the altitude of the given coordinate in the simulated terrain.z (x, y)

n is the number of peaks (mountains).

1. a mountain area of size 10 × 20km2; 2. Five UAVs “scan” the area on preset height (100

meters over ground); 3. distance between two neighboring UAVs should be

kept as 500 meters (due to the restriction of sensor, e.g., camera);

4. vehicles are set to fly with their curing speed (35 m/s) to save power consumption.

Demo. ApplicationSimulating Conditions

1 2 3 4 5

µ

Demo. ApplicationSwarm Contro Model

x = �Lx+ bµ

y = c

Tx

2

66664

x1

x2

x3

x4

x5

3

77775=

2

66664

�1 1 0 0 01 �2 1 0 00 1 �2 1 00 0 1 �2 10 0 0 1 �1

3

77775

2

66664

x1

x2

x3

x4

x5

3

77775+

2

66664

10000

3

77775µ

y =⇥1 0 0 0 0

⇤

2

66664

x1

x2

x3

x4

x5

3

77775

0

2000

4000

6000

8000

10000

12000

14000

16000

18000

20000

0−1000

01000

20003000

40005000

60007000

80009000

10000

0

100

200

300

400

500

z e(meter)

uav5uav4uav3uav2uav1

xe(meter)

ye(meter)

Demo. Application

3D View of UAV swarm trajectory

0 100 200 300 400 500 600−0.5

−0.4

−0.3

−0.2

−0.1

0

0.1

0.2

0.3

0.4

0.5

time(second)

a z(m/s2 )

uav5uav4uav3uav2uav1

constraint

0 100 200 300 400 500 600−40

−30

−20

−10

0

10

20

30

40

time(second)

heig

ht e

rror

(m)

uav5uav4uav3uav2uav1

0 100 200 300 400 500 60033

33.5

34

34.5

35

35.5

36

36.5

37

time(second)

sear

ch sp

eed

(m/s

)

Conclusions and Future work(Conclusions)

1. This work is a part of a longterm project about intelligent UAV swarm application.

2. Model of UAV swarm; 3. Implemented a prototype in NetLogo3D; 4. one proof-of-concept case study was presented to show

its potential usage;

Conclusions and Future work(future work)

1. in a Netlogo3D world the number of patches grows very quickly that slow the model down or even cause NetLogo to run out of memory. => implement by using Google’s Go programming language without concept of patch.

2. rotary-wing, such as quadcopter is also widely used in some application. So, we will construct the model of quadcopter in order to provide the ability of building heterogeneous UAV swarm.

Thank You Very Much for Your Attention!

Q + A = Progress

1 Northwestern Polytechnical University (NPU) 2 Universitat Autònoma de Barcelona (UAB)

Model of Collaborative UAV Swarm Towards Coordination and Control Mechanisms Study

Xueping Zhu1 Zhengchun Liu2 Jun Yang1