UAV Overview document discusses unmanned aerial vehicles (UAVs) and approaches to automating UAV behavior. UAVs can perform tasks like remote sensing, surveillance and attacks without risking human pilots. Their behavior can be automated through direct control methods that evolve behaviors or rule-based systems that encode specific behaviors. Sensors provide UAVs information about their environment and other UAVs to influence their movement decisions. The best automation approach depends on whether predefined or emergent behaviors are needed for the task.

1. UAV Overview
Harry Sunarsa 137351

2. Introduction
• Unmanned Aerial Vehicles
(UAVs) is an aircraft, without
human pilot on board.
• Either controlled
autonomously by computers
in the vehicle, or under
the remote control of
a pilot on the ground or in
another vehicle
• constitute a specific case of
mobile sensors which have
been used, with success, in
different applications.
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3. • UAVs perform a wide variety of functions
– Remote Sensing
– Commercial aerial surveillance
– Armed attacks
– Search and rescue
– Etc..
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4. Advantages
Dull
• increase unit effectiveness and multiply force by surveying an
area better than ten or more human sentries and have longer
persistence
Dirty
• reconnoiter in areas contaminated by nuclear, chemical, or
biological agents without risk to humans
Dangerous
• suppress enemy air defenses in high risk operations,
eliminating the risk of loss of life of an air crew
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5. Disadvantages
Operating
Commnunic personel
ation
Bandwidth
Cost
Self-organized heterogenous UAV
swarm
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6. Automation Approach
• How needs of the UAV are distilled into
a single vector
• How each of these vectors are
combined into coherent behaviors
• How the coherent behaviors are then
selected
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7. Vectorizing UAV Need
Direct Control of Velocity
Maps what the UAV directly
knows about the other UAVs
and environment into
unprioritized vectors. [7]
Rule Based Directional
Control
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8. Direct Approach
• Uses some sort of decision making process
that directly determines the turn rate and
thrust.
• Range from an evolutionary programming
mechanism which directly encodes the
direction and velocity of each UAV to a
perceptron or neural network with the
outputs tied to velocity and steering.
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9. • When used in an
evolutionary sense, this
approach seeks to evolve
behaviour from scratch.
• Attempts to evolve a
controller used by the
UAVs defining its own
behaviour without any
explicit human guiding.
• Creating emergent
behaviour and not
constrained by
programmer
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10. • In a non-evolutionary sense, the actual behaviors
of UAVs are directly connected to static and
complex mathematical ideas about how UAVs
behave.
• The particular appeal of these systems is that
they explicitly perform what they are
programmed to do.
• However, with respect to SO systems, a manually
created system is very difficult to construct. [7]
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11. Rule Based
• A different way to map inputs to the next
heading and velocity relies upon the explicit
use of codified behavior rules.
• These rules describe behaviors based upon
certain anticipated system needs and projects
a velocity and heading for the individual.
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12. • Rather than directly
determining the next velocity,
the behavior matrix
determines the relative
importance of each behavior
rule.
• The weighted rules are then
combined to generate a
velocity.
• Seems to be far more useful
when the particular behaviors
that are needed are known
and can be encoded into the
system rather than elicited via
an evolutionary process.
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13. • In a sense, the rule based systems add
another layer of complexity and predefined
behavior
• The following equation demonstrates how a
series of weightings, w, derived from a
behavior mechanism could be applied to a set
of behavior rules, R, to derive a next velocity,
V.
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14. • Another consideration is the use of sets of
behavior weightings for particular situations
rather than using a behavior matrix to directly
determine rule weightings.
• This type of behavior weighted structure is
termed as behavior archetype [7].
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15. • With a large number of rules
and flexibility in the actual
weights associated with each
behavior rule, countless
behavior archetypes can be
created.
• The behavior matrix selects
which statically defined
behavior archetype is used.
• the final behaviors from a
behavior archetype system
are described simply as sets
of behaviors.
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16. • The use of behavior
archetypes has three major
effects upon the structure
which determines UAV next
behavior:
– it reduces potential
complexity,
– allows for incorporation of
difficult to represent data,
– and simplifies the
understanding of resultant
behavior.
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17. • However, it also has the disadvantage of
limiting the rule values which can be
expressed;
• The rules are not able to function in a dynamic
way and must operate as a limited number of
states
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18. UAV Senses
• UAVs utilize a series of sensor values distilled
from their local representation of the
environment as input to their movement logic.
• These senses contain information which is
useful in deciding when and in what value
each of the behavioral rules are applied.
Additionally, the senses facilitate cooperative
action.
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19. • For example, the senses aid the UAVs in
determining when to perform the major
behaviors.
• Additionally, the senses allow coordinated
attacks and searching behaviors. Likewise, the
senses allow the UAVs appropriate
information to determine their own behaviors
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20. Potential sensory values for each UAV include but are not limited to [7]:
• Density of other known UAVs
• Proximity to environment obstacles or boundaries
• Density of targets
• Whether enemy attacks are observed
• Density of different types of UAVs
• Behaviors selected last time by the same UAV
• Entity sensing using directionality and shadowing
• UAV damage
• Density of each behavior being used by known UAVs
• Coordinating signals between UAVs
• Pheromone-like signals
• when enemies are spotted
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21. Conclusion
• By better automating UAVs, these vehicles are able to
operate with reductions in their personnel requirements,
potentially inefficient use of costly and essential
components, and communications bandwidth.
• The exact nature of the behavior selected could be:
– a direct encoding to actuators which seeks to create emergent
behavior
– or more rule-based approaches which uses behavior archetype
by matching the current situation to a particular rule [2].
• In any event, these different design choices afford UAV
behavior models the ability to address different situations.
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22. References
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Graduate School of Engineering and Management, Air Force Institute of Technology (AU), Wright-Patterson AFB, OH, March
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Institute of Technology, Wright Patterson Air Force Base, Dayton, OH, March 2003.
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[12] Crowther, W.J. Flocking of autonomous unmanned air vehicles. Aeronautical Journal, Vol. 107(No. 1068):pp.
99{110, February 2003.
[13] Parunak, H. Van Dyke. Making Swarming Happen. Presented at Conference on Swarming and C4ISR, January 2003.
[14] Klausner, Kurt A. Command and Control of air and space forces requires significant attention to bandwidth.
http://www.airpower.maxwell.af.mil/airchronicles/apj/apj02/win02/klausner.html. Air Space Power Journal,
November 2002.
[15] Stern, Christopher. Satellite makers rake in war dollars. Washington Post, March 2003.
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University Press, 1999.
[17] Baldassarre, Gianluca, Stefano Nolfi, and Domenico Parisi. Evolving Mobile Robots Able to Display Collective
Behaviors. Technical report, Institute of Cognitive Science and Technologies, National Research Council(ISTC-CNR);
Viale Marx 15, 00137, Rome, Italy.
[18] Marocco, Davide and Stefano Nolfi. Emergence of Communication in embodiedagents: co-adapting communicative
and non-communicative behaviours. Technical report, Institute of Cognitive Science and Technologies, CNR, Viale
Marx 15, Rome, 00137, Italy.
[19] Floreano, Dario and Joseba Urzelai. Evolutionary Robots with On-line Self-Organization and Behavioral Fitness.
Neural Networks, 13:pp. 431-443, 2000.
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