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An Architecture for UAV Team Control


Abstract and Figures

Recent years has seen a widespread interest in the use of Unmanned aircraft vehicles for military applications. These UAV's can be used in many applications such as surveillance, information gathering, suppression of enemy defenses, air to air combat, mapping building and facilities etc. In this paper, we present an architecture with the necessary algorithms that we have implemented to control a team of UAVs to search for targets such as SAMs, ground troops, artillery, tanks etc in a given region.
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Sivakumar Rathinam Marco Zennaro ∗∗
Tony Mak ∗∗∗ Ra ja Sengupta ∗∗∗∗
Graduate Student, CEE Systems, Berkeley
∗∗ Graduate Student, CEE Systems, Berkeley
∗∗∗ Systems Analyst, PATH, Berkeley
∗∗∗∗ Assistant Professor, CEE Systems, Berkeley
Abstract: Recent years has seen a widespread interest in the use of Unmanned
aircraft vehicles for military applications. These UAV’s can be used in many
applications such as surveillance, information gathering, suppression of enemy
defenses, air to air combat, mapping buildings and facilities etc. In this paper, we
present an architecture with the necessary algorithms that we have implemented
to control a team of UAVs to search for targets such as SAMs, ground troops,
artillery, tanks etc in a given region.
Keywords: UAV, search, architecture
Unmanned Aerial Vehicles (UAV’s) has received
significant attention in recent years for military
applications {Pachter, 98} {Bortoff, 99} {Mclain,
99} {McLain, 00} {Chandler, 01} {Nygard, 01}
{Passino, 01} {Lee, 03}. The motivation behind
this interest is to realize a vision where these
vehicles cooperatively accomplish missions such
as search and attack {Murphey, 99} {Polycarpou,
01} {Beard, 02} {Jaques, 03} {Bellingham, 03}.
The reason for the widespread interest are the
many advantages that the unmanned vehicles
have over manned vehicles such as reduced human
risk, manoeuvrability, and superior cooperation.
In this paper, we primarily concentrate on the
search mission and describe a architecture with
the algorithms that we have implemented to re-
alize the same. A detailed review of the search
literature is presented in {Polycarpou, 01}. Our
contributions of this work compared to the previ-
ous work are:
Modularity with which the different types of
sensors and the strategies are coupled in the
A safe flight path planning algorithm that
allows sufficient time for the sensors for basic
operations such as imaging and image pro-
cessing. Also this algorithm satisfies the the
yaw rate and sensor range constraints of the
1.1 Basic Approach
The primary step before developing search strate-
gies and its architecture is to first come up with
a way of representing the threats and to answer
what kind of information is shared between the ve-
hicles. To address these questions, we use a proba-
bility map to represent the threats. As the vehicles
move in the unknown regions, the sensors collect
new information (location and the type of targets)
about the environment. This new information is
updated with the probablity map using bayes rule.
The probability maps of all the different types
of the threats is used to calculate the risk map
which indicates the risk of being shot at any given
point in the desired search region. This risk map
is shared by all the vehicles and is used for path
planning. Following the path based on the par-
tially known risk map, would damage or destroy
the vehicles if they enter regions where Surface
to Air Missiles (SAMs) are present. Therefore,
we reduce the forward speed of the vehicles by
refining the nominal path, thus allowing sufficient
time for image processing and target recognition.
This refinement of the nominal path is basically
done by a safe flight design which is discussed in
the later sections.
Two strategies for navigating in an unknown re-
gion are outlined in this paper: one for reaching
a destination and an other for searching a given
area. All the ideas presented above forms the
backbone for the UAV team architecture. Ideally,
what we want is an architecture that allows hu-
man interface at each level of control and also
can automatically make its decisions if required
as to which speed mode or sensor choice to use to
suitably realize a mission. The architecture should
allow an human interface to pick and choose the
regions that is required to be searched.
The paper is organized as follows: The problem
setup which includes the capabilities of the various
sensors, threat models, assumptions on the motion
of the vehicle is presented in 2. The threat map
representation and the risk function calculation
is presented in section 3. Safe flight algorithm is
discussed in 4. Section 5 and 4 presents the two
main search strategies. The main architecture is
explained in section 6 and the properties are dis-
cussed in 6.5. Simulation results are presented in
section 7 and the paper concludes with comments
and future work.
2.1 Sensors and their capabilities
Sensors such as Electro-optical(EO), Infrared(IR)
and Synthetic Aperture Radar Systems can be
used onboard of these vehicles to collect infor-
mation about the partially known or unknown
environments. The information that we are inter-
ested from these Surveillance and Reconnaissance
(S&R) systems are the type of threats and the
location of the threats. Each of these sensors has
its own advantages and disadvantages and cannot
be used for all applications. {Leachtenauer, 01}
proposes a guideline in the selection of a sensor
for a S&R application. This is presented in figure
1. Retrieving this useful information requires a
sufficient amount of processing time (varies for
different sensors) for performing basic operations
Heavy cloud, cover/
rain or long range? Daytime?
Electro Optical
High Resolution
no no
Synthetic Aperture
All weather conditions
Low Resolution
Fig. 1. General guidelines for sensor selection
{Leachtenauer, 01}.
such as image formation and image processing. In
this paper we assume that if sufficient amount of
time is spent on imaging and processing an area,
then the probability of detecting a target located
in that area is 1.
2.2 Threat Model
In this work, we assume that the only threat
that can destroy the vehicles are the stationary
SAM launchers. We also assume that the range
of the search radars of the launchers are larger
than its fire control range. Therefore, as soon as
the vehicles enter their search radar zone, they
are locked for destruction and the weapons with
its tracking radars are instantly fired when the
vehicles enter the fire control range. Hence, a
vehicle is instantly destroyed when they fall in the
range of the fire control radars of the SAMs. The
only way to keep the vehicles safe is to avoid the
threat regions of all the launchers.
2.3 Assumptions on the Motion of the UAV
The vehicles are assumed to fly at a constant
height. A UAV at any time t can be specified by its
coordinates {x(t),y(t),θ(t)}. We treat each UAV
as a Dubins car (simple stick model), travelling
at a constant speed with a bound on its yaw
rate. Let vdenote the velocity of the UAV. Let
ωrepresent the bound on the yaw rate of the
UAV. The motion of the UAV is governed by the
following equations:
x(t) = vcos θ(t) (1)
y(t) = vsin θ(t) (2)
θ(t) = Ω where ²[ω, +ω] (3)
The targets consists of different types of targets
such as Surface to Air Missile Launchers (SAMs),
Surface to surface missiles, tanks, etc. A set of N
targets is represented as:
T argets ={target1= (type1,(x1, y1)), ..
, ..., targetN= (typeN,(xN, yN))}(4)
Here, the targetiis of typeiand is situated at a lo-
cation (xi, yi). Let the targets be distributed over
kareas A1, A2, ..., Ak.Ntj targets of type tare
assumed to be independently and uniformly dis-
tributed over Area Aj. The information about the
targets is represented as a probability distribution
of the random variables N, typei,{xi, yi}. This
probability distribution of targets is expressed as:
Pthreat(T ar gets) =
ptj (type =t, (xi, yi))Ptj (Ntj )(5)
in which,
ptj (type =t, (xi, yi)) = ½|Aj|1,(xi, yi)Aj
0. otherwise (6)
At the start of the mission, this information about
the targets or the probability distribution is also
referred as the Initial Preparation of the Bat-
tlefield (IPB). As the vehicles move through the
region, sensors on the vehicles gather information
about the targets. We use Bayes rule the update
the distribution with the incoming sensor infor-
3.1 Risk Function Calculation
The instantaneous risk function r(x, y, P ) at any
point (x, y) given the probability distribution P
of the targets is given by:
r(x, y, P ) =
j=1 X
Ntj =0 X
ft(|(x, y)(xn, yn)|)|Aj|1dxndyn
where, the function ftis chosen as follows: ft(r) =
1 if rRLand ft(r) = 0 if r > RL.
A detailed description of a general safe flight
algorithm is presented in {Rathinam, 04}. Here,
we just present the gist of designing a path for
a vehicle required to travel from waypoint Ato
waypoint B0. As the vehicle moves from Ato B0,
let the sensor on the vehicle collect information
about the area S1S2S3S4as shown in the figure
2. If the vehicle travels straight from Ato B0,
since the forward speed of the vehicle is generally
large (200km
hr ), the sensor on the vehicle may
not have enough time to process the information.
Therefore, the vehicle may not be able to take
any evasive action in the presence of any SAM
launcher in the area S1S2S3S4. So, the vehicle
has the risk of being shot. To avoid this, we
increase the length of the path from Ato B0to
give sufficient time for the sensor to process the
information. This is done by piecing together a
series of semi circles and half ellipses as shown
in figure 3. The minor axis xand the major
axis yof the ellipses are the variables and are
chosen to satisfy all the required constraints. Let
us denote the generated path from Ato Bwith
nsuch semicircles and half ellipses be denoted by
G(A, B, n). The values of xand yare chosen to
satisfy the following constraints:
Minimum curvature constraint: Choosing y=
xr where r=v
ωis the radius of the semi-
cirle satisfies this constraint. Refer to the
appendix for the proof.
Sensor timing constraint: As shown in the
figure 4, the sensor on the UAV, as it moves
from Ato B0, is required to collect and
process the information of the targets present
in the area S1S2S3S4. The constraint here is
that, the flight time required to travel from
Ato B0should be sufficient enough for the
sensor to process the collected information.
Let RLbe the firing range (km)of the SAM
launchers present in the area of interest. Let
τindicate the image processing rate per unit
area ( hr
km2) of the sensor on the UAV. Then,
2(RL+d)τ(πr +L(x, y))
where, L(x, y) indicates the length of the half
Sensor range constraint: The range of the
sensor RSshould be large enough (>length
P S2in figure 4)for the sensor on the UAV to
reach the entire area S1S2S3S4. That is,
(RL+y+ 2x)2+ (RL+ 2y)2R2
5.1 Strategic Search
The objective in strategic search is to find a fea-
sible path (if there exists one), devoid of threats
to a given destination. At the start of the mission,
only partial or no information is known about the
location of the launchers. {Stentz, 96}proposed a
efficient path planning algorithm for vehicles for
A B’
area to be
Fig. 2. Reason behind safe flight design.
Direction of motion
d = nx
connecting ‘n’ semicircles and half ellipses to reach the waypoint B
Fig. 3. Connecting semicircles and half ellipses to
augment the length of the path.
A B’
Safe distance = RL+y
area to be
Fig. 4. Scanning area for the vehicle as it moves
reaching a destination in partially known environ-
ments. The algorithm is functionally equal to the
brute-force optimal strategy that is presented as
Follow the minimum risk path using the
information known at the start.
If any obstacle is identified, update the map
with the collected information and follow the
new minimum risk path to the destination
(from the current location). Repeat this step
until you reach the destination or all the
paths to the destination are blocked by ob-
The above algorithm has the completeness prop-
erty that the vehicle will find a feasible path to
the destination if there exists one.
5.2 Threat Search
The aim of the threat search is to find all the
SAMs in a given area. For this search, we use a
adaptive space filling algorithm as follows:
Generate a space filling curve that covers the
given area
Fly the generated curve until one of the
following happens:
·If a new threat is observed, then fly the
minimum risk path to the next destina-
tion on the curve. Repeat this process
until all the destination points in the
curve are used.
·Final destination on the curve is reached.
6.1 Architecture overview and design guidelines
The system goal is to employ a team of UAVs to
search collaboratively an unknown environment
while avoiding obstacles and threats. The UAVs
have different capabilities (e.g. different sensors
accuracy and type, speed, endurance), that have
to be used efficiently to complete the mission.
Given the nature of the scenario we design the
system to have the following four properties: mod-
ularity, safety, resilience, scalability. The system
consists of heterogeneous components, e.g. dif-
ferent UAVs equipped with different sensors. We
want this heterogeneousness to be transparent to
the system. In order to achieve this goal we made
the design modular: we decompose the system into
logical blocks (e.g. sensor controller, UAV autopi-
lot) and we define the interfaces between them.
In this particular scenario the set of available
resources dynamically changes: resources may be
added on the run, while other may be destroyed or
damaged. The system must be able to cope with
these continuous changes. It has to be scalable, in
order to be able to operate with all the available
resources and accommodate additional resources.
It has to be resilient to failures in order to con-
tinue to operate even when some of the resources
are destroyed or damaged. The system perfor-
mances should degrade gracefully. Scalability and
resilience are addressed using a layered architec-
ture that offers different level of control. Since the
environment is unknown and potentially unsafe,
the system should try to minimize destruction or
damage to the UAVs. Moreover the output of the
available sensors is subjects to errors and long pro-
cessing delay. The sensor inaccuracy problem has
Resource allocation/scheduling
Operation decomposer
Operation monitor
Update Risk Map
Team Manager
Human interface
Fig. 5. Overview of the Architecture.
been addressed by sharing the threat information
among the members of the team. The processing
delay problem has been addressed by having every
single UAV adopting a safe flight strategy (as
described in section 4). The high level structure of
the system architecture is given in figure 5. The
system consists on three main components: the
Team Manager, the UAV Managers and the Sen-
sor Information Processing Unit(Risk map, Target
distribution modul e, Prob update module ). The
UAV Managers offers a higher level of control of a
single UAV to the Team Manager: instead of flying
the UAV waypoint by waypoint, we can command
it to execute a more complex task (e.g. flying to
an area while avoiding obstacles or search an area
for threats). The Sensor Information Processing
Unit is used to share and fuse the information
gathered by different sensors from different UAVs.
The Team Manager block coordinates and mon-
itors the team members, and it offers a higher
level of control. Instead of controlling every single
UAV using tasks we can assign to the team a
mission (e.g. efficiently search an area or look for
a minimum risk corridor).
6.2 Team Manager
The Team Manager offers a higher level of control
above the single UAV task control level. It allows
the user to control a team of UAVs to collabora-
tively perform a mission, such as target search in
an unknown environment and safe corridor discov-
ery. The Team Manager components is also given
in figure 5. A mission is broken down by the Oper-
ation Decomposer into a sequence of tasks. The re-
source allocation block then allocates the available
resources to perform these tasks. The dispatcher
assigns the tasks to the allocated resources. The
Operation Monitor monitors the system status.
If some of the resources are destroyed, or new
ones become available, it prompts the Resource
Allocator to redistribute the task load among the
new set of resources. This component allows the
system to dynamically adapt to changes.
6.3 UAV Manager
The current available systems, for example the
Cloudcap Piccolo system offer a low level of con-
trol, usually a waypoint navigation control with-
out obstacle and threat avoidance. The UAV man-
ager is built on top of such systems in order to
provide a higher level of control as well as obstacle
avoidance. The structure of the UAV manager
is described in figure 6. The Sensor Manager
deals with the complexity of the sensors using
the information provided by the Dynamic Path
Planer (e.g. the direction and the speed of the
motion). It takes care of the sensor aiming and
ensures that the sweeping pattern does not leave
behind unsearched areas. The autopilot deals with
the waypoint navigation (without obstacle avoid-
ance). An example of such an Autopilot is the
just mentioned Cloudcap Piccolo system. On top
of it, we built a safe controller. The Dynamic
Path Planer (DPP) creates a minimum risk path
(using the risk map) between the current position
and the destination given by the task manager,
avoiding known obstacles and threats. The task
manager provides a task execution service to the
above layers.
If any threat is detected, the safe controller stops
the autopilot from following the assigned path and
interrupts the Dynamic Path Planner for a re-
vised nominal path. Meanwhile, the safe controller
Risk Map
UAV manager
Dynamic Path Planner
Safe Flight
Task Manager
Sensor Manager
Autopilot Sensor
Controller Observation Update
Nominal path
Fig. 6. UAV Manager
generates dummy paths that forces the vehicle to
just fly around a fixed point until it receives a
new nominal path from the DPP to follow. This
interrupt from the safe controller ensures that
irrespective of the decisions that are taken at the
higher levels of the architecture, it always controls
the safety of the vehicle.
6.4 Architecture implementation using an example
Lets consider an example of a search area mission.
The following are the break up of the functions of
each block in the proposed architecture:
The Operation decomposer produces the fol-
lowing list of tasks: fly to the area to be
searched, search the area and fly back home.
The Resource Allocator assigns each of these
tasks to the team of UAVs available. The
following three tasks are assigned to every
UAV: a fly to to the search area, a search
area task, where the area to be searched is
a fraction of the total area to be searched
and a fly to the base. If any of the UAVs
are destroyed or damaged, the Operation
Monitor prompts the Resource Allocator to
redistribute the areas to be searched among
the remaining UAVs.
Each UAV manager is assigned with an area
to searched. The Task Manager produces a
set of way points to be followed based on a
adaptive space filling algorithm for the given
search area.
Dynamic path planner generates the mini-
mum risk path (nominal path) to any desti-
nation way point specified by the task man-
ager. The safe flight controller refines the
path with the safe flight design.
Apart from the functions that the sensor
manager performs as mentioned before, a
human interface is provided to pick the type
of sensor to be used in the mission. SAR
sensors can be first used to identify all the
main threats that can destroy the vehicle
such as SAMs. Once they are identified, then
EO sensors can be used to classify targets
such as trucks or buses accurately.
A simulation of the above search mission to find all
the SAMs is shown in figure 7. One of the other
missions that we implemented was the strategic
search mission. This mission would be useful when
the aim is to find a feasible safe path to the
destination. In this mission, the only real part that
is different from the search mission is the Task
Manager. In this case, the Task Manager has just
one destination point to reach. A simulation of
this mission is shown in figure 8. The following
subsection lists the properties of the architecture
that we presented above.
6.5 Properties of the Architecture
The interrupt that the safe controller has basi-
cally provides the completeness property of the
strategic search that if a there exists a safe path,
the components in the architecture will guide the
vehicle to find that path. The safe controller by
refining the nominal path to the destination pro-
vides sufficient time for the sensors to process the
information about the scanned region in front of
the vehicles for threats. If the sensors report any
presence of SAMs, then the entire mission is im-
mediately stopped and the interrupt is passed on
to the Dynamic path planner. The Dynamic path
planner then again probes the sensor information
processing unit for the latest risk map with the
new updates. The DPP uses this new risk map
to find a feasible nominal path and this process
repeats itself according to the strategy outlined
in section 5. Thus the completeness property of
the strategy is satisfied. Also, the presented ar-
chitecture has a property of information adequacy.
That is, the safe controller generates a path such
that the information that is gathered by flying
the refined safe path is atleast equal to the infor-
mation that would have been gathered by flying
the nominal path generated by DPP. The next
section discusses the Mission control interface that
we built for the architecture.
An implementation of the proposed architecture
has been developed on Mixed Initiative Control
for Automata-teams (MICA) Open Experimental
Platform (OEP) Simulator. We implemented also
a Mission Control interface to interact with the
system. The interface is shown in figure 9.
The user specifies a mission (currently only a
search area mission is implemented) and its pa-
rameters (e.g. the area to be searched, some
constraints on the sensor type to use, etc).Then
Fig. 7. Search mission
Fig. 8. Strategic search
he/she monitors the mission progress. The user
interface displays the location of the UAVs and
of the detected objects (in the current appli-
cations trucks, SAM and busses) in real-time.
When an object is detected but cannot be clas-
sified (because, for example, SAR sensors can-
not distinguish between truck and busses since
they belong to the same visually similar objects
group), the user can interactively dispatch a new
UAV equipped with EO sensors for classification.
Again, the results are displayed in real time on
the user interface.
An architecture with the algorithms and its imple-
mentation for controlling a team of vehicles for the
search mission was presented in the paper. Some
of the assumptions (which also are the drawbacks)
of the current work are as follows:
Sensors are perfect or the probability of de-
tecting a SAM launcher is 1 if sufficient
amount of time is spent.
A simple stick model is used for the kine-
matics of the UAV and only 2-D motion is
The sensor control onboard UAV’s are capa-
ble of scanning a specified part of an area
irrespective of the direction of the UAV.
The following are the future directions of this
current work
The resource allocation during cooperation
between the vehicles has not been solved.
Fig. 9. Mission Control user interface
Also, the question as to how to redistribute
the resources when the vehicles are shot has
not been answered
The information structure in the presented
architecture was centralized. In other words,
the information about the entire region or the
risk map is known to all the vehicles. So when
large number of vehicles are operating, the
aim now is to make this information structure
The authors thank Prof Pravin Varaiya, Univer-
sity of California, Berkeley and Prof Joao Sousa,
Universidade do Porto, Portugal for their useful
comments. The research was supported in part by
the Boeing Mica Program - grant #F33615-01-C-
3150 and by ONR AINS Program - grant #25833.
Assume x y. If x v
ωand if y pxv
ω, the yaw
rate constraint is always satisfied.
Proof: The speed of a UAV is a constant and is
always along the direction of the path. Let the
ellipse be represented in a parametric form by
(X(t),Y(t)), where X(t) = xcos t,Y(t) = ysin t
and t varies in the interval [0,2π]. Also let the
radius of curvature and the angular velocity at
any point be denoted by ρ(t) and ˙
Φ(t). Since the
velocity vector of the vehicle is always tangent
to the path of the vehicle and its magnitude is
a constant, the following claim is true:
t[0,2π]ρ(t) (10)
The radius of curvature is given by,
ρ(t) = (X02+Y02)3
X0Y00 Y0X00 (11)
where X’ = dx
dt and Y’ = dy
dt . Hence by substituting
the parametric forms for X and Y, we have,
ρ(t) = (x2sin t2+y2cos t2)3
xy (12)
It is trivial to see that the radius of curvature is
the same at all the points if x = y. ρ(t) is minimum
at t ={0, π}if x >y. The minimum value for x
y is equal is equal to y2
x. Hence for the yaw
rate condition to be satisfied, x v
ωand y2
Hence proved.
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Papers from the 1996 AAAI Spring Symposium,
AAAI Press, Menlo Park, California, pp. 10-116,
... Based on dynamic system graph representation, Tamas Keviczky transformed the cooperative control issue into a constrained optimization issue and put forward a coordination control framework for RHC (Receding Horizon Control), leading to a strict mathematical framework representation of the distributed control [10]. By dividing the UAV control issue into hierarchies with standard task components, S. Rathinam proposed a hybrid active control architecture for multi-UAV system, which realized the UAV cooperative consensus [11]. W. Ren extended cooperative consensus of the first-order dynamic system and introduced a multi-UAV cooperative consensus method for second-order dynamic systems, making it possible to achieve formation keeping only with message exchange between adjacent two UAVs [12]. ...
... Design and implementation of these functionalities, by themselves, constitute well-known research topics. Algorithms and design principles proposed by research communities in wireless ad hoc and sensor networks, robotics, and swarm intelligence provide valuable insights into one or more of these functionalities as well as combinations of them [9,10,11]. ...
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Small drones are being utilized in monitoring, transport, safety and disaster management, and other domains. Envisioning that drones form autonomous networks incorporated into the air traffic, we describe a high-level architecture for the design of a collaborative aerial system consisting of drones with on-board sensors and embedded processing, coordination, and networking capabilities. We implement a multi-drone system consisting of quadcopters and demonstrate its potential in disaster assistance, search and rescue, and aerial monitoring. Furthermore, we illustrate design challenges and present potential solutions based on the lessons learned so far.
Small drones are being utilized in monitoring, delivery of goods, public safety, and disaster management among other civil applications. Due to their sizes, capabilities, payload limitations, and limited flight time, it is not far-fetched to expect multiple networked and coordinated drones incorporated into the air traffic. In this paper, we describe a high-level architecture for the design of a collaborative aerial system that consists of drones with on-board sensors and embedded processing, sensing, coordination, and communication&networking capabilities. We present a multi-drone system consisting of quadrotors and demonstrate its potential in a disaster assistance scenario. Furthermore, we illustrate the challenges in the design of drone networks and present potential solutions based on the lessons we have learned so far.
Conference Paper
The interest of both the academics and industrials for the UASs (Uninhabited Aerial Systems) has been significantly growing for the last ten years. Technological developments in their design are opening the way for their exploitation in a wide range of applications. Nevertheless, the expected benefits are not yet fully exploited due to the still significant number of accidents. Several researches have demonstrated that the absence of vehicle decisional capacity is among the most important causes of such accidents. One of the proposed solutions is to increase the vehicle ability to perform autonomous mission planning. In this paper a planning algorithm for UASs mission is presented. Such algorithm has been designed following an heuristic approach in order to rapidly provide a robust solution to mission planning problem in evolving scenario. Original obstacle avoidance strategies have been conceived in order to generate mission plans which are consistent with flight rules and with the vehicle performance constraints. Simulation test results show that very efficient routes are computed in a few seconds. Copyright © 2008 by the American Institute of Aeronautics and Astronautics, Inc.
Conference Paper
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In this paper, a new approach for evaluating the performance of a coverage scenario in a border patrolling mission is presented. In these kinds of missions, continuous coverage of the entire surface over time, which maximizes the probability of detecting the intruders during crossing the border region, has great importance. Continuous and complete coverage of a border defines as “the maximum time distance between two successive visits from a specific point while it is less than a predefined threshold”. This threshold is defined due to the maximum speed of the intruders and period of flight scenario for all the points along the border during this period. The proposed approach by using a graphical virtual plane provides the opportunity of evaluation of the performance of the coverage scenarios and the probability of detecting passing intruders in a specific time period. This virtual plane represents spatial and temporal coverage of the border simultaneously, hence it is called spatiotemporal virtual plane. Relative movement of the UAV(s) and intruders, and assigning a metric to the probability of intruders detection are the main problems in the evaluation of the coverage scenario. The proposed virtual plane determines the effect of this relative movement on the performance of the coverage and illustrates this spatiotemporal concept in a graphical manner. By using this graphical representation, the capability of a scenario can be extracted and used in order to maximize the efficiency of the conceptual design phase. Also, the different coverage scenarios and paths can be compared via this tool. The verification of the proposed approach is done by comparing the probability of detection of the intruders which is computed by this approach and Monte-Carlo simulation.
A dynamic path-planning algorithm is proposed for routing unmanned air vehicles (UAVs) in order to track ground targets under path constraints, wind effects, and obstacle avoidance requirements. We first present the tangent vector field guidance (TVFG) and the Lyapunov vector field guidance (LVFG) algorithms. We demonstrate that the TVFG outperforms the LVFG as long as a tangent line is available between the UAV's turning circle and an objective circle, which is a desired orbit pattern over a target. Based on a hybrid version of the TVFG and LVFG, we then derive a theoretically shortest path algorithm with UAV operational constraints given a target position and the current UAV dynamic state. This algorithm has the efficiency of the TVFG when UAV is outside the standoff circle and the ability to follow the path via the LVFG when inside the standoff circle. In addition we adopt point-mass approximation of the target state probability density function (pdf) for target motion prediction by exploiting road network information and target dynamics as well as obstacle avoidance strategies. Overall, the proposed technical approach is practical and competitive, supported by solid theoretical analysis on several aspects of the algorithm performance. With extensive simulations we show that the tangent-plus-Lyapunov vector field guidance (T+LVFG) algorithm provides effective and robust tracking performance in various scenarios, including a target moving according to waypoints or a random kinematics model in an environment that may include obstacles and/or winds.
Purpose The purpose of this paper is to present a model developed for emergent formation of multi‐unmanned aerial vehicles (UAVs) into functional teams that cooperatively complete a mission in which they search for specific mobile targets and escape obstacles. Design/methodology/approach The design and development of distributed UAVs simulator use agent‐based platform employing a decentralized control which follows the flocking behavior‐based design philosophy. Findings The results of the simulation indicate that the emergent behavior‐based search procedure for UAVs is autonomous, effective and robust. It is especially well suited for emergent teams to quickly solve dynamic teaming and task allocation. Practical implications The development of a UAV is expensive, and a small error in automatic control results in a crash. Therefore, the platform is useful to develop and verify the coordination behavior of UAVs through software simulation prior to real testing. Originality/value The proposed emergent behavior simulated environment is working on an agent‐based UAV simulated platform, and hence, it naturally adapts to the behavior of a distributed and concurrent situation. The authors' can easily improvise the execution environment without changing the UAV simulator.
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We present a path planning algorithm for coop- erative surveillance using a set of heterogeneous unmanned vehicles. The paper describes our overall framework and algorithm for the computation of trajectories that maximize spatio-temporal coverage while satisfying hard constraints such as collision avoidance and specifications on initial and final positions. An Integer Programming (IP)-based strategy for successfully operating within these constraints is developed. IP is applied over a receding planning horizon with terminal cost to reduce the computational effort of the planner and to incorporate feedback. Simulation and results are presented to demonstrate the efficacy of the proposed approach.
Conference Paper
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In this paper, we present a strategy of path-planning for an unmanned aerial vehicle (UAV) to follow a ground vehicle. The ground vehicle may change its heading and vary its speed from a standstill up to the velocity of the UAV, while the UAV will maintain a fixed airspeed and will maneuver itself to track the ground vehicle. The algorithm also allows the UAV to track the ground vehicle with an offset vector (i.e. the user may wish the UAV to stay ahead of the ground vehicle or to its sides). Since the ground vehicle may operate in a range of velocities, the algorithm must plan the UAV's path with the appropriate schemes for the various ground vehicle speeds. The natural effect of wind injects a disturbance into the system, and so wind compensation techniques had to be developed. In order to maintain the focus of this project on path-planning strategies, the path-planning algorithm was implemented on top of a system that already controls the dynamics of the UAV. Simulation of aircraft and ground vehicles was performed with a hardware-in-the-loop simulation environment to test for mission feasibility. After attaining satisfactory simulation results, an experiment was conducted to confirm the path-planning strategy.
Here we describe the basic morphology of a typical fixed-wing UAV, setting out the main component categories that must be allowed for during design. We illustrate this with a number of photographs of the aircraft we have been directly involved with over the last 10 years.
There are currently several wide area search munitions in the research and development phase within the Department of Defense. While the work on the airframes, sensors, target recognition algorithms and navigation schemes is promising, there are insufficient analytical tools for evaluating the effectiveness of these concept munitions. Simulation can be used effectively for this purpose, but analytical results are necessary for validating the simulations and facilitating the design trades early in the development process. Recent research into cooperative behavior for autonomous munitions has further highlighted the importance of fundamental analysis to steer the direction of this new research venture. This paper presents extensions to some classic work in the area of search and detection. The unique aspect of the munition problem is that a search agent is lost whenever an attack is executed. This significantly impacts the overall effectiveness in a multi-target/false target environment. While the analytic development here will concentrate on the single munition case, extensions to the multi-munition will be discussed to include the potential benefit from cooperative classification and engagement.
In Chapter 1 we introduced configuration space as a space in which the robot maps to a point. The mathematical structure of this space, however, is not completely straightforward, and deserves some specific consideration. The purpose of this chapter and the next one is to provide the reader with a general understanding of this structure when the robot is a rigid object not constrained by any kinematic or dynamic constraint. This chapter mainly focuses on topological and differential properties of the configuration space. More detailed algebraic and geometric properties related to the mapping of the obstacles into configuration space will be investigated in Chapter 3.
Conference Paper
This paper presents results on the guidance and control of fleets of cooperating Unmanned Aerial Vehicles (UAVs). A key challenge for these systems is to develop an overall control system architecture that can perform optimal coordination of the fleet, evaluate the overall fleet performance in real-time, and quickly reconfigure to account for changes in the environment or the fleet. The optimal fleet coordination problem includes team composition and goal assignment, resource allocation, and trajectory optimization. These are complicated optimization problems for scenarios with many vehicles, obstacles, and targets. Furthermore, these problems are strongly coupled, and optimal coordination plans cannot be achieved if this coupling is ignored. This paper presents an approach to the combined resource allocation and trajectory optimization aspects of the fleet coordination problem which calculates and communicates the key information that couples the two. Also, this approach permits some steps to be distributed between parallel processing platforms for faster solution. This algorithm estimates the cost of various trajectory options using the distributed platforms and then solves a centralized assignment problem to minimize the mission completion time. The detailed trajectory planning for this assignment can then be distributed back to the platforms. During execution, the coordination and control system reacts to changes in the fleet or the environment. The overall approach is demonstrated on several example scenarios to show multi-task allocation and cooperative path planning.
Motion planning is one of the most important areas of robotics research. The complexity of the motion-planning problem has hindered the development of practical algorithms. This paper surveys the work on gross-motion planning, including motion planners for point robots, rigid robots, and manipulators in stationary, time-varying, constrained, and movable-object environments. The general issues in motion planning are explained. Recent approaches and their performances are briefly described, and possible future research directions are discussed.
Conference Paper
Military applications require unmanned aircraft vehicles (UAVs) to travel in an unknown, hostile environment. Hence minimizing the damage of these UAVs is crucial to any mission. The objective of this paper is to develop control algorithms that help in keeping the UAV 'safe'. Safety implies that the UAVs are not damaged or destroyed during the mission. We present two algorithms that guide the forward motion of these UAVs and illustrate how this safe flight algorithm can be coupled with other path planning algorithms.