Aerial Swarm Robotics Introduction and Exploration Parth Sarthi - - PowerPoint PPT Presentation

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Aerial Swarm Robotics Introduction and Exploration Parth Sarthi - - PowerPoint PPT Presentation

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MIN-Fakultt Fachbereich Informatik Aerial Swarm Robotics Introduction and Exploration Parth Sarthi Pandey Universitt Hamburg Fakultt fr Mathematik, Informatik und Naturwissenschaften Fachbereich Informatik Technische Aspekte

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MIN-Fakultät Fachbereich Informatik

Aerial Swarm Robotics

Introduction and Exploration Parth Sarthi Pandey

Universität Hamburg Fakultät für Mathematik, Informatik und Naturwissenschaften Fachbereich Informatik Technische Aspekte MultimodalerSysteme

  • 11. December 2017

P.S. Pandey – Exploring Aerial Swarms

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Outline

  • 1. Motivation

From fellow Earthlings

  • 2. What are swarms?

Definition & Properties

  • 3. Surveillance by Micro-Aerial Vehicles

Motion Planning Particle Swarm Optimization

  • 4. Energy Aware PSO

Algorithm

  • 5. Aerial Swarms as Asymmetric Threats
  • 6. Conclusion

P.S. Pandey – Exploring Aerial Swarms 2/ 27

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Motivation

Sapiens:

The reason why Homo Sapiens are the only human species alive today and ruling the planet is their capacity to coordinate in large numbers and work together for a commongoal.

P.S. Pandey – Exploring Aerial Swarms 3/ 27

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Motivation

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[www.fuelspace.org]

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Motivation

P.S. Pandey – Exploring Aerial Swarms 5/ 27

[iopscience.iop.org]

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Motivation

P.S. Pandey – Exploring Aerial Swarms 6/ 27

[www.treehugger.com]

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Motivation

P.S. Pandey – Exploring Aerial Swarms 7/ 27

Wasp Nest

[agriculture.purdue.edu]

Beehive

[en.wikipedia.org]

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How do we define a swarm?

P.S. Pandey – Exploring Aerial Swarms 8/ 27

  • Not just a random group of agents.
  • Properties:
  • No centralized controlling
  • Only local sensing and

communication

  • Large number of agents
  • Single agent relatively

incapable

[spectrum.ieee.org/automaton/robo tics/robotics-hardware/a-thousand- kilobots-self-assemble]

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Collective Behavior

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Collective behavior is classified into 4 categories:

  • Coordination: appropriate organization in space and time
  • Cooperation: individuals achieve tasks together which could not

be done by a single one alone

  • Deliberation: colony faces several options and collectively choses
  • ne of them
  • Collaboration: different activities simultaneously performed by

groups of specialized individuals

Garnier, Simon, Jacques Gautrais, and Guy Theraulaz. "The biological principles of swarm intelligence." Swarm Intelligence 1.1 (2007): 3-31.

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Aerial Swarms

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  • Multiple unmanned aerial

vehicles.

  • Coordinating the space and time
  • Working together for a single

task

  • Collectively taking real time

decisions

  • Collaborating with one another

A common COTS quadcopter widely used in research and by hobbyists. [Wilkerson et. al. 2016]

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Surveillance by Micro-Aerial Vehicles

Applications of Aerial Swarms

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Initial position of MAV. Obstacles – Green lines. No fly zones – red

  • lines. Areas of interest – Blue regions.

[Saska et. al. 2016]

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Surveillance by Micro-Aerial Vehicles

P.S. Pandey – Exploring Aerial Swarms 12/27

[Saska et. al. 2016]

MAV Group deployed. Camera range – white pyramids. Trajectories – Yellow curves. Localization linkages – Red lines.

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Swarm Motion Planning

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  • Two main challenges:
  • How to find a suitable swam distribution that covers all

AoIs.

  • How to find feasible trajectories of MAVs – described

by Swarm Distribution.

  • Cost Function:
  • Describes the quality of coverage of AoIs achieved by a

swarm distribution X.

  • Is a vector of 3D positions of ‘n’ MAVs

[Saska et. al. 2016]

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Construction of a Map

P.S. Pandey – Exploring Aerial Swarms 14/27

[Saska et. al. 2016]

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Construction of a Map

P.S. Pandey – Exploring Aerial Swarms 15/27

[Saska et. al. 2016]

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Optimizing Swarm Distribution

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  • Challenges:
  • Each MAV represented by its 3D position.
  • ‘n’ MAVs belong to a swarm.
  • Cost Function may have several local minima.
  • High dimensional optimization problems
  • Plenty of local extremes.
  • Particle Swarm Optimization

[Saska et. al. 2016]

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Particle Swarm Optimization

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  • Eg. Swarm of birds searching for food.
  • Swarm as a vector of P particles.
  • Each particle keeps track of its “best” position
  • “pbest” for individual particle.
  • “gbest” for best in population.
  • “lbest” for best in defined neighborhood.
  • At each time step, each particle stochastically accelerates

toward its pbest and gbest or lbest.

[Kennedy & Eberhart 2001]

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Particle Swarm Optimization Process

P.S. Pandey – Exploring Aerial Swarms 18/27

  • 1. Initialize population in hyperspace.
  • 2. Evaluate fitness of individual particles.
  • 3. Modify velocities based on previous best and

global (or neighborhood) best.

  • 4. Terminate on some condition.
  • 5. Go to step 2.

[Kennedy & Eberhart 2001]

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Energy Aware Particle Swarm Optimization

Towards Efficiency

P.S. Pandey – Exploring Aerial Swarms 19/27

EAPSO – considers the trade-off between profit and energy

consumption.

[Mostaghim et. al. 2016] Mostaghim, Sanaz, Christoph Steup, and Fabian Witt. "Energy Aware Particle Swarm Optimization as search mechanism for aerial micro-robots." Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016.

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Building Blocks of EAPSO

P.S. Pandey – Exploring Aerial Swarms 20/27

  • DecideState – Takeoff or

Stay Grounded

  • LeaderSelection –

Choses Best Individual (PSO or Local Search)

  • ComputeVelocity,

UpdatePosition, ComputeEnergy

[Mostaghim et. al. 2016]

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It's not just Rainbows and Butterflies

Aerial Swarms as Asymmetric Threats

P.S. Pandey – Exploring Aerial Swarms 21/27

Estimated sales of popular drones (i.e. quadcopters) Intel Drone 100 record breaking aerial swarm. [Wilkerson et. al. 2016] Wilkerson, Stephen, et al. "Aerial swarms as asymmetric threats." Unmanned Aircraft Systems (ICUAS), 2016 International Conference on. IEEE, 2016.

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Aerial Swarms as Asymmetric Threats

P.S. Pandey – Exploring Aerial Swarms 22/27

  • Cheaper and more powerful drones readily available.
  • Intel set the Guinness World Record for most simultaneously

airborne UAVs.

  • US Navy program LOCUST (Low-cost UAV Swarming

Technology)

  • Plans to include armed and unarmed UAVs
  • Aerial Swarm Attack a much bigger threat than

acknowledged for.

[Wilkerson et. al. 2016]

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Conclusion

P.S. Pandey – Exploring Aerial Swarms 23/27

  • Motivation from nature – Awesome
  • Application in UAV Surveillance – Awesomer
  • Energy Efficient Enhancements to the Tech – Awesomerer
  • But…
  • Threats are Real – Aerial Swarm Attack (Boomm!!^n)
  • Needed..
  • Investments in countermeasures.
  • Containing the virus before it contaminates.
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References

[Garnier et. al. 2007] Garnier, Simon, Jacques Gautrais, and Guy

  • Theraulaz. "The biological principles of swarm intelligence." Swarm

Intelligence 1.1 (2007): 3-31. [Saska et. al. 2016] Saska, Martin, et al. "Swarm distribution and deployment for cooperative surveillance by micro-aerial vehicles." Journal of Intelligent & Robotic Systems 84.1-4 (2016): 469-492. [Mostaghim et. al. 2016] Mostaghim, Sanaz, Christoph Steup, and Fabian Witt. "Energy Aware Particle Swarm Optimization as search mechanism for aerial micro-robots." Computational Intelligence (SSCI), 2016 IEEE Symposium Series on. IEEE, 2016. [Wilkerson et. al. 2016] Wilkerson, Stephen, et al. "Aerial swarms as asymmetric threats." Unmanned Aircraft Systems (ICUAS), 2016 International Conference on. IEEE, 2016.

P.S. Pandey – Exploring Aerial Swarms 26/27

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References (contd..)

uhhBeamer

[Kennedy & Eberhart 2001] Eberhart, Russell C., Yuhui Shi, and James Kennedy. Swarm intelligence. Elsevier, 2001. [i] www.fuelspace.org [ii] iopscience.iop.org [iii] www.treehugger.com [iv] agriculture.purdue.edu [v] en.wikipedia.org [vi] https://spectrum.ieee.org/automaton/robotics/robotics- hardware/a-thousand-kilobots-self-assemble [vii] dailymail.co.uk

P.S. Pandey – Exploring Aerial Swarms 27/27