On swarm robotics. A beginner

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On swarm robotics. A beginners view

• Luboš Popelínský
• Knowledge Discovery Lab
• Faculty of Informatics, Masaryk University Brno
  
• popel_at_fi.muni.cz, http//www.fi.muni.cz/kd

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Overview

• 1. Introduction to swarm intelligence
• 2. Swarm robots Perception and communication
• Swarm robotics control algorithms
• 3. Temporal logic formal specification of
  emergent behaviours
• in swarm robotics systems.
• Temporal and spatiotemporal refinement
  operator
• Appendix 1 Learning when to auction and when to
  bid

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Swarm intelligence

• based on the collective behavior of
  decentralized, self-organized systems
• population of simple agents interacting locally
  with one another and with their environment
• follow very simple rules
• It leads to the emergence of complex global
  behavior
• Bee hive, ant colonies, bird flocking, animal
  herding, bacterial growth, and fish schooling
• Bonabeau E. Thrauluz G. Dorigo M. Swarm
  Intelligence Oxford University Press 1999
• Introduced by Gerardo Beni and Jing Wang in 1989,
  in the context of cellular robotic systems.

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Bees

• Beehive metaphor
• Foraging, randomly at the begining
• or
• Learning in the hive dancing floor and auditory
• Web search
• Schultze, S.J. A Collaborative Foraging Approach
  to Web Browsing Enrichment. InProc. CHI 2002,
  ACM, 2002, 860-861.
• Lorenzi F. Sherer dos Santos D. Bazzan A.L.C.
  Negotiation for task allocation among agents in
  case-based recommender systems. In IJCAI-05 Ws on
  Multiagent IR and Recommender Systems

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Ant colony and source allocation

• Without a control center
• Without direct communication between ants
• An ant is building a path
• If succeeds to find a source,
• follows the same path back and
• sign it with a pheromone.
• The shorter path, the higher level of pheromone
• positive feedback
• Consequence more and more ants follow the most
  promising
• pathes

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Ants colony and source allocation

• How it correspond to classification?
• source learning examples from the same class
• path
• between nodes ltattributevaluegt
• result classification rule
• A1v1 A2v2 Anvn gt class

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Swarm robotics

• multirobot system which consist of large numbers
  of simple physical robots
• A key-component communication between the
  members of the group that build a system of
  constant feedback
• local communication - wireless, e.g. bluetooth or
  infrared

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Related fields

• Multi-agent systems
• Swarm intelligence
• Robotics
• Sensor networks
• But new

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Two
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More two
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And more
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Why do we need microrobots?

• can provide accurate handling of micro and nano
  parts
• exempt humans from tedious and very lengthy tasks
• can be used in hazardous environments
• can be cheaper to build than equipment currently
  used
• provide flexible and programmable systems for
  microassembly
• 'encourage' the development of novel manipulating
  tools

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Swarm robotics algorithms

• Dispersion in indoor environments
• Distributed localization and mapping
• Mobile formation
• Cooperative hole avoidance
• Don Miner, Swarm Robotics Algorithms (2007)

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Dispersion in indoor environments
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Dispersion in indoor environments

• Uniform dispersion
• Wall nodes, frontier nodes (both do not move),
  interior nodes
• Disperse robots uniformly
• Generate vectors away from N particular
  neighbors
• Explore boundaries
• Frontier node send a message so that each node
  knows a number of hops from a frontier
• Then an interior node moves towards the lowest
  numbered neighbor (fastest path to the frontier)

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Distributed localization and mapping
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Distributed localization and mapping

• Main idea
• robot-beacons - are broadcasting position
  information
• Move in general direction
• IF num. of beacons goes below a threshold
• THEN become beacon
• IF num. of dependent nodes goes below a threshold
• THEN stop being a beacon, return to (1.)

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Mobile formation
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Mobile formation

• Moving a large number of robots while maintaining
  connectivity
• Model
• newtonian physics Forcemassacceleration and
  Lennard-Jones (LJ) forces
• (modelling crystalline formation, liquids,
  gases)
• Results
• with LJ performed much better

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Cooperative hole avoidance
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Cooperative hole avoidance

• Clearance sensors - to detect holes
• Traction sensors - to detect movements of other
  S-bots
• Evolutionary algorithm used
• Drawbacks
• evolutionary algorithms are very slow
• -learing is done in simulation, not work in real
  environment
• robots would fall in holes

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Summary

• Local communication
• A robot usually represented by a finite state
  automaton
• Easy to represent in first-order logic
• A robot a context (neighbors)
• Probabilitic automata, e.g. Markov chains ?
• Temporal, spatiotemporal logic?

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Temporal logic and swarm robotics

• Temporal logic for formal specification (and
  proving) emergent behaviour of a robotic swarm
• Allan Winfield,, Michael Fisher. On Formal
  Specification of Emergent Behaviours in Swarm
  Robotic Systems, Intl. Journal on Advanced
  Robotic Systems Vol 2., p. 363-371

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Basic algorithm

• Range-limited wireless communication
• rw - radius for communication
• ra - collision avoidance radius
• ? - number of neighbors threshold
• Default forward moving, transmitting I am
  here
• If num.of neighbors lt ? (moving out of the
  swarm)
• Then turn 180?
• If num.of neighbors gt ? (regained)
• Then execute a random turn

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Finite state machine
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A linear time temporal logic

• Discrete time, linear ordering
• s0, s1, s2, s3, s4,
• Finite past, infinite future
• Modalities
• NEXT ltformulagt
• SOMETIMES ltformulagt
• ALLWAYS ltformulagt

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Simplified algorithm

• Robots move in a grid world
• to N(orth), E(ast), S(outh), W(est)
• Can turn before making a move
• 90? right, 90? left, 180? back
• Robots always move ? units
• Avoidance state is omitted
• ? 1

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State transition

• Forward state, connected -gt move forward
• Forward state, not connected -gt
• turn 180?,
• change state to Coherent
• Coherent state, not connected -gt move forward
• Coherent state, connected -gt
• perform random turn (90? right, 90? left),
• change state to Forward

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Temporal logic and beyond

• Specification expressed in First-order temporal
  logic (FOTL)
• mapping to monodic FOTL (max. 1 free variable)
• TeMP - resolution-based theorem prover for FOTL
• Refinement operator for temporal logic exists
• even for spatiotemporal logic
• (Blaták, Popelínský, ECML04 WS)

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• Thank for your attention

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Learning when to auction and when to bid

• Market based approach
• frequently used for multi-robot coordination
• taskgood, robots bid in auction for these goods
• Communication cost
• number of messages needed for running the
  auctions
• Computational cost
• cost of running the auctions
• Here learning to reduce communication and
  computation cost
• Learning the probability of whether a given bit
  may win an auction
• D. Busquets, R. Simmons (CMU) DARS06

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Learning when to auction and when to bid II

• Usually bidders respond to all the tasks being
  announced
• Here
• Compute Prob, probability of a bid being awarded
  in an auction
• Generate R, a random number.
• IF IR lt Prob THEN bid
• Similar for the tasks auctioned
• Off-line learning

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Experiments

• To characterize a set of rocks at different
  locations
• 3 settings
• NP (no probability), AuP (auction), AllP
  (auction and bid)
• AuP Num. of rocks same, much better performance
• auctions 1394 -gt 350
• AllP Num. of rocks slightly smaller, much lower
  cost
• messages 13606(8608) -gt 3814
• Challenge on-line learning