A Platform for Local Interactions between Robots in Large Formations

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A Platform for Local Interactions between Robots
in Large Formations

• Ross Mead
• Jerry B. Weinberg
• Jeffrey R. Croxell

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Motivation

• Space Solar Power (SSP)
• large solar reflector panel in space
• diameter 16.5 km (10mi)
• focus concentrated beam of solar energy
• diverted to an energy plant on Earth for
  harvesting

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Motivation

• One solution that received considerable attention
  was the use of robots to form a solar reflector.
• Imagine the space shuttle releasing thousands of
  robots, each with a piece of reflector attached
  to them.
• These robots then navigate themselves to form a
  large parabolic structure resembling a reflector,
  which is then used to harvest solar energy.

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Motivation ? Problem

• How can a massive collection of robots
• 33,000 required for SSP (Landis 2004)
• moving with no group organization
• swarm
• coordinate to form a global structure?
• formation

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Problem
swarm
formation
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Background

• Fredslund Mataric 2002
• Balch Arkin 1998
• Reynolds 1987
• Farritor Goddard 2004

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Background Goals

• In related work on formations, units know
• where they belong in the formation
• who their neighbors are supposed to be
• Goals
• generality conforming to a variety of
  formations
• stability maintaining the formation
• robustness responding to changes in group size
• dynamic switching capability responding to
  commands for changes in its organization

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Background

• This approach to the autonomous control of
  creating and maintaining multi-robot formations
  is similar to work done in coordinating
  formations of Earth-bound, mobile robots.
• Fredslund Mataric 2002
• Balch Arkin 1998
• This work has been inspired by biological or
  organizational systems, such as geese flying in
  formation.

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Background

• A variety of work has also been done to apply
  reactive control structures to create emergent
  group behaviors.
• Flocking algorithms have been used for both
  physical and simulated robots.
• Ando, et al 1995
• A digital hormone model, inspired by biological
  cell interaction, has also been proposed for
  robotic organization
• Shen, et al 2004

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Background

• Robot formations have been applied to
  applications such as automated traffic cones.
• Farritor Goddard 2004
• Swarm behavior control has been applied to urban
  search-and-rescue robotics.
• Tejada, et al 2003

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Formation Control

• Utilize reactive robot control strategies
• closely couple sensor input to actions
• Treat the formation as a cellular automaton
• lattice of computational units (cells)
• each cell is in one of a given set of states
• governed by a set of rules

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Formation Control

• A command that indicates the geometric formation
  is sent to a seed robot
• The formation then transforms as robots
• react to changes in their neighbors
• attain their calculated relationships
• based on the formation definition

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Formation Control
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Formation Control

• A desired formation, F, is defined as a geometric
  description
• i.e., mathematical function
• F ? y ax2, where a is some constant

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Formation Control

• A robot is chosen as the seed, or starting point,
  of the formation.

F ? y ax2
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Formation Control

• The desired location on the formation is
  determined by calculating a relationship vector
  from c,
• where c is the formation-relative position (xi,
  yi) of the robot,
• and the intersection of the function F and a
  circle centered at c with radius r, where r is
  the distance to maintain between neighbors in the
  formation.

F ? y ax2
c ? (xi, yi) r2 ? (x-cx)2 (y-cy)2
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Formation Control

• Relationships and states are communicated locally
  to robots in the seeds neighborhood, which
  propagates changes in each robots neighborhood
  in succession.
• Using sensor readings, robots attempt to acquire
  and maintain the calculated relationship with
  their neighbors.

F ? y ax2
c ? (xi, yi) r2 ? (x-cx)2 (y-cy)2
r
r
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Formation Control

• Despite only local communication, the calculated
  relationships between neighbors results in the
  overall organization of the desired global
  structure.

F ? y ax2
c ? (xi, yi) r2 ? (x-cx)2 (y-cy)2
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Formation Control

• Thus, it follows that a movement command sent to
  a single robot would cause a chain reaction in
  neighboring robots, which then change states
  accordingly, resulting in a global transformation.

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Formation Control
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Formation Control

• Likewise, to change a formation, a seed robot is
  simply given the new geometric description, and
  the process is repeated.

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Results

• A proof-of-concept of the formation control
  algorithm was successfully demonstrated in a
  simulated environment at AAAI-06.
• We have developed a robot platform to assess the
  algorithm in the physical world.

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Robot Platform

• Each robot features
• a Scooterbot II base
• differential steering system
• an XBC v2 microcontroller
• executes formation control algorithm
• a color-coding system and color camera
• visual identification and tracking of neighbors
• an XBee radio communication module
• sharing information within a robots neighborhood

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Robot Platform

• Scooterbot II base
• precision cut double-decker base
• rigid expanded PVC
• strong, but very light
• 2" risers for additional decks
• differential steering system
• http//www.budgetrobotics.com/

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Robot Platform

• Differential steering
• two modified R/C servo motors with 2 1/2"
  diameter rubber wheels
• if motors are operated at same speed, the robot
  goes straight
• if motors are operated at different speeds, the
  robot turns or spins

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Robot Platform

• XBC v2 microcontroller
• executes formation algorithm
• back-EMF PID motor control
• fast charging
• 1 hour to fully charge
• http//www.botball.org/

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Robot Platform

• Color-coding system
• visual identification and tracking of neighbors
• Color camera
• multi-color, multi-blob simultaneous color
  tracking

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Robot Platform

• Color camera
• multi-color, multi-blob simultaneous color
  tracking
• Color-coding system
• visual identification and tracking of neighbors

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Robot Platform

• XBee radio communication module
• sharing state information within a robots
  neighborhood
• ZigBee/IEEE 802.15.4 specification
• up to 65,535 nodes on a network
• support for multiple network topologies
• low duty cycle ? long battery life
• collision avoidance
• retries and acknowledgements
• link quality indication
• 128-bit AES encryption
• http//www.maxstream.net/

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Robot Platform

• XBee radio communication module
• share information within a robots neighborhood
• ZigBee/IEEE 802.15.4 specification
• 300 (100m) line-of-sight range
• peer-to-peer, point-to-point, point-to-multipoint
  and mesh network topologies
• retries acknowledgements for error handling
• 65,535 network addresses for each channel
• http//www.maxstream.net/

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Robot Platform

• Simple, light, and inexpensive
• reproduction of each unit is easy and affordable
• A successful implementation of the algorithm on a
  modest number of physical robots will prove that
  the approach is viable in the real world.

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Future Work Formation Management

• Develop a graphical user interface to provide a
  human operator with
• a visualization of the formation
• information on each individual robot unit

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Future Work Dynamic Neighborhoods

• Implement an auction-based method to determine
  neighborhoods dynamically
• a robot is chosen to be a neighbor based on its
  distance to the desired location in the formation

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Future Work Formation Classification

• Classify different types of formations
• those defined by multiple functions
• those that generate erroneous neighbors

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Future Work Formation Classification
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Future Work Formation Classification
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Future Work Formation Classification
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References

• Balch, T. Arkin R. 1998. Behavior-based
  Formation Control for Multi-robot Teams IEEE
  Transactions on Robotics and Automation, 14(6),
  pp. 926-939.
• Bekey G., Bekey, I., Criswell D., Friedman G.,
  Greenwood D., Miller D., Will P. 2000. Final
  Report of the NSF-NASA Workshop on Autonomous
  Construction and Manufacturing for Space
  Electrical Power Systems, 4-7 April, Arlington,
  Virginia.
• Farritor, S.M., Goddard, S. 2004. Intelligent
  Highway Safety Markers, IEEE Intelligent
  Systems, 19(6), pp. 8-11.

• Fredslund J., Mataric, M.J. 2002. Robots in
  Formation Using Local Information, The 7th
  International Conference on Intelligent
  Autonomous Systems, Marina del Rey, California.
• Reynolds, C.W. 1987. Flocks, Herds, and Schools
  A Distributed Behavioral Model, in Computer
  Graphics, 21(4) SIGGRAPH 87 Conference
  Proceedings, pages 25-34.
• Tejada S., Cristina A., Goodwyne P., Normand E.,
  OHara R., Tarapore, S. 2003. Virtual Synergy
  A Human-Robot Interface for Urban Search and
  Rescue. In the Proceedings of the AAAI 2003
  Robot Competition, Acapulco, Mexico.

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Questions?

• For more information,
• visit the exhibition or
• http//roboti.cs.siue.edu/projects/formations/