Automatic Collision Avoidance System: Design, development and flight tests

30th Digital Avionics Systems Conference – Seattle WA, USA – Oct 16-20 2011

Automatic Collision Avoidance System: Design, Development and Flight tests

Salvatore Luongo and Vittorio Di Vito Italian Aerospace Research Center (CIRA)

Giancarmine Fasano, Domenico Accardo, Lidia Forlenza, and Antonio Moccia

Department of Aerospace Engineering - DIAS, University of Naples “Federico II”


Outline of presentation

• CIRA TECVOL project

• System Architecture

• Algorithms

Obstacle detection and tracking

Automatic Collision Avoidance

• Development process

• First flight results

• Conclusion


30th Digital Avionics Systems Conference – Seattle WA, USA – Oct 16-20 2011 30th Digital Avionics Systems Conference – Seattle WA, USA – Oct 16-20 2011

Collision Sense & Avoid: ELOS

• “Equivalent Levels of Safety”

must be guaranteed to allow UAVs to access NAS (FAA Order 7610.4K Ch. 12 Sec. 9-2 – August 5th 2004);

• Human pilot vision shall be replaced by a Sense&Avoid system with augmented capabilities: Obstacle Detection and Tracking; Conflict Detection; Autonomous Collision Avoidance.

Focus of this presentation. Sense-related aspects discussed in detail in presentation 5C.3


CIRA TECVOL project


• TECVOL project goal is the development of enabling technologies for autonomous UAS flight.

• Functions to be demonstrated in flight with optionally piloted VLA aircraft:

Autonomous Flight Path Execution Autonomous Landing Collision Sense&Avoid Enhanced Remote Piloting

• Department of Aerospace Engineering (DIAS) has been a partner of

CIRA in the design of the collision avoidance sensor system


ACAS functional architecture


The autonomous collision avoidance (ACA) module is based on two core algorithms: • a customized multi-sensor tracking software ensures that intruders’ dynamics is properly estimated • a decision making logic is able to handle collision conditions by commanding the autopilot to perform adequate evasive maneuvers whenever a potential conflict is detected.


ACAS hardware architecture



Obstacle detection and tracking

• Multi-sensor tracking algorithm is based on an Extended Kalman Filter with linear dynamical model and non-linear measurement equation

• It can work in two operative modes:

• Stand-alone radar tracking

• Radar/EO tracking

• Estimates of position and velocity of eventual intruders are provided to the ACA system at 10 Hz

• Obstacle detection in EO images is carried out by means of a customized edge detection/labeling technique

• Further details provided in presentation 5C.3



• The Automatic Collision Avoidance algorithm on-line accomplishes two tasks:

• conflict detection

• conflict resolution

• In order to define the conflict situation, a proper volume (defined as “safety bubble”) is set around the intruder aircraft

• The ACA algorithm implements a spherical safety bubble whose dimensions are

where R is the radius of the sphere centered in the intruder aircraft, while R0 is the FAA minimum required safety distance.


Automatic Collision Avoidance

mftRR 8.30410002 0 ≈==


• The own aircraft is modeled (A/CA) as a point-of-mass object A

• The intruder, with its surrounding safety bubble, is modeled as a spherical object centered in the point B

• The relative velocity vector is considered:

• Distance at closest point of approach can be calculated as

• Collision condition is verified if and only if

Conflict detection


NEDX

NEDY

NEDZA

B

AV

BV

rABV

NEDX

NEDY

NEDZA

B

AV

BV

rABV

R NEDX NEDX

NEDYNEDY

NEDZNEDZA

B

AV

AV

BV

BV

rABV

ABV

NEDX NEDX

NEDYNEDY

NEDZNEDZA

B

AV

AV

BV

BV

rABV

ABV

R

.2 rVV

Vrd AB

AB

ABAB

−

⋅=

.0<< randRdAB

BAAB VVV

−=


Conflict resolution

• The strategy is based on the analytical solution of the following kinematic optimization problem:

to find the minimum change to be forced in speed vector of aircraft A (compatible with its envelope limitation and dynamic constraints) in order to avoid a collision with the spherical safety bubble surrounding aircraft B

• This leads to the formulation of a nonlinear constrained optimization problem:


>∃∈∈

≥∀≥−

∆∫

0],[)(],,[)(

,)()(..

)(min

maxminmaxmin

0

,, 0

Aa

AAdAAA

dA

Bd

A

t

t AV

ttVVtV

ttRtPtPts

dVdA

dA

dA

γγγ

ττγχ

Collision avoidance constraint Envelope limitations

Dynamical constraint (required change in velocity cannot be obtained instantaneously)


Conflict resolution


• Three simplifying assumptions are considered:

• change in speed vector occurs only at time t0, i.e., is a step function;

• aircraft have straight nominal trajectories with constant speed modules;

• no envelope limitations and dynamic constraints act on own aircraft

• After some mathematics (described in the paper) the optimization problem becomes

i.e., we impose the tangency of the new trajectory with the safety bubble

...

min,,

Rdts

V

dAB

AV dA

dA

dA

=

∆

γχ


Conflict resolution


• This problem admits an analytical solution and thus is very suitable for real time implementation

• Once the simplified optimization problem is solved, the constraints are considered, eliminating the candidate solutions which fall outside the envelope limitations of the vehicle

• The needed velocity vector is evaluated in real time at 10 Hz

• Finite settling time for the required changes in velocity vector, and sensing system uncertainty, are accounted for by enlarging the safety bubble considered in conflict detection and conflict resolution


Development process


• Developed algorithms were first tested in off-line simulations (Monte Carlo generation of collision scenarios) and hardware-in-the-loop tests

• Two main indexes were used to validate collision avoidance algorithms:

Minimum separation distance between the aircraft

Maximum deviation of own aircraft from nominal trajectory


Development process


• The system was then installed for flight demonstration and performance assessment onboard the customized very light aircraft FLARE, which is a modified version of the TECNAM P92 plane and represents a cost-effective platform to test innovative flight technologies


Flight results


• Several flight tests with a single intruder aircraft in the same category of FLARE were carried out in different environmental conditions

• Indeed, given the relatively low speed and the high maneuverability of this type of platforms, automatic collision avoidance can be tested in full safety conditions even in radar-only configuration. Thus, it was decided to carry out the first series of collision avoidance tests in this configuration.

• Some results are described in what follows


Flight results


5.4718 5.472 5.4722 5.4724 5.4726 5.4728 5.473 5.4732 5.4734 5.4736

x 104

0

500

1000

1500

2000

2500

3000

3500

GPS time of day (s)

rang

e (m

)

GPStrackerradar

• A single quasi frontal encounter is described in detail

• The two aircraft are initially on a quasi frontal collision course (altitude separation of 70 m for safety reasons)

• Intruder detected at 2090 m, track declared at a range of about 1600 m (time to collision of the order of 20 s)

• Conflict is detected and the avoidance maneuver is initiated soon after track declaration

• The intruder is reliably tracked during the maneuver though a very few valid radar detections are available and a huge amount of clutter echoes is detected

Range in the first part of the considered scenario


Flight results


-3000 -2000 -1000 0 1000 2000

-1500

-1000

-500

0

500

xRW [m]

y RW

[m]

WP 1

WP 2 WP 3

WP 4

WP 5

WP 6

True FLARE trajectory (GPS)Measured Intruder trajectoryMeasured distance at CPA = 274.7mMax Dist from Nom Traj = 504.6mTrue Intruder trajectory (GPS)True distance at CPA = 319.3m

The maneuver is mostly in the horizontal plane and consists in a smooth right turn


Flight results


5.4726 5.4728 5.473 5.4732 5.4734 5.4736 5.4738 5.474 5.4742 5.4744 5.4746 5.4748

x 104

140

160

180

200

220

240

260

280

300

320

340

GPS time of day (s)

dist

ance

at c

lose

st p

oint

of a

ppro

ach

(m)

post processing from GPS dataFAA safety bubble = 500 ftRsafety bubble = 1000 ft

Post processing of GPS data reveals how the avoidance maneuver increases the foreseen distance at closest point of approach, avoiding the collision threat:


Flight results


5.4725 5.473 5.4735 5.474 5.4745 5.475 5.4755

x 104

0

200

400

600

800

1000

1200

1400

1600

1800

GPS time of day (s)

rang

e (m

)

post processing from GPS dataRsafety bubble = 304.8 m (1000 ft)

Declaration Range [m] 1588

Maximum 4D deviation from nominal trajectory [m] 504.6

Rotation of the speed vector [°] 58.1

Maximum angle of attack [°] 4.4

Maximum vertical load factor [g] 1.17

Maximum angular velocity [°/s] 35.5

Synthetic performance indexes relevant to the considered collision avoidance maneuver

The achieved minimum distance is a little bit larger than Rsafety bubble (minimum deviation from original trajectory)


Conclusion

• This paper presented the fully Automatic Collision Avoidance System developed by the Italian Aerospace Research Center and the department of Aerospace Engineering of the University of Naples “Federico II”

• The focus of the presentation was on the conflict detection and decision making strategies

• First experimental results (based on radar-only tracking) have been discussed which were obtained from the flight test campaign carried out to validate the proposed ACAS in a real operational environment

• These results clearly demonstrate the effectiveness of the system in dealing with real-world conflict scenarios

• Future works will deal with results from multi-sensor based collision avoidance flight tests



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Automatic Collision Avoidance System: Design, development and flight tests

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