UAV Overview

Harry Sunarsa 137351

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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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• UAVs perform a wide variety of functions– Remote Sensing– Commercial aerial surveillance– Armed attacks– Search and rescue– Etc..

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Advantages

• increase unit effectiveness and multiply force by surveying an area better than ten or more human sentries and have longer persistence

Dull

• reconnoiter in areas contaminated by nuclear, chemical, or biological agents without risk to humans

Dirty

• suppress enemy air defenses in high risk operations, eliminating the risk of loss of life of an air crew

Dangerous

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Disadvantages

Self-organized heterogenous UAV swarm

Cost

Commnunication Bandwidth

Operating personel

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Automation Approach•Ho

w 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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Vectorizing UAV Need

Maps what the UAV directly knows about the other UAVs

and environment into unprioritized vectors. [7]

Direct Control of Velocity

Rule Based Directional Control

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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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• 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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• 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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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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• 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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• 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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• 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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• 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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• 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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• 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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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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• 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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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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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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References

[1] Culler, D., E. Deborah and M. Srivastava (2004). Overview of Sensor Networks. IEEE Computer, Special Issue in Sensor Networks: 41-49.

[2] Prieditis, Armand, Mukesh Dalal, Andrew Arcilla, Brett Groel, Michael Van Der Bock, and Richard Kong. “SmartSwarms: Distributed UAVs that Think”. Command and Control Research and Technology Symposium, San Diego, CA, June 2004.

[3] Milam, Kevin. Evolution of Control Programs for a Swarm of Autonomous Unmanned Aerial Vehicles. Master's thesis, Air Force Inst. of Tech., WPAFB, OH, March 2004.

[4] Wu, Annie S., Alan C. Schultz, and Arvin Agah. Evolving Control for Distributed Micro Air Vehicles. IEEE Conference on Computational Intelligence in Robotics and Automation, Vol. 48:pp. 174{179, 1999.

[5] Zaera, N., D. Cliff, and J Bruten. (Not)Evolving Collective Behaviors in Synthetic Fish. Fourth International Conference on Simulation of Adaptive Behavior, 1996.

[6]. Parker, Gary, Matt Parker, and Steven Johnson. Evolving Autonomous Agent Control in the Xpilot Environment. The 2005 IEEE Congress on Evolutionary Computation, September 2005.

[7]. I. C. Price, Evolving self organizing behavior for homogeneous and heterogeneous swarms of uavs and ucavs, Master's thesis, Graduate School of Engineering and Management, Air Force Institute of Technology (AU), Wright-Patterson AFB, OH, March 2006.

[8] Lotspeich, James T. Distributed Control of a Swarm of Autonomous Unmanned Aerial Vehicles. Master's thesis, Air Force Institute of Technology, Wright Patterson Air Force Base, Dayton, OH, March 2003.

[9] Kadrovich, Tony. A Communications Modeling System for Swarm-based Sensors. Ph.D. thesis, Air Force Inst. of Tech., WPAFB, OH, March 2003.

[10] Lua, Chin A., Karl ALtenburg, and Kendall E. Nygard. "Synchronized MultiPoint Attack by Autonomous Reactive Vehicles with Local Communication". Proceedings of the 2003 IEEE Swarm Intelligence Symposium.

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[11] Reynolds, Craig W. Flocks, Herds, and Schools: A Distributed BehavioralModel. Maureen C. Stone (editor), Computer Graphics 4, volume 4, 25-34. SIGGRAPH, July 1987.[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.[16] Bonabeau, Eric, Marco Dorigo, and Guy Theraulaz. Swarm Intelligence From Natural to Artificial Systems. Oxford

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.

[20] Schlecht, Joseph, Karl Altenburg, Benzir Md Ahmed, and Kendall E. Nygard.Decentralized Search by Unmanned Air Vehicles using Local Communication. Mun Y. Joshua R (editor), Proceedings of the

International Conference on Artificial Intelligence, volume 2. Las Vegas, NV, 2003.