Communication of Swarm Robots Lennart Manthey December 5, 2016 - - PowerPoint PPT Presentation

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Communication of Swarm Robots Lennart Manthey December 5, 2016 - - PowerPoint PPT Presentation

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Communication of Swarm Robots Lennart Manthey December 5, 2016 Lennart Manthey Swarm Robots December 5, 2016 1 / 27 Contents Swarm 1 GUARDIANS 2 Designing a Swarming Algorithm 3 Additional Behaviour 4 Conclusion 5 Lennart Manthey

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SLIDE 1

Communication of Swarm Robots

Lennart Manthey December 5, 2016

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SLIDE 2

Contents

1

Swarm

2

GUARDIANS

3

Designing a Swarming Algorithm

4

Additional Behaviour

5

Conclusion

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SLIDE 3

Swarm

Swarms

large numbers, scalability, fault tolerance coordination of large multirobot systems inspired by nature

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SLIDE 4

Swarm

(D. Dibenski, 1986)

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SLIDE 5

Swarm

(Mehmet Karatay, n.d.)

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SLIDE 6

Swarm

(Tero et al., 2010)

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SLIDE 7

GUARDIANS

GUARDIANS: swarm robots to aid firefighters

(Witkowski et al., 2008) (Penders et al., 2007) Scenario: fire in an industrial warehouse complex architecture metal and concrete walls toxic materials

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SLIDE 8

GUARDIANS

The Swarm’s Task

aid the firefighters maintain a communication link detect humans locate danger: fire, smoke, toxic material

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SLIDE 9

GUARDIANS

The Swarm’s Task

dynamic adhoc communication network positioning and mapping system toxic alarm system

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SLIDE 10

GUARDIANS

Equipment

WLAN, Bluetooth, ZigBee special sensors: temperature, gas SLAM mobility light, sound for communication with humans

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SLIDE 11

Designing a Swarming Algorithm

Designing a Swarming Algorithm

Situation: burning warehouse, lots of robots Task: search for hazards Problem: efficiently search and disperse to cover a large area

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Designing a Swarming Algorithm

Designing a Swarming Algorithm

centralised leader based behaviour based virtual structure learning based spring based models social potential fields (Reif and Wang, 1999)

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Designing a Swarming Algorithm

Social Potential Field

every robot has a goal − → EA

  • bstacles to avoid

−

→ ER wants to stay in contact with the other robots

−

→ IA but also doesn’t want to come to close

−

→ IR F = − → EA +

  • bstacles

− → ER +

  • ther robots

(− → IA + − → IR)

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SLIDE 14

Designing a Swarming Algorithm

Social Potential Field

Fi,j = f (xj − xi) xj − xi xj − xi choose for example f (r) = − c1 r σ1 + c2 r σ2 with c1, c2 ≥ 0 ∧ σ1 > σ2 > 0

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Designing a Swarming Algorithm

A Typical Potential Function

(Reif and Wang, 1999)

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Designing a Swarming Algorithm

Example

(Calerga Sarl, n.d.)

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SLIDE 17

Designing a Swarming Algorithm

Example

(Damer et al., 2006)

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Designing a Swarming Algorithm

Advantages

high level distributed still works with limited communication grouping possible analytical solutions

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Additional Behaviour

Additional Behaviour

Problem: swarm found something, how does it communicate it to the base station? build a communication network static beacon robots communication and localisation swarming algorithm to dynamically build the network topology

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Additional Behaviour

Dynamic Triangulation Method

(Witkowski et al., 2008)

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Additional Behaviour

Dynamic Triangulation Method

(Witkowski et al., 2008)

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Additional Behaviour

Communicative Swarming

the global communication enables communicative swarming i.e. each robot can use global information to calculate its social potential field this also enables more effective division of labour grouping the robots with different social potential functions

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Additional Behaviour

Human-Robot Interaction

the swarm provides a danger map at the base station the firefighter in the warehouse needs to command the swarm for example by applying a new potential, i.e. a new goal, a safe route robots needs to show the human potential hazards, paths . . .

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Additional Behaviour

UI

(Christof et al., 2010)

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Conclusion

Conclusion

a swarm can achieve more than a single actor distributed swarm algorithms are needed to control it as micromanaging is not feasible this is really complex but the control models can help by simplifying the problem interaction with and control by humans must also be considered

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Conclusion

Citations I

Calerga Sarl. Mobile Robot Navigation with Sysquake. url: https://www.calerga.com/products/Sysquake/robotnav.html. Christof, Breckenfelder et al. (2010). “A Cognitive Glove Sensor Network for Fire Fighters”. In: Ambient Intelligence and Smart Environments,

  • pp. 158–169. issn: 1875-4163. doi: 10.3233/978-1-60750-639-3-158.
  • D. Dibenski (1986). Auklet flock Shumagins 1986. url:

https://commons.wikimedia.org/wiki/File: Auklet_flock_Shumagins_1986.jpg. Damer, Steven et al. (2006). “Dispersion and exploration algorithms for robots in unknown environments”. In: ed. by Grant R. Gerhart, Charles M. Shoemaker, and Douglas W. Gage, 62300Q. doi: 10.1117/12.668915. Mehmet Karatay. Safari ants. url: https://commons.wikimedia.org/wiki/File:Safari_ants.jpg.

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Conclusion

Citations II

Penders, Jacques et al. (2007). “Guardians: a swarm of autonomous robots for emergencies”. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI’07) Workshop on Multirobotic Systems for Societal Applications, pp. 8–16. Reif, John H. and Hongyan Wang (1999). “Social potential fields: A distributed behavioral control for autonomous robots”. In: Robotics and Autonomous Systems 27.3, pp. 171–194. issn: 09218890. doi: 10.1016/S0921-8890(99)00004-4. Tero, A. et al. (2010). “Rules for Biologically Inspired Adaptive Network Design”. In: Science 327.5964, pp. 439–442. issn: 0036-8075, 1095-9203. doi: 10.1126/science.1177894. Witkowski, Ulf et al. (2008). “Ad-hoc network communication infrastructure for multi-robot systems in disaster scenarios”. In: Proceedings of the International Workshop on Robotics for Risky Interventions and Surveillance

  • f the Environment.

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