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On swarm robotics. A beginner

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Title: On swarm robotics. A beginner


1
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

2
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

3
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.

4
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

5
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

6
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

7
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

8
Related fields
  • Multi-agent systems
  • Swarm intelligence
  • Robotics
  • Sensor networks
  • But new

9
Two
10
More two
11
And more
12
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

13
Swarm robotics algorithms
  • Dispersion in indoor environments
  • Distributed localization and mapping
  • Mobile formation
  • Cooperative hole avoidance
  • Don Miner, Swarm Robotics Algorithms (2007)

14
Dispersion in indoor environments
15
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)

16
Distributed localization and mapping
17
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.)

18
Mobile formation
19
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

20
Cooperative hole avoidance
21
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

22
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?

23
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

24
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

25
Finite state machine
26
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

27
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

28
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

29
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)

30
  • Thank for your attention

31
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

32
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

33
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
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