ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010)[EN-037] Airborne Conflict Modeling and Resolution for UAS
Insertion in Civil Non-Segregated Airspace(EIWAC 2010)
C.A. Persiani*, S. Bagassi***Department of Electronic,
Computer Sciences and SystemsUniversity of Bologna**Department of Mechanical,
Nuclear, Aviation and MetallurgicalEngineering - University of Bologna
Abstract: In this paper we present a Genetic Algorithm designed to manage the mission of an UAS that has to visit a setof mission points into a congested airport TMA (Terminal Area) .
The genetic approach is useful to model the presence of different avoidance options: populations of pilots that have different“avoidance philosophy” are crossed in order to obtain a good mix of avoidance technique.
Our methodology is based on a strategic, intent-based, non-cooperative (only one aircraft – the UAV –maneuver), geometric(prediction is based on geometrical projections) and distributed (as opposed to centralized) approach.
Finally, real piloted traffic data from the Milano Linate (LIML) Terminal Area are used to test the algorithm.
Keywords: Airborne conflict resolution, UAS insertion

In order to study the feasibility of self separation, many
prototype tools such as the Autonomous Operations
Planner (AOP), Future ATM Concepts Evaluation Tool
(FACET) [1] developed at NASA and the Airborne
Separation Assurance System (ASAS) [2] developed at the
National Aerospace Laboratory (NLR) in the Netherlands
have been proposed. All these tools implement CD&R
algorithms.

1. ITRODUCTIOOne of the most interesting challenge of the next years willbe the Air Space Systems automation. This process will
involve different aspects as the Air Traffic Management,
the Aircrafts and Airport Operations and the Guidance andNavigation Systems. The use of UAS (Uninhabited Aerial
System) for civil mission will be one of the most important
steps in this automation process.

CD&R algorithms can be airborne or centralized. Usually
airborne conflict detection and resolution algorithms are
suitable for tactical use, whereas centralized CD&R
algorithms can be used both for tactical and for strategic
control. CD&R algorithms can be state-based or intentbased. State-based refers to the use of aircraft state
information as opposed to those algorithms that use intent
information, e.g. flight plan.

The UAS insertion in civil non-segregated airspaceaddresses quite a number of issues such as the level ofpriority that unmanned air traffic should have relative tomanned air traffic, the coordination between the UAV
controller and the air traffic control, the level ofautomation of the UAS, the sense & avoid capacity of the
UAS.

The airborne algorithms used in the Autonomous
Mediterranen Free Flight (AMFF) simulations have proved
to be effective with limitations for dense traffic conditions[3]. AMFF is a state-based conflict detection andresolution concept with a level of automation such that thepilot follows the automatically generated conflict
resolution advices by steering the aircraft.

The management of potential conflicts between UAS andmanned air traffic is a challenging issue that is often
considered as an instance of the general Conflict Detection& Resolution (CD&R) problem.
Since the first concepts concerning Free Flight wereenvisaged, both the industry and the academia researchcommunities paid attention to CD&R problem.

Farley et al. [4] assessed the performance of a conflict
resolution algorithm developed as part of the Automated123

C.A. Persiani, S.Bagassi

regulations concerning minimum separation between
piloted and not piloted traffic do not exist: we assume aminimum longitudinal separation of 5 NM and a minimum
vertical separation of 1000 ft. According to its
performance, the UAV has different options to avoid thepiloted air traffic: holding over a way point, reducing or
increasing its speed or using a vertical or horizontal
avoidance. The goal consists in planning the UAV
minimum-time and minimum-fuel routes ( in terms of
succession of mission targets) that allow to visit the targets
maintaining the minimum separation from the piloted
traffic. This problem has two decisional dimensions: thechoice of the best order of visiting a set of way points andthe choice of the avoidance manoeuvres that have to be
used. The first problem can be considered as the wellknown Travelling Salesman Problem (TSP), while thesecond is pertaining to an UAV path planning problem thathas been investigated using several approaches. These two
problems can be joint considering that the avoidance of thepiloted air traffic in a sub-path between two mission way
points depends on the moment by which the UAV starts
flying between this couple of way points. In this way it ispossible to formulate the problem as a Time-Dependant
Travelling Salesman Problem (TDTSP). The temporaldimension can be introduced by dividing into time steps
the temporal horizon. If tend is an upper bound on the
mission duration obtained by the UAV endurance and ∆t is
the duration of a time step, the set T = {1,...h} is the set of
the time steps where h = t end / ∆t represents the numberof time steps used. For a mathematical formulation of thisproblem see [9].

Airspace Concept [5] for conflicts detected in any phase offlight - including arrival merging operations - in asimulation environment designed to represent thecomplexity, variety and volume of current and future air
traffic operations.
Afterwards, the literature has expanded to include
approaches based on genetic algorithms [6][7][8] andarrival-time constraints.
In this paper we present a Genetic Algorithm designed to
manage the mission of an UAS that has to visit a set of
mission points into a congested airport TMA (Terminal
Area) .
The genetic approach is useful to model the presence ofdifferent avoidance options.
Our methodology is based on a strategic, intent-based,
non-cooperative (only one aircraft – the UAV –maneuver),geometric (prediction is based on geometrical projections)and distributed (as opposed to centralized) approach. It iscomprised by two steps.
In a first step a geometric analysis allows to identify all
possible UAS paths among the mission targets and theirrelated potential conflicts with the piloted aircraft (traffic
aircraft).
Air Traffic Management operative techniques are used tomodel different options of conflicts resolution: vertical andhorizontal avoidance, speed regulation and the use of theholding patterns. These avoidance options are thencompared taking into account the mission constraints andobjectives, minimum time or minimum fuel, in order todefine a cost for each UAS path and its related conflict
avoidance options.

Considering long endurance missions (7/8 hours and up to
40/50 targets) and different avoidance options, the problem
calls for heuristic solution. Our approach uses a first pre-In the second step the problem of find the mission targets
visit order is considered. It consists in a combinatorial
problem that concerns the sequencing of both targets andconflict resolution options.

processing step by which the function of the time w(ki , j ) (t )is calculated for each UAV sub-path: it represents theweight (as estimation of the time and fuel consumption ) of
the sub-path (i,j)started by the UAV at time step t ∈ Tusing the k avoidance manoeuvre. These weights are thenused by the genetic algorithm to calculate the population
fitness. Populations of pilots that have different “avoidance
philosophy” are crossed in order to obtain a good mix of
avoidance technique.

Finally, real piloted traffic data from the Milano Linate(LIML) Terminal Area are used to test the algorithm.
2. PROBLEM FORMULATIOLet us consider a UAV mission performed in Controlled
Air Space. The mission consists in departing from aground base, taking pictures over a set Mwp (Mission Way
Points) of m targets at a given mission altitude and comingback to the same airfield. During this mission a set A of npiloted aircrafts fly into the same Air Space following
authorized routes: the UAV has to maintain a minimum
separation from the piloted air traffic. We suppose that theposition and the altitude of each aircraft during the entire
mission (off-line approach) are known. Currently ICAO

3. COFLICT GRAPH AD AVOIDACEMAOEUVRESAs the TSP is associated to a graph, the UAV mission
environment can be modeled using a “Conflict Graph”. We
definea
ConflictGrapha“sixtuple” G = {V , M wp , A, C , K , T } .

124

ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010)of the conflict c and the related time step tc. Let us consider
Fig.1 where an intersection between the UAV path p1u-p2u
and the aircraft a sub-route p1a-p2a occurs in the point pcnf .

V = {(i, j ) / i ∈ M wp , j ∈ M wp , i ≠ j} is the set of possible
paths between the mission way points: if no conflict occurs,
the UAV flies direct from i-th to j-th target. Due to thedifferent altitude values required to overfly the targets, thepath (i,j) is different from the path (j,i) in terms of time andfuel consumption: the Conflict Graph is an asymmetric anddirect graph. C is the set of possible conflicts: a conflictc ∈ C between the UAV u and an aircraft a ∈ A occurs if
the minimum separation requirement is not satisfied during
all the time steps t ∈ T . K is the set of all possible
avoidance options: holding, speed control and avoidanceon vertical and horizontal planes.
In order to build the Conflict Graph it is necessary toconsider the intersection between the UAV Mission Graph
G m = {V , M wp } and the route of each aircraft a ∈ A .
Such routes consist in a set of IFR (Instrumental Flight
Rules) points (RadioAssistance or Fix Points): ∀a ∈ A ,R a = {a 1 ,...an } , n a ∈  . As input of the problem (off-line

Figure 1. A conflict between a UAV path and an
aircraft sub-route defines a conflict zone and its relatedcharacteristic points.

aproblem) the altitude haa and the time t aa of the aircraft aii

The conflict time step tc is the overflying time step of thepoint pcnf . A “sub-separation stretch” over p1u-p2u is
identified between p1cu and p2cu . We define the point p1cu
as the first point over the UAV path between p1u and pcnf
that presents a distance of 1000 ft on the vertical direction
or 5NM on the horizontal direction, whichever happens
first, moving from pcnf to p1u. Analogously the point p2cu isthe first point over the UAV path between pcnf and p1u that
presents a distance of 1000 ft on the vertical direction or
5NM on the horizontal direction, whichever happens first,
moving from pcnf to p2u. In the same way the points p1ca
and p2ca are defined over the aircraft a route. The points
p1cu , p2cu ,p1ca , p2ca define a “sub separation” zone
around the conflict point pcnf . Considering a fixed speed
for the UAV vuav (for example the best range speed, thebest climb speed or the best glide speed), its last departingtime step from the target point p1u is such that it can arrivein p2cu when the aircraft a is in the point p1ca. If∆t p1u − p1cu is the time step interval necessary for the UAV

over its routing point ai are known for each aircraft a ∈ A .
An intersection between a UAV path (i,j) and an aircraft
sub-route (ai,aj) occurs if the distance between the closest
points of these segments is less than 1000 ft on the verticalplane or 5 NM on the horizontal plane. In this case a
conflict c ∈ C in the path (i,j) and its related time step tc
are identified. The “weights” (in terms of time and fuelconsumption) of the avoidance manoeuvres of the conflictc depends on:•

the departing time step from i to j;•

the geometry of the conflict;•

the UAV performances.

Naturally, starting a path in a specific time step andmaintaining a defined speed could implicate a conflict
while another departing time step could be conflict free.
The fuel and the time consumption function of thedeparting time step are estimated using, for each UAV path,the geometric model described below.

to fly from p1u to p1cu maintaining vuav , the holding overp1u starts at time step:

3.1. Holding
An holding is an avoidance manoeuvre that consists inperforming an orbit over the initial way point i of the path(i,j). The manoeuvre is comprised by an in-bound leg andby an out-bound leg. An holding over a target point i canbe modeled by forbidding the departing from that point foriia subset of time steps T fi = [t hstart...t hend]p1u
t hstart= max{0, t1a + ∆t p1a _ p1ca − ∆t p1u _ p 2cu }Analogously the first UAV restarting time step from thetarget point p1u is such that it can arrive in p1cu when theaircraft a is in the point p2ca. The holding over p1u finishes
at time step:

T fi ⊆ T ,iiwhere t hstartis the time step of holding start and t hendisthe time step of holding end. For given UAV performances,these time steps are determined considering the geometryp

1u
t hend= max{0, t1a + ∆t p1a _ p 2 c a − ∆t p1u _ p1cu }125

C.A. Persiani, S.Bagassi

example, let us consider the case represented in Fig.4,
when the UAV flies from points p1u to points p2u . The
direction of the UAV after p2u is not known, so it isnecessary to consider a safe buffer zone through theconstruction of a sphere around pcnf . Whatever the UAV
direction is, in the subsequent path the holding over p1u
allows to avoid the mentioned conflict. Moreover, once a
conflict free time step for the departure from the groundbase has been identified and this procedure is repeated foreach conflict c ∈ C for the all UAV path (i, j ) ∈ V , thedeparture from p2u to p1u cannot happen during a conflict.

The holding over p2u is determined in the same way
considering the UAV flying from p2u to p1u.
p2u
t hstart= max{0, t1a + ∆t p1a _ p1c a − ∆t p 2 u _ p1cu }p

2ut hend= max{0, t1a + ∆t p1a _ p 2c a − ∆t p 2u _ p 2 cu }The intersection case presented in Fig.1 is the general caseby which several particular cases can be derived.
Fig.2 represents an intersection between the UAV path p1up2u and an aircraft route such that more than one aircraft
sub-route is involved: the aircraft direction change in the
sub separation zone.

Figure 4 The conflict zone involves more than one UAV
path
Finally for each departing time step it is now known therelated holding hit, j to reach the next target point avoiding
the conflicting traffic.
If the UAV is flying maintaining the speed v, it is possible
to estimate the weight of the UAV sub-path (i,j) as:w(holding(t )i, j )

3.2. Speed Control
The speed control is often used by the Air Traffic Control
to maintain or to provide a minimum separation between
aircraft of different performances without changing theirroutes.

Figure 2 and Figure 3. The conflict zone involves more
the one aircraft sub route

Using the same geometric model presented for the holding,

The point p2cu is defined through the distance from the
sub-route p2u-p3u and the UAV path p1u-p2u ; moreover the
point p2ca is on the sub-route p2u-p3u . In case the point p2cu
p1u
is between p1u and p1cu ,then t hstart(h t + ( p1u _ p2u / v))= i, jred

it is possible to find the medium speeds vm

inc thatand vmthe UAV has to maintain to avoid a conflict by reducing or
increasing respectively its medium speed. Considering

is defined using theredonly the case reported in Fig.1, vm is the medium speed

p2cu*

approximation of the pointthat corresponds to theprojection of p2u over the UAV path. The same happens
for the point p1cu if the direction change occurs as in Fig.3.

that the UAV has to maintain to arrive in p1cu when theaircraft is in p2ca starting the path p1u-p2u at time step t1u:

In case the point p1u or p2u is in the sub-separation area asin Fig.4, the points p1cu and p2cu are defined using a “safesphere” with radius of 5NM and center in pcnf . For

redvm(t1u ) =( p1u_p1cu)/( t1a + ∆t p1a _ p 2ca )126

ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010)“Opening” its route, the aircraft A reaches the position of
B when B will be in a safe position. Naturally thistechnique is applicable only for particular convergenceangles between the paths. In Fig.6 the manoeuvre ismodeled using our geometric approach. A safe sphere isbuilt around the position of the aircraft a at each time step
starting from t1u. The vectoring is modeled considering thetangent to the safe sphere that passes through the current
position of the UAV and behind the aircraft position.

inc
Similarly, v m (t1u ) is the medium speed that the UAV
have to maintain to arrive in p2cu when the aircraft is in
p 1 ca :incvm(t1u ) =( p1u_p2cu)/( t1a + ∆t p1a _ p1ca )Once identified the medium speeds, it is necessary toanalyze if they are consistent with the UAV performances:both speeds must be smallest or equal to the UAV
maximum speed and greater than the UAV stall speed.
Furthermore the thrust available should allow the UAV toreach the calculated speed in a manner consistent with theconflict: we suppose the acceleration/deceleration phase isconsistent with the conflict. If it is possible to reduce or
increase the UAV speed, the estimation of the weight ofscontrthe path (i,j) due to speed control w(i, j ) (t ) can be written
as:

Figure 6. The geometric approach used to model ATC avoidancetechniqueinc / redw(scontr(t )i , j ) (t ) = ( p1u _ p2u ) / vm

The position of the UAV is updated for the next time stepby a shift of v∆t . Once the UAV passed behind theaircraft it may proceed direct to the point p2u. If nv is thenumber of time steps required to pass behind the aircraft
and np is the number of time steps required to proceedback to the original route to point p2u, it is possible toestimate the weight of the UAV subpath (i,j) as

3.3. Horizontal and vertical avoidance
Several models have been developed for the horizontal orfor the vertical avoidance. MILP techniques, for example,
allow to obtain an optimal trajectory that avoids a fixed or
moving obstacle by modeling an exclusion zone around it.
Instead our approach for the horizontal avoidance is basedon Air Traffic Management operative technique that uses
vectoring to avoid collision between converging traffic.
This manoeuvre is represented in Fig.5: the aircraft A isvectored and directed toward the aircraft B.

w(Havoidi , j ) (t ) = nv ∆t + n p ∆t

The vertical avoidance is modeled creating an alternative
UAV sub-route over or under the aircraft route. The model
detects possible further conflicts of the alternative subVavoid

route. The weight w(i , j )of the path (i,j) is estimateddirectly from the UAV performances (rate of climb, max
efficient speed, rate of descent) and from the length of thealternative generated path.

4. COFLICT DETECTIO ALGORITHM
In the following pseudo-code, we present a simple
geometric algorithm (with complexity O (| A || V |) ) basedon the Conflict Graph that allows to compute the weightw(ki , j ) (t ) of the UAV path (i,j) as a function of the

Figure 5 : ATC avoidance technique on the horizontal plane: theaircraft A is vectored to the position of B in order to pass behind it127

C.A. Persiani, S.Bagassi

selected in order to obtain a “good pilot” for a specificroute.

departing time step from i. The conflict condition between
the UAV path (i,j) and the aircraft a route, considering a
UAV speed v, is indicated as c ( Ra , (i, j ), v )Finally, the genetic algorithm presents fast computation
time compatible with future real time applications: if thecomputation time is smaller than the time step, a
replanning can be done at each time step considering
tactical updating.
The algorithm we propose starts randomly generating afirst population of ηp UAV routes Ru. At each route a“Pilot Philosophy” kr is randomly assigned. It consists in
one of the avoidance techniques k ∈ K presented:

Using this model, the presence of a conflict on a path (i,j)
is related to the departing time step: if a conflict occurs at
speed v, the arches weight depends on the avoidance
option and it is calculated by the model presented. Instead,
if no conflict occurs, the arches weight corresponds to theflight time, considering the Euclidean distance at speed v.
The UAV path weight allows to provide an estimation of
the mission endurance, while to calculate the fuelconsumption it is necessary to use the specific look-up
table of the UAV. These table provides the fuelconsumption per time unit as a function of the TAS and of
the altitude.

•

holding(k=0),•

speed control (speed reduction k=1, speed
increasing k=2),•

avoidance on vertical plane (k=3),•

avoidance on horizontal plane (k=4).

In each route of the first population, the UAV uses only
one avoidance technique to solve all the conflicts: it looks
like it is managed by a “not efficient pilot” that is able to
avoid the traffic only in one way. The fitness (min time or
min fuel) of each route is calculated considering the weightof the arches function of the departing time step
(calculated trough the geometric pre-processing algorithm
presented) and the population is sorted by decreasing
fitness. Then the evolution to the next population starts.
This process is repeated until the STOP criterion issatisfied. It consists in repeating ηt times this evolution,
checking if the improvement in the last ηc individual isgreater than 1%. If that improvement is less than 1% theevolution is terminated. During the evolution different“Pilot Philosophies” are combined each other as well as
the paths of the routes are combined. The evolution
process selects the most efficient pilot for a specific route.

5. SOLUTIO APPROACH: GEETIC
ALGORITHM
The Travelling Salesman Problem has been hardly
discussed in past years: many exact and heuristic
approaches have been proposed in literature. Instead
researches on the TDTSP (Time Dependant TSP) are quite
rare; interesting algorithms have been proposed to deal
with traffic jam. Frequently, algorithms that provide goodsolution for the TSP do not allow to obtain valid results in
the TDTSP. For example, let us consider a solution that
presents crossing arches: it is non-efficient circuit for the
TSP. Instead that solution could be valid for the TDTSP.
Moreover the problem we are dealing with requires the
choice of the avoidance techniques coupled with the choiceof the path. In this context, a genetic algorithm represents a
valid approach.

The evolution to the following populations is based oncross over operations applied to the array used to store theroute and the avoidances manoeuvres.
The first half of the population, sorted by decreasing
fitness, is considered ”good”: the second part of the
population is built from the chromosome of the previous
part. The operations used are “random shuffle, rotate andreverse” as available in the “Evolutionary Concert Tour”
(ECT)
for
C++
Builder6(www.duke.edu/web/isis/gesser/borland/evolution.htm).
Note that this evolutions operation are not applied to thefirst and to the last element of the array because theycorrespond to the departing/arriving aerodrome. The “Pilot
Philosophies“ associated to the path are crossed too andthe path obtained presents a better mix of avoidance
options. Finally the population obtained is sorted again byFirstly, it allows a better management of the timedependence than other heuristic approaches. In fact, while
the fitness of a route is time dependent, the operations ofchromosome recombination are not time dependent. This
allows to by-pass several problems typical of someconstructive heuristics in the TDTSP.
Moreover, the evolutionary process can easily be extended
to the choice of the avoidance technique: different“Avoidance Philosophy” can be properly combined and128

ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010)

green one; it is also possible to recognize the navigation
points and the aircraft indicated with red squares.

decreasing fitness. The genetic algorithm used is describedin the following pseudocode where the generic term“route” indicates the combine of a set of paths and a set of
avoidances:
BEGIN
1) Preprocessing: computation of w(t.);2) Generation of the first population of ηp UAV
routes and assignment of the “Pilot
Philosophy”kr ;3) Sort the population by decreasing fitness (mintime/min fuel);generation=1; STOP=FALSE
4) While STOP==FALSE
begin
Evolve population: first ηp /2 routes in solution;
second ηp /2 routes crossed;
Sort the population by decreasing fitness;
generation=generation+1;
if(generation<=ηt)or(improvement(ηc)<=0.01)

Figure 7 Example of results of the simulation with 20 targets. The
UAV route is red, the aircraft routes are green.

then STOP=TRUE;
end

An interesting example of UAV conflict resolution isreported in Fig 8, 9, 10,11, that shall to be observed
subsequently. The points AMOXI, LIMBA and DIXER
are lined up to Runway 36, arriving aircraft coming fromSouth follow this route. Looking the pictures in sequence it
is possible to recognize an arrival sequence AZA2036,
ACL324 and AZA2032. The UAV route includes the path
between DIXER and LIMBA in opposite direction.
However, this path is performed by the UAV between
ACL324 and AZA2032 without separation minima
infringement.

END

6. SIMULATIO RESULTS
We test our approach on a real air traffic scenario: the
TMA (Terminal Manoeuvring Area) of Milano Linate(ICAO code LIML), a major airport in the North of Italy
with an average of 350 movement (air) per day. In this area
Navigation Points as Radio-Assistance and Fix Points(radial and distance by a radio assistance) are reported;
SID (Standard Instrumental Departure) route and STAR
(STandard arrival Route) of the airport are modeled usinggraphic tools. Air traffic data related to the day of
November 12th 2009 are acquired by AOIS (Aeronautical
Operational Information Sistem) and Radar Trackprovided by ENAV S.p.A (Italian Agency for Air
Navigation Services). Position and altitude of 115 aircraft(arrival, departure and overflying traffic) are simulated
starting from 5:30 UTC (Universal Time Coordinated) for
6 hours of simulation. We generate the UAV target points
considering that some of the UAV target points correspondto the aircraft navigation points, the others have been
randomly generated.
In Fig.7 an example of mission simulation is reported. The
UAV route is the red one while the aircraft routes are the129

C.A. Persiani, S.Bagassi

Figure 8, Figure 9, Figure 10, Figure 11. Examples of UAV route
management in a critical area, the long final or ILS path: the UAV
uses this path between sequenced aircraft without infringing theminimum separation.

Finally, in the tables below simulation results are reported.
They consider different mission dimensions (10, 20, 25,
30, 35, 40 and 45 target points) for both objectives ofminimum fuel (Table 1) and minimum time (Table 2).
The results provided consider the following parameters: ηp= 200, ηt = 3500, ηc = 330 , and ∆t = 1 minute, computedon an IntelCore Duo 2GHz.

8130

ENRI Int. Workshop on ATM/CNS. Tokyo, Japan. (EIWAC 2010)

Table 1 Simulation results with minimum fuel objective
Obj
Value

N° of
Conflicts

Holding

Speed Red.

Speed Inc.

H Avoidance

V Avoidance

CPU time

20 targets

249

3

0

0

0

0

3

3,78

25 targets

310

4

1

0

1

1

1

6,12

30 targets

317

4

0

1

1

1

1

7,41

35 targets

332

2

1

0

0

0

1

15,66

40 targets

354

5

1

1

1

1

1

17,47

45 targets

433

4

1

0

0

0

3

21,98

Obj
Value

N° of
Conflicts

Holding

Speed Red.

Speed Inc.

H Avoidance

V Avoidance

CPU time

20 targets

260

5

1

2

1

1

0

3,83

25 targets

298

2

1

0

0

1

0

5,67

30 targets

304

5

1

0

2

0

2

13,69

35 targets

325

5

4

1

0

0

0

16,8

40 targets

404

4

0

0

1

2

1

12,48

45 targets

416

4

0

1

0

1

2

18,53

Table 2 Simulation results with minimum time objective

Management R&D Seminar, Naples, Italy, June
2000.
[2] J. Hoekstra, R. Ruigrok, R. van Gent, J. Visser, B.
Gijsbers, M. Valenti, W. Heesbeen, B. Hilburn, J.
Groeneweg, and F. Bussink, Overview of NLR
free flight project 1997-1999, Tech. Report NLRCR-2000-227, National Aerospace Laboratory(NLR), May 2000.
[3] Blom, H.A.P., Obbink, B.K. and Bakker, G.J.,
2009. Safety Risk Simulation of an Airborne Self
Separation Concept of Operation. 7th AIAAATIO Conference, September 18-20, 2007,
Belfast, Northern Ireland.
[4] Farley, T., Kupfer, M., Erzberger, H., AutomatedConflict Resolution: A Simulation Evaluation
Under High Demand Including Merging Arrivals,
7th AIAA-ATIO Conference, September 18-20,
2007, Belfast, Northern Ireland.
[5] Erzberger, H., The Automated Airspace Concept,
Proceedings of the 4th USA/Europe Air Traffic
Management R&D Seminar, Santa Fe, New
Mexico, 2001.

7. COCLUSIOS
We present the problem of the management of an UAV
mission into controlled air space. In this paper the problem
has been formalized as a TDTSP then a geometric model
has been provided to calculate the weight of the UAV
paths as a function of time. Finally a genetic algorithm has
been presented to solve the problem in real traffic
scenarios. Simulation results show how the proposed
geometric model efficiently defines the arches weights to
be used in the conflict resolution. The genetic algorithm
allows to deal efficiently with the problem’s timedependence, moreover it is useful to identify a proper
sequence of avoidance maoeuvres. Finally computation
time shows that this approach could be applied to future
real time applications.
8. REFERECES[1] K. Bilimoria, B. Sridhar, G. Chatterji, K. Sheth,and S. Grabbe, FACET: Future ATM concepts
evaluation tool, in 3rd USA/Europe Air Traffic

9131C.A. Persiani, S.Bagassi

Insertion in Civil Non-Segregated Airspace,
AIAA-2009-5840,
AIAA
Modeling
andSimulation Technologies Conference, Chicago,
Illinois, Aug. 10-13, 2009.

[6] Mondoloni, S., Conway, S., Airborne Conflict
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Airspace, AIAA-2001-4054, American Instituteof Aeronautics & Astronautics, Reston, Virginia,
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[7] Vivona, R. A., Karr, D. A., Roscoe, D. A.,
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[8] Karr, D.A., Vivona, R. A., Roscoe, D.A.,
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[9] Bagassi S., Bombardi T., Francia D., Persiani
C.A., 3D Trajectory Optimization for UAS“Copyright Statement
The authors confirm that they, and/or their company or
institution, hold copyright of all original material includedin their paper. They also confirm they have obtained
permission, from the copyright holder of any third party
material included in their paper, to publish it as part oftheir paper. The authors grant full permission for the
publication and distribution of their paper as part of the
EIWAC2010 proceedings or as individual off-prints from theproceedings.”

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