A Laser Obstacle Warning and Avoidance System for Manned and Unmanned Aircraft

Abstract

In this paper we briefly review the development and flight test activities performed to integrate the Laser Obstacle Avoidance and Monitoring (LOAM) system on helicopter platforms and focus on the recent research towards the development of a new scaled LOAM variant for small-to-medium size Unmanned Aircraft (UA) applications. After a brief description of the system architecture and sensor characteristics, emphasis is given to the performance models and data processing algorithms developed for obstacle detection, classification and calculation of alternative flight paths, as well as to the ground and flight test activities performed on various military platforms. A concluding section provides an overview of current LOAM research developments with a focus on non-cooperative UA Sense-and-Avoid (SAA) applications.

Full text

This is the author pre-publication version. This paper does not include the changes arising from the revision, formatting and publishing process.
The final paper that should be used for referencing (available from: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6865998) is:
R. Sabatini, A. Gardi, S. Ramasamy and M. A. Richardson, “A Laser Obstacle Warning and Avoidance System for Manned and Unmanned
Aircraft”, in proceedings of IEEE Metrology for Aerospace (MetroAeroSpace) 2014, pp. 616-621, Benevento, Italy, 2014.
DOI: 10.1109/MetroAeroSpace.2014.6865998
A Laser Obstacle Warning and Avoidance System for
Manned and Unmanned Aircraft
Roberto Sabatini, Alessandro Gardi
and Subramanian Ramasamy
RMIT University – SAMME, Melbourne, Australia
roberto.sabatini@rmit.edu.au
Mark A. Richardson
Cranfield University – DAUK, Shrivenham, Swindon, UK
Abstract—In this paper we briefly review the development and
flight test activities performed to integrate the Laser Obstacle
Avoidance and Monitoring (LOAM) system on helicopter
platforms and focus on the recent research towards the
development of a new scaled LOAM variant for small-to-medium
size Unmanned Aircraft (UA) applications. After a brief
description of the system architecture and sensor characteristics,
emphasis is given to the performance models and data processing
algorithms developed for obstacle detection, classification and
calculation of alternative flight paths, as well as to the ground and
flight test activities performed on various military platforms. A
concluding section provides an overview of current LOAM
research developments with a focus on non-cooperative UA
Sense-and-Avoid (SAA) applications.
Keywords—Avionics; LIDAR; Obstacle Detection; Low-level
Flight; Nap-of-the-Earth; Obstacle Avoidance; Obstacle Warning
System; Sense-and-Avoid; Unmanned Aircraft
I. INTRODUCTION
Operations of a wide range of manned and Unmanned
Aircraft (UA) at low-level or nap-of-the-earth flying are
increasingly common. In the military context, this tactic
exploits the terrain profile to reduce the enemy ability of
visual, optical or electromagnetic detection and therefore
enhances the platform survivability. Unfortunately, the
adoption of this tactic has led to a significant increase in the
number of obstacle strike accidents. Similarly, in the civil
aviation domain, the widespread introduction of small-size
UAs designed for low-level flight applications is rising public
concerns regarding the overall safety of the people on the
ground, especially over populated and high air traffic density
areas. Low visibility due to bad weather or natural/man-made
obscurants is also responsible for a number of collisions with
fixed obstacles in low-level operations of both UA and manned
aircraft. While the development of an effective and certifiable
Sense-and-Avoid (SAA) capability is considered paramount,
significant research efforts are being devoted to the
development of cooperative, non-cooperative and hybrid
architectures capable of achieving this fundamental goal [1-7].
Research and Development (R&D) activities on laser-based
obstacle detection and avoidance systems have progressed
significantly over the past twenty years [8-13]. The Laser
Obstacle Avoidance and Monitoring (LOAM) system is a low-
weight/volume eye-safe 1.54 m navigation aid system for
low-dynamics platforms (e.g., rotary-wing and small/medium
size UA) specifically designed to detect potentially dangerous
obstacle placed in or nearby the flight trajectory and to warn
the crew in suitable time to implement effective avoiding
manoeuvres. LOAM was developed and tested by SELEX-ES
in collaboration with the Italian Air Force Flight Test Centre
[14-16]. Current research efforts are addressing the possible
scalability of LOAM for small/medium size UAs, with a
special focus on the potential contributions that this Light
Detection and Ranging (LIDAR) technology can provide to the
development of a cost-effective integrated sensor system for
non-cooperative SAA.
II. DESCRIPTION OF THE SYSTEM
LOAM is capable of detecting obstacles placed in or
nearby the aircraft trajectory, classifying/prioritizing the
detected obstacles, and providing obstacle warnings and
information to the crew (both aural and visual). As shown in
Fig. 1, the LOAM main components are the Sensor Head Unit
(SHU), the Control Panel (CP) and the Display Unit (DU).
The CP and DU may take various forms depending on the
specific manned/unmanned platform application.
Fig. 1. LOAM prototype for flight test experimentation.
The LOAM laser beam scans periodically the area around
the flight trajectory inside a Field of View (FOV) of 40° in
azimuth and 30° in elevation, with a field of regard capability
of ±20° both on azimuth and elevation, centred on the optical
axis of the system. The main optical axis can be oriented either
in the same direction of the platform heading or 20° left or
right. During every scan the laser beam is scanned with an
elliptical pattern in the FOV of the system, in order to detect
dangerous obstacles like wires, poles, buildings, etc. The
elliptical scan pattern is illustrated in Fig. 2. LOAM features
dedicated signal processing algorithms optimised to detect
low-level obstacles independently from the platform motion,
reconstructing the obstacle shape without using navigation data
(stand-alone integration) in slow-moving platforms with a
benign attitude envelope. Additionally, LOAM can be

This is the author pre-publication version. This paper does not include the changes arising from the revision, formatting and publishing process.
The final paper that should be used for referencing (available from: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6865998) is:
R. Sabatini, A. Gardi, S. Ramasamy and M. A. Richardson, “A Laser Obstacle Warning and Avoidance System for Manned and Unmanned
Aircraft”, in proceedings of IEEE Metrology for Aerospace (MetroAeroSpace) 2014, pp. 616-621, Benevento, Italy, 2014.
DOI: 10.1109/MetroAeroSpace.2014.6865998
integrated with the aircraft navigation sensors if required,
especially in platforms with high dynamics envelopes [4, 14,
17].
Fig. 2. LOAM scan pattern.
LOAM performs echo detection through an analog signal
processing that comprises an optical-electrical conversion, a
signal pre-amplification and a threshold comparison. Local
analysis and global analysis are performed for reliable obstacle
data processing. The local analysis is performed on single
echoes in order to determine range, angular coordinates and the
characteristics of the obstacle. The global analysis processes
groups of echoes detected over a scan period, with the related
information provided by the local analysis process, in order to
reconstruct the shape and determine the type of obstacles.
LOAM is capable of automatically classifying obstacles
according to the following classes: wires, poles/trees or
extended obstacles. Information relative to the detected
obstacles can be provided on a dedicated display whose screen
represents the FOV of the system. The integrated LOAM
architecture is illustrated in Fig. 3.
Fig. 3. Architecture of the LOAM avionic integration.
The SHU generates the laser beam scans, detects return
echoes and analyses these echoes in order to compute range,
coordinates and local geometrical characteristics (attributes) of
the obstacles they come from. The SHU then communicates
echo range, coordinates and attributes to the LOAM
Processing Unit. In order to discriminate the most dangerous
obstacles, LOAM employs three algorithms: calculation of
future trajectory; calculation of intersections with the
obstacles; determination of the alternative (optimal) flight
trajectory.
III. RANGE PERFORMANCE
The obstacle detection and classification logic implemented
in LOAM is based on two different measurement algorithms:
the first is optimised to process echoes generated by thin
objects, like wires and poles, the second is optimised to
process all the echoes generated by extended obstacles, like
houses, trees, woods and other solid objects. These algorithms
identify the boundaries of the obstacles; additional
neighbourhood criteria allow distinguishing “wire-class” from
“extended object” obstacle classes. The pre-processing
algorithms elaborate the range contrast between consecutive
laser returns and perform a pre-classification of detected
obstacles. The processing algorithms are conceived and
optimized for the quasi elliptical scanning pattern described
before. Processing of available data has shown that the
implemented algorithms are capable of detecting and
classifying the different obstacles of interest. A criterion to
determine the system detection range performances in the
worst conditions and with the worst obstacle scenarios (i.e.,
small wires with low reflectivity) has been defined. For initial
performance calculations, the wire obstacle detection
capability of LOAM is modelled by the following simplified
Signal to Noise Ratio (SNR) equation [4]:
 
NEPDRRP
deLLAE
SNR
D
W
R
rTrp






2
2
4
(1)
where:
EP = output laser pulse energy
Ar = receiver aperture
LT = transmission losses (including beam shaping)
Lr = reception losses (including optical filter)

= atmospheric extinction coefficient
dW = wire diameter

= wire reflectivity
PD = pulse duration
R = obstacle range

= beam divergence (l/e2)
D = initial beam diameter
NEP = noise equivalent power
The extinction coefficient (

) is calculated using the
empirical model suggested by Elder and Strong [18] and
modified by Langer [19]. Additionally, for propagation in
rainy conditions, the equations developed by Middleton are
adopted [20]. These mathematical models are used to obtain
an estimate of the laser beam atmospheric transmittance (

atm)
at the centre of the ith propagation window for any slant-path, if
the meteorological range V and the absolute humidity are
known [4]. Suitable correction functions have also been
developed in [10] taking into account the experimental results
obtained during ground and flight trails with laser systems
operating at λ = 1.54 m test activity results. In order to
estimate the SNR from experimental LOAM detector current
measurements (iSIG), obtained with certain obstacle ranges (R)
and incidence angles (

), SNR is expressed as:

This is the author pre-publication version. This paper does not include the changes arising from the revision, formatting and publishing process.
The final paper that should be used for referencing (available from: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6865998) is:
R. Sabatini, A. Gardi, S. Ramasamy and M. A. Richardson, “A Laser Obstacle Warning and Avoidance System for Manned and Unmanned
Aircraft”, in proceedings of IEEE Metrology for Aerospace (MetroAeroSpace) 2014, pp. 616-621, Benevento, Italy, 2014.
DOI: 10.1109/MetroAeroSpace.2014.6865998
 







NOISE
SIG
i
Ri
SNR

,
log20
(2)
where:
La
h
R
aWT
SIG RK
P
R
eDdP
i1
43
2
3



 
(3)
PT = transmitted power
Ph = amplifier gain
Da = aperture diameter
Ka = aperture illumination constant = 
The noise current terms are modelled as:
2222
RADKBKTHNOISE iiiii 
(4)
where:
iTH = thermal noise current
iBK = background noise current
iDK = dark noise current
iRA = receiver amplifier noise
According to the calculation by Rice [21], the average
resulting False Alarm Rate (FAR) for the LOAM system is
given by:







 2
2
2
exp
32
1
n
t
I
I
FAR

(5)
where:

= Electrical pulse length
It = Threshold current
In = Average noise current
The detection probability, Pd is determined using pure
Gaussian statistics and is given by:















 

n
n
n
n
I
II
dI
i
d
I
i
P
n
nt 2
2
exp
1
2
2
2

(6)
where:
In = average signal current
in = instantaneous noise current
The false alarm probability (Pfa) is given by:
 






(7)
and the cumulative detection probability (PD) is given by:
 



 m
i
iM
d
i
d
i
MD PPCP
0
11
(8)
where:
M = number of possible detections
m = minimum number of detections required
Ground trials of the LOAM system were performed in
order to verify the system detection performance in various
weather conditions, and to test the validity of the mathematical
models used for performance calculations [4]. Two different
platforms were used for the tests: NH-300 and AB-212
helicopters. The LOAM CDU was installed at the centre of the
AB-212 glare-shield, in order to be accessible to both pilot and
co-pilot. For the AB-212 test campaign, the LOAM Main
Control Unit (MCU) was installed in the centre of the middle-
console, in a position accessible to both pilot and co-pilot.
During the test flights, a flight test engineer also operated a
computer, linked to the LOAM system, displaying in real-time
a 3-Dimensional image reconstructed using the LOAM data.
All images were recorded for successive data analysis. The
LOAM range performance were in accordance with the
predictions and the LOAM detection/classification data
processing algorithms were validated (detection and
classification of all obstacles encountered was performed
successfully). The following characteristics were defined for a
wire type obstacle according to LOAM operational
requirements:
 Diameter: 5 mm  DW  70 mm
 Shape: twisted or round
 Reflection: Purely diffuse (Lambertian)
 Reflectivity:  20% ( = 0)
The reference environmental parameters were set as
follows:
 Visibility: V  800 m
 Humidity: RH  100%
 Temperature: T  50 °C
 Rain: Light/Medium/Heavy
 Background: PB = 50 W/m2 sr m
Tables 1 lists the detection range test results obtained for
cable obstacles of 10 mm in diameter, in dry weather
(visibilities of 800 m, 1500 m and 2000 m), and at incidence
angles of 90 and 45. The experimental detection ranges also
exceeded the ESLM model detection ranges and this is due to a
slight overestimation of the extinction coefficient at λ = 1.54
m as detailed in [22]. LOAM history function stores the data
of the obstacles for a defined time interval and deletes them
when they are outside the platform possible trajectories. It was
verified that the LOAM history function was correctly
implemented to cover the flight envelope of the selected test
platforms. Further trials have been also performed to
investigate the performance of an ad-hoc control panel and
display unit combination suitable for the LOAM system.
TABLE I. DETECTION RANGE OF 10 MM DIAMETER CABLE
Visibility
Incidence
Angle
ESLM Model
Detection
Distance
Actual
Detection
Distance
Minimum
Specified
Detection
Distance
800 m
90
727 m
788 m
550 m
1500 m
90
813 m
934 m
630 m
2000 m
90
996 m
1145 m
780 m
800 m
45
553 m
634 m
460 m
1500 m
45
595 m
641 m
500 m
2000 m
45
691m
733 m
640 m
IV. AVOIDANCE TRAJECTORY GENERATION
When the impact warning processing of LOAM determines
that the currently flown trajectory involves a risk of collision,
the avoidance trajectory generation algorithm is triggered to
determine the necessary manoeuvres for safe avoidance, and
an indication about the alternative (optimal) direction to fly is
subsequently displayed to the pilot. Different algorithms for
the generation of avoidance trajectories have been
implemented in LOAM for rotorcraft and fixed-wing
platforms. All algorithms are based on platform dynamics as

This is the author pre-publication version. This paper does not include the changes arising from the revision, formatting and publishing process.
The final paper that should be used for referencing (available from: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6865998) is:
R. Sabatini, A. Gardi, S. Ramasamy and M. A. Richardson, “A Laser Obstacle Warning and Avoidance System for Manned and Unmanned
Aircraft”, in proceedings of IEEE Metrology for Aerospace (MetroAeroSpace) 2014, pp. 616-621, Benevento, Italy, 2014.
DOI: 10.1109/MetroAeroSpace.2014.6865998
well as all obstacles position, dimensions and types. The active
obstacle set includes the obstacles in the current FOV as well
as the ones previously detected and recorded by the LOAM
history function, described in [16], and currently outside of the
LOAM FOV. The original avoidance trajectories generation
algorithm for rotorcraft platforms was described in [15]. The
algorithm for rotorcraft was based on the determination of the
smaller possible correction manoeuvre to attain a safe
avoidance trajectory. In this paper we present the key aspects
of the avoidance trajectory generation algorithm for small-to-
medium fixed-wing UA applications. The approximated
dynamic model of the fixed-wing UA platform adopted in the
LOAM avoidance trajectory generation algorithm is based on
the following assumptions:
 The UA is modelled as a point-mass rigid body with
three linear degrees of freedom (3DOF);
 The inertial reference system is centred on the initial
position of the UA point-mass, with the X axis pointing
eastward, the Y axis northward and the Z axis normal to
the ground (relative navigation frame);
 The UA is subject to a constant gravitational
acceleration  parallel and opposite to the
Z axis;
 The mass of the vehicle, , is considered constant along
the avoidance trajectory;
 The airspeed of the UA, , expressed as True Air Speed
(TAS), is tangent to the aircraft trajectory. The assumed
initial TAS is .
 The wind velocity, , is a vector,
which from the airborne perspective can be seen as
effectively accounting for the perceived obstacle
motion.
The resulting system of differential equations for 3DOF
vehicle dynamics is:













 



(9)
where  is the propulsive thrust [N], (the
adopted model depends on the actual propulsive
configuration);  is the aerodynamic drag force [N]; 
is the flight path angle [°];  is the track angle [°] and  is the
bank angle [°]. During the avoidance manoeuvre, the load
factor is set close to the certified flight envelope limits of the
UA. In our case these correspond to  for the pull-
up. We then assume that during the entire approach to the
obstacle, the vehicle control system provides a linear variation
of, up to the assumed maximum bank angle,  and is
expressed as:  
 (10)
The maximum roll rate was set as  . The
maximum bank angle was calculated as:
 
 (11)
The wind velocity vector is derived from the sensed
relative motion of the obstacle based on differential geometry,
as in [23]. In order to provide the fast and reliable performance
required for our safety-critical task, the avoidance trajectory
generation is based on simplified geometric shapes. When the
distance between two detected obstacles is comparable with
the calculated uncertainty values, or with the UA dimensions,
the algorithm combines the two obstacles in a single avoidance
volume. The subsequent step involves the selection of the
optimal trajectory from the generated set of safe trajectories,
which is then fed to the aircraft guidance subsystems. The
implemented decision logic is based on minimisation of the
following cost function:
  
  (12)
where:
  is the estimated distance of the generated
avoidance trajectory points from the avoidance volume
associated with the obstacle.
   is the estimated minimum distance
of the avoidance trajectory from the avoidance volume.
   is the time at which the safe avoidance
condition is successfully attained.
 
 is the specific fuel consumption.
  is the thrust profile.
  are the weightings attributed to time,
fuel, distance and integral distance respectively.
In time-critical avoidance applications (i.e., closing-up
obstacles with high relative velocities) appropriate higher
weightings are used for the time and distance cost elements.
V. ERROR ANALYSIS
The error analysis is performed to determine the
uncertainty volume resulting from navigation and tracking
errors. Since the motion data supplied from navigation system
are, like every measure, affected by an error, we evaluate how
these errors affect the obstacle. To do so, Gaussian error is
added to every datum and a statistic of the position error is
calculated. The standard deviation of the LOAM detection and
tracking error in each cardinal direction is expressed as:
 


 (13)
Given the different values of uncertainty associated with
the three cardinal directions, an ellipsoidal avoidance volume
is implemented in the algorithm and is given by [24]:


 
 (14)
where ,  and  represent the standard deviation of
the errors in the three cardinal directions. The navigation
errors are derived from the UA dynamics model and the
tracking errors are a consequence of the errors from sensors
employed for LOAM. In order to assure adequate safety levels,
a separation buffer is introduced, which inflates the ellipsoidal
avoidance volume associated with the obstacle. In particular,

This is the author pre-publication version. This paper does not include the changes arising from the revision, formatting and publishing process.
The final paper that should be used for referencing (available from: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6865998) is:
R. Sabatini, A. Gardi, S. Ramasamy and M. A. Richardson, “A Laser Obstacle Warning and Avoidance System for Manned and Unmanned
Aircraft”, in proceedings of IEEE Metrology for Aerospace (MetroAeroSpace) 2014, pp. 616-621, Benevento, Italy, 2014.
DOI: 10.1109/MetroAeroSpace.2014.6865998
to provide a confidence level of 95%, the uncertainty
associated with the position of an obstacle is calculated as
twice the standard deviation (i.e. two-sigma) of the total
obstacle detection and tracking errors. The navigation and
tracking errors are statistically combined to obtain the
uncertainty volume with respect to the obstacle reference
frame.
VI. SIMULATION AND RESULTS
Simulation activities were performed in realistic scenarios
to assess the performance and validate the avoidance trajectory
generation algorithm for fixed-wing UA. A typical test case is
depicted in Fig. 4.
Fig. 4. Small fixed-wing UA approaching a power transmission
line obstacle and performing wire detection.
The LOAM equipped UA is flying at an altitude z = 100 m
Above Ground Level (AGL) and approaching a power
transmission line consisting of a tower and a number of wires
of 10mm in diameter both in front of the UA flight path and on
the left side. The altitude of the lowest wire is 95 m AGL and
the altitude of the highest wire is 115 m AGL; the wires are
separated by about 6.5 m vertically and 5 m laterally. The
tower is 120 m high. The transmission lines lie approximately
70 m in front and 40 m to the side of the UA. The original
horizontal flight trajectory would lead to a collision with the
power transmission line. After a successful detection of all
wires, the algorithm calculates the distances to each of them.
As previously described the algorithm then recognises that the
calculated distances are all comparable with the UA size and
therefore combines all frontal wires in a single avoidance
volume, and similarly all lateral wires in an additional separate
avoidance volume. The centre C1, C2 and C3 positions and the
semi-major axis a1,2,3, b1,2,3, c1,2,3 of the resulting ellipsoidal
avoidance volumes are then calculated. In particular C1 = [60
m, -30 m, 105 m]; a1 = 15 m, b1 = 100 m, c1 = 25 m, C2 = [30
m, 30 m, 105 m]; a2 = 100 m; b2 = 15 m; c2 = 25 m; C3 = [60
m, 30 m, 80 m]; a2 = 20 m; b2 = 20 m; c2 = 80 m. A
representative set of avoidance trajectories generated following
these assumptions, is depicted in Fig. 5.
Fig. 5. Valid and conflicting generated trajectories.
Fig. 6 shows the distance of all points in the calculated UA
trajectories from the avoidance volumes boundaries.
Fig. 6. Absolute distance of the generated trajectories from the ellipsoidal
avoidance volume boundary.
VII. CONCLUSIONS AND FUTURE WORK
Current and future development and test activities will
address the HMI2 design, integration with forward looking
sensors, and Night Vision Imaging Systems (NVIS) [17, 25-
27]. A scaled version of LOAM is currently being developed
for small-to-medium size UA platforms. This research will
proceed in parallel with the development of bistatic and
monostatic LIDAR systems for turbulence detection and
atmospheric pollutant concentration measurements [10, 28,
29]. Implementation of suitable integrity monitoring and
augmentation technologies is also considered a fundamental
step in the development of effective and certifiable obstacle
avoidance systems [30, 31]. This research is also focusing on
the LIDAR potential contributions to integrated avionics
architectures for non-cooperative UA Sense-and-Avoid (SAA)
[23, 32]. In particular, the possible integration of LIDAR with
other UA avionic sensors and systems is being studied and
further research efforts will address the non-cooperative and
cooperative SAA functionalities required for 4-Dimensional
Trajectory Intent Based Operations (4DT-IBO), in line with

This is the author pre-publication version. This paper does not include the changes arising from the revision, formatting and publishing process.
The final paper that should be used for referencing (available from: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6865998) is:
R. Sabatini, A. Gardi, S. Ramasamy and M. A. Richardson, “A Laser Obstacle Warning and Avoidance System for Manned and Unmanned
Aircraft”, in proceedings of IEEE Metrology for Aerospace (MetroAeroSpace) 2014, pp. 616-621, Benevento, Italy, 2014.
DOI: 10.1109/MetroAeroSpace.2014.6865998
SESAR and NextGen requirements [33, 34]. Additionally, both
3-Degree-of-Freedom (3-DOF) and 6-Degree-of-Freedom (6-
DOF) aircraft dynamic models will be evaluated and compared
for a possible inclusion in the real-time avoidance trajectory
generation algorithms [35].
ACKNOWLEDGMENT
The original development and testing activity was funded
by the Italian Ministry of Defence (MoD) under R&D contract
No. 2097-22-12-2000. The authors wish to thank the personnel
of SELEX-ES, LOT-ORIEL, the Italian Air Force
Experimental Flight Test Centre, and the Italian MoD Laser
Test Range (PILASTER) for helping in the preparation and
execution of the ground and flight test activities.
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