Airborne Conflict Modeling andResolution for UAS Insertion in Civil

Non-Segregated Airspace

Carlo Alfredo Persiani, Sara Bagassi
University of Bologna

Introduction
UAS Technologicalimprovement

ATM (Air TrafficManagement)

Advances in military technologies
lead to the use in civil context:•Surveillance•Fire fighting•...
•Dirty-Dull-Dangerous

Main goals in ATM:
•Avoiding collision betweenaircraft•Expediting and maintaining anorderly flow of air traffic
ATM Automation Process

UAS Insertion in Civil
Non-Segregated Airspace

Theoretical Framework
CD&R: Conflict Detection and Resolution Problem
Airborne

CentralizedNon-cooperative

CooperativevsTactical
State-based

Strategic
Intent-based

Problem Formulation/1
UAS has to visit a set of mission waypoints:
Maintaining separation with air traffic;
Optimizing a selected function (Minimum Fuel,Minimum Time).
Assumptions:
The piloted air traffic has priority on the unmannedone;
If a conflict occurs, the UAS will modify its route inorder to resolve it.

Problem Formulation/2
Two decisional dimensions:1)Best visit order of the m mission waypoints2)Type of avoidance manoeuvre:• Holding• Speed Variation• Vertical/Horizontal AvoidanceAlgorithm Overview

Pre -processing• For each
UAV subpath (i,j),started attime tWeightestimation•Wk(i,j)(t) isestimated•For each typek of manoeuvre

GeneticAlgorithm• Different“avoidancephilosophies”• Fitnessfunction:
minimumTime orminimum
FuelPre-processing: weight estimation
A conflict c occurs at the time t on the arch between i and j

The weight, for each avoidance manoeuvre, is calculatedw(ki, j ) (t )It depends on:•The delay generated by the manoeuvre (time optimization)•The fuel burnt during the manoeuvre (fuel optimization)

Manoeuvre modeling
Each conflict resolution manoeuvre has to be:•Feasible, taking into account UAV performances•Modeled, in order to understand how it affects time delay(or fuel consumption)
Type of avoidance manoeuvre:• Holding• Speed Variation• Vertical/Horizontal AvoidanceHoldingholding
w(i, j ) (t )a(hit, j  ( p1u _ p2u / v))

Speed variationw(scontri , j ) (t )ainc / red( p1u _ p2u ) / vm(t )Vertical/Horizontal avoidancew(Havoidi, j ) (t )anv t  n p tk K

Genetic algorithm: Pilot Philosophy
A first population of ηp UAV routes Ru is generated.
At each route a “Avoidance Philosophy” kr is randomly
assigned.
It consists in one of the following avoidance techniques:•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).

GA: Fitness function
1. An optimization criterion is chosen: minimum time orminimum fuel;2. The fitness (time or fuel consumption) of each route iscalculated - considering the weight of the arches
function of the departing time step;

3. The population is sorted by decreasing fitness.
4. The evolution to the next population starts.

GA: Evolution &Termination criteria
Evolution:•The first half of the population, sorted by decreasing fitness,is considered ”good”: the second part of the population isbuilt from the chromosome of the previous part.

Termination:•Repeat ηt times the evolution;•If the improvement in the last ηc individuals is less than 1%
then the evolution is terminated.

Simulation set-up• Real air traffic scenario: the TMA (Terminal Manoeuvring Area) of Milano
Linate (ICAO code LIML).
• Navigation Points as Radio-Assistance and Fix Points (radial and distance by aradio assistance) are reported; SID (Standard Instrumental Departure) route andSTAR (STandard arrival Route) of the airport are modeled using graphic tools.
• Air traffic data are acquired by AOIS (Aeronautical Operational Information
System) and Radar Track provided by ENAV S.p.A (Italian Agency for Air
Navigation Services).
• Position and altitude of 115 aircraft (arrival, departure and overflying traffic) are
simulated for 6 hours of simulation.
• The UAV target points are generated considering that some of the UAV target
points correspond to the aircraft navigation points, the others have been randomly
generated.

Simulation results/1

Simulation results/1

Simulation results/1

Simulation results/2
minimum fuel objective
Obj
Value

N° ofConflicts

Holding

Speed Red.

Speed Inc.

H Avoidance

V Avoidance

CPU time

20 targets

249

300003

3,78

25 targets

310

4

101

1

1

6,1230 targets

317

4011117,4135 targets

332

21000115,66

40 targets

354

51111117,47

45 targets

433

4

10003

21,98

ηp = 200, ηt = 3500, ηc = 330 , and Δt = 1 minute,computed on an IntelCore Duo 2GHz

Simulation results/3
minimum time objective
Obj
Value

N° of
Conflicts

Holding

Speed Red.

Speed Inc.

H Avoidance

V Avoidance

CPU time

20 targets

260

5

12

1

103,8325 targets

298

2

100

105,6730 targets

304

51020213,69

35 targets

325

54

100016,840 targets

404

40012112,48

45 targets

416

40101218,53

ηp = 200, ηt = 3500, ηc = 330 , and Δt = 1 minute,computed on an IntelCore Duo 2GHz

Conclusion• We present the problem of the management of an UAV mission into controlled
non-segregated air space.
• A genetic algorithm has been presented to solve the problem in real traffic
scenarios.

• The genetic algorithm allows to deal efficiently with the problem’s timedependence, moreover it is useful to identify a proper avoidance manoeuvre.
• Simulation results show how the proposed geometric model efficiently defines thearches weights to be used in the conflict resolution.
• Computation time shows that this approach could be applied to future real time
applications.

Thank you for your attention