VBCA: A Virtual Forces Clustering Algorithm for Autonomous Aerial Drone Systems

VBCA: A Virtual Forces Clustering Algorithm forAutonomous Aerial Drone Systems

Matthias R. Brust‡, Mustafa Ilhan Akbas∗ and Damla Turgut∗

‡Singapore University of Technology and Design∗University of Central Florida

April 19, 2016

M. Brust, M. I. Akbas, D. Turgut (SUTD,UCF) IEEE SysCon 2016 April 19, 2016 1 / 17

1 Application scenario

2 Problem definition

3 System model

4 VBCA

5 Simulation study

6 Conclusion


Application scenario

Flying Ad Hoc Network (FANET) with built-in sensors toinvestigate 3D spaceUAV system must scan its environment and react in real-time toadjust location and formationSurveillance, search and rescue operations in disaster recovery,and target localization


Problem definition

ProblemI For effective data collection, positioning of UAVs is importantI Most dynamic node positioning strategies limited to 2-D spaceI Popular 2-D strategies become NP-Hard in 3-D space

ObjectiveI Dynamic positioning of the drones in three dimensional space with

local communication


System model

ChallengesI UAV system has autonomous flight operation modeI Autonomous flight may reduce situational awareness and error

correctionI The communication must be simple yet effectiveI The drones must be able to reorganize in case of a loss


System dynamics

FANET with built-in sensor nodesEach drone communicates with neighboring drones in thecommunication rangeCentral drone have stronger computation and communicationresourcesNodes have spherical transmission rangesA hierarchically structured drone network is formed using VSEPRtheory based approach


“VSEPR theory” based approach

VSEPR (Valence Shell Electron Pair Repulsion) model is the mostsuccessful model for the molecular geometry predictionArrangement of electron pairs in valence shell of the central atomare due to the repulsion between themVSEPR theory is adopted to build a self-configuring dynamicnetwork architecture


“VSEPR theory” geometries

Peripheral atoms mapped to peripheral drones and central atomto the central drone


“VSEPR theory” geometries

Example locations with respect to the centralnode:

Positions of drones in Linear geometry:pa1(x , y , z) = (r ,0,0) pa2(x , y , z) = (−r ,0,0)

Positions of drones in Trigonal planargeometry:

pa1(x , y , z) = (r ,0,0)pa2(x , y , z) = (−r .sin(30◦), r .sin(60◦),0)pa3(x , y , z) = (−r .sin(30◦),−r .sin(60◦),0)

Positions of drones in Tetrahedral geometry:

pa1(x , y , z) = (0,0, r)pa2(x , y , z) = (−r .a,−r .b, r .cos(109.5◦))pa3(x , y , z) = (−r .sin(109.5◦),0, r .cos(109.5◦)pa4(x , y , z) = (−r .a, r .b, r .cos(109.5◦))


Formulation of VSEPR geometries

APAWSAN† locates nodes using exact position calculationsI When number of drones is between 1-3, located on a single planeI When number of drones is between 4-7, 2 located on z-axis, others

located on single plane with equal connection anglesI When number of drones is 8, located on 2 planes

VBCA uses virtual forcesI Attraction force acts between the central drone and each of the

remaining dronesI Repulsion force acts among all drones except the central droneI Type and direct neighbors affect the position of a node

†M. I. Akbas, and D. Turgut. “APAWSAN: Actor positioning for aerial wireless sensor and actor networks” In Proc. of IEEE LCN,

2011, pp. 567-574.


Dynamic positioning

Attraction and Repulsionforces among dronesThere is no operation center orremote controlChanges and maintenancethrough local communicationonlyExtended geometries can beachieved by heterogeneousforces


Compactness Parameter

Transmission range is an influence on the distancesDistances in VSEPR are determined by physical rulesTopology of the aerial network can be preserved while adjustingthe distancesCompactness parameter CP is introduced to regulate thecloseness of drones


Volume coverage

Volume coverage follows a linear increase as the number ofdrones increasesRate of this increase varies for different CP values

3 4 5 6 7 8 9 100

1

2

3

4

5

6

7

8x 10

6

Number of Drones

Vol

ume

CP=10

CP=20

CP=30

CP=40

CP=50

CP=60

CP=70


Volume coverage comparison

VBCA (CP=40 and 50) vs APAWSANAPAWSAN calculates exact locations, VBCA uses virtual forces

3 4 5 6 7 8 9 100

0.5

1

1.5

2

2.5

3x 10

6

Number of Drones

Vol

ume

VBCA (CP=50)APAWSANVBCA (CP=40)


Stability of topologies

Drone topology becomes stable after the self organization periodStability of the topoplogy is important for the volume coverage


Average distance and CP valuesAverage distance to central node increases with increasing CP

I Larger communication range required with larger CPI CP and communication range create mutual requirements to the

central node.

3 4 5 6 7 8 9 100

10

20

30

40

50

60

70

Number of Nodes

Ave

rage

Dis

tanc

e to

Cen

tral

Dro

ne (

m)

CP=10

CP=20

CP=30

CP=40

CP=50

CP=60

CP=70


Conclusion

Autonomous FANET system for constraint-based volume coverage

Virtual forces are used to dynamically create the VSEPR theorygeometries

Simulation results show our protocol provides topologies withnetwork connectivity and efficient volume coverage

Future steps:I Exploring other concepts of VSEPR theory and molecular geometry

for scalabilityI Experiments with real life antenna modelsI Application of VBCA to the onservation of irregular real-world

objects


VBCA: A Virtual Forces Clustering Algorithm for Autonomous Aerial Drone Systems

Science

Transcript of VBCA: A Virtual Forces Clustering Algorithm for Autonomous Aerial Drone Systems