1. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Self-Organising UAVs for Wide Area
Fault-tolerant Aerial Monitoring
Massimiliano De Benedetti, Fabio D’Urso, Fabrizio Messina,
Giuseppe Pappalardo, Corrado Santoro
ARSLAB - Autonomous and Robotic Systems Laboratory
Dipartimento di Matematica e Informatica
Universit`a di Catania, Italy
WOA 2015 - Napoli, Italy, Sept. 17-18, 2015
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 1

2. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Outline
1 Aim and Scope
2 System Model
3 Area Coverage Algorithms
Overlay Construction
Flock Formation
Path Planning
4 Simulation Study
5 Conclusions
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 2

3. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Aim and Scope
Aim and Scope
Context: Aerial inspection of a wide area of terrain for
monitoring purposes.
Autonomy: ability to perform the required inspection without
external control.
Requirements: to minimise mission time and to ensure a
high degree of fault tolerance
PROPOSAL: To use of set of Unmanned Aerial Vehicles (VTOL
multirotors) able to:
self-organise in a flock
cover the desired area
handle faults of one or more UAVs
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 3

4. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
System Model
System Model
The system is made of:
A set of mobile entities (agents), the multirotors
A certain physical area to be covered
An inertial 3-dimensional reference system: latitude,
longitude, altitude, heading
The area to be monitored can be represented with a shape of a
rectangle: (x1, y1), (x2, y2), (x3, y3), (x4, y4).
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 4

5. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
System Model
System Model—Agents
Each UAV/agent is equipped with:
A flight stabilization system
A GPS-based autopilot
A low-power wireless transmission system (with limited
range)
A monitoring sensor (i.e. a camera) able to cover, at an
altitude of z, the area (wsz, hsz)
At run-time, each UAV/agent is characterised by a unique ID and
a time-dependent pose:
posei (t) = (xi , yi , zi , headi )
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 5

6. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
System Model
System Model—Mission
A mission is characterised by:
The area to be covered, i.e. : (x1, y1), (x2, y2), (x3, y3), (x4, y4)
The number of agents, N
The operating altitude, Z
The objective is:
to ensure the complete coverage of the area
to minimise mission time
to avoid over-coverage
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 6

7. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Area Coverage
To perform area coverage, three algorithms are employed:
Overlay Construction, agents self-organise in an overlay
network in order to know each other and be able to interact
Flock Formation, agents self-organise in order to assume a
flock shape with certain characteristics
Flock Driving, agent flock sets-up and follows a certain path
in order to cover the mission area
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 7

8. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Overlay Construction
Overlay Construction—(I)
Each agent k maintains a local Agent Database (ADB) which
stores the information of the other agents known by k:
ADB = {(IDi , posei , HOP
(k)
i , TSi )}
where:
IDi is the identifier of agent i
posei is the pose of agent i
HOP
(k)
i is the number of hops—in the view of k—required to
reach i
TSi is the timestamp (in the localtime of i) at which posei
has been generated
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 8

9. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Overlay Construction
Overlay Construction—(II)
Periodically, agent k retrieves its position posek and send in
broadcast the information:
ADB ∪ {(IDk, posek, 0, TSk)}
Since transmission system is limited in range, only some of the
other agents will receive the message.
A so-formed message (we call it RADB) received by an agent k∗ is
then analised by k∗ and compared with its local ADB.
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 9

10. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Overlay Construction
Overlay Construction—(III)
On the reception of RADB, the following operation are performed
by agent k∗:
each hop count field in RADB is incremented
for each tuple ∈ RADB, find ID in ADB of k∗
if it is not found, ADB ← ADB ∪ {tuple}
if it is found then
if the TS(RADB)
> TS(ADB)
, ADB ← ADB ∪ {tuple}
else keep information in ADB and discard tuple
After some iterations each agent will know:
all the other agents of the system;
their last position (which is periodically updated);
its “distance” with respect to all the other agents.
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 10

11. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Flock Formation
Flock Formation—(I)
Aim: to maximize area coverage and mimize
over-covering.
We choose a flock shape which ensures that a certain are is
not monitored by two agents.
The ideal shape is a linear placement along a formation line,
perpendicular to the direction of flight.
The distance between two agents is kept close to wsZ (with
an overlap of 20%—configurable).
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 11

12. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Flock Formation
Flock Formation—(II)
To form a flock, a leader is elected as the agent with the
lowest ID.
Each agent elects the leader by directly choosing it from the
ADB, no further interaction is required.
As soon as the overlay construction algorith proceeds, all
agents will elect the same leader.
Tuples in the ADB are subject to aging: if a tuple is not
updated within a timeout, it is discarded.
Should the leader fail, its data (in the ADBs) will be
discarded and a new leader will be elected.
The leader establishes the flock formation line which is the
virtual line perpendicular to leader heading.
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 12

13. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Flock Formation
Flock Formation—(III)
Rule R1: Separation
The geographic distance di to each other neighbour agent i is
computed.
If di is less than a hard threshold DH, no motion is applied
(the UAV remains in hovering for the current iteration); this is
required to avoid any possible collision.
If di is less than a soft threshold DS > DH, a repulsion force
is generated for the agent, with an heading always parallel to
the formation line.
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 13

14. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Flock Formation
Flock Formation—(IV)
Rule R2: Alignment
The distance di to each other neighbour agent i is computed.
The agent k which is the nearest to the leader (w.r.t. hi ) is
found (or the leader itself).
The orientation of k is copied by using a yaw rotation.
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 14

15. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Flock Formation
Flock Formation—(V)
Rule R3: Cohesion
The distance di to each other neighbour agent i is computed.
The agent k which is the nearest to the leader (w.r.t. hi ) is
found (or the leader itself).
Agent k is approached by means of translated flight (if they
are too close, rule R1 intervenes)
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 15

16. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Path Planning
Path Planning—(I)
To cover the desired area, the following algorithm is employed:
1 The overall area is subdivided in small portions.
2 Periodically, all the agents spread the data about the portions
already covered by each of them.
3 The leader, on the basis of such data, computes the optimal
path and follows it driving the entire flock.
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 16

17. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Path Planning
Path Planning—(II)
The area is subdivided before starting the mission as follows:
A roto-translation of the reference is performed in order to set one
corner of the rectangle area to the origin.
The flight is performed by keeping the formation line parallel to
the X axis.
The area is discretized in stripes along Y axis using a size of hsZ
per stripe. Each stripe is numbered.
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 17

18. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Path Planning
Path Planning—(III)
Each agent holds and updates an Area Parts Database APDB which
contains the information about the parts already covered. It is a set of:
(StripeID, {(XS1, XE1), (XS2, XE2), . . . , (XSn, XEn)})
Each stripe part is represented by its starting and ending X coordinate
(XSi , XEi ).
Two close parts are always merged together by means of a stripe union
operation:
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 18

19. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Path Planning
Path Planning—(IV)
The leader periodically starts a distributed aggregated
query which aims at obtaining the knowledge of the APDB of
all the other agents
Agent respond by broadcasting their APDB
An agent receiving an other’s APDB, merges received data
with its own APDB and broadcasts the resulting set
...
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 19

20. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Area Coverage Algorithms
Path Planning
Path Planning—(V)
...
The leader, on the basis of received data, computes some
possible paths and chooses the one currently nearest to the
flock
The path is then smoothed
The leader starts to follow the chosen path and drives the
overall flock.
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 20

21. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Simulation Study
Simulation Study
To validate the proposed approach, a software simulator has been
built:
written ad-hoc in C++ with QT libraries
able to simulate all the algorithms by graphically showing the
flight of the UAVs and colouring the parts of the area covered
able to simulate faults in one or more agents (crash) or in the
communication system (packet loss)
The simulator is available on request.
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 21

22. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Conclusions
Conclusions and Future Work
While preliminary tests showed the validity of the approach,
further simulation studies are needed in order to numerically
evaluate:
fault tolerance degree
mission time trend vs. number of agents
percentage of portions over-covered due to faults
....
This work is part of a research project (PON-CLARA) for the
monitoring and control of landslips.
A further step will be the implementation on real UAVs
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 22

23. Self-Organising UAVs for Wide Area Fault-tolerant Aerial Monitoring
Conclusions
Self-Organising UAVs for Wide Area
Fault-tolerant Aerial Monitoring
Massimiliano De Benedetti, Fabio D’Urso, Fabrizio Messina,
Giuseppe Pappalardo, Corrado Santoro
ARSLAB - Autonomous and Robotic Systems Laboratory
Dipartimento di Matematica e Informatica
Universit`a di Catania, Italy
WOA 2015 - Napoli, Italy, Sept. 17-18, 2015
De Benedetti, D’Urso, Messina, Pappalardo, Santoro. Self-Organising UAVs - WOA 2015 23