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Since each taskable machine has limited scope, achieving
flexibility and sophisticated capability requires groups,
or colonies, of taskable machines [2,11].
In other words, future task-solving robotic systems will be made
up of colonies of taskable machines that collectively achieve a
wide range of tasks. Other motivations for robot colonies include:
Building and using several simple taskable machines can be easier,
cheaper, more flexible and more fault-tolerant than building
a single universal robot to accomplish all possible
Tasks may be inherently too complex for a single robot to
accomplish, or performance benefits can be gained from using
multiple robots. A population of cooperating taskable machines
is less limited by spatial and temporal constraints: certain
problem classes are provably beyond the capability of a
monolithic robot, essentially because a single robot cannot be
spatially distributed (that is, in two places at the same time).
Colonies of taskable robots are of independent research interest
since their development may shed light on fields spanning
the social sciences (organization theory, economics), life sciences
(theoretical biology, animal ethology) and cognitive sciences
(psychology, learning, artificial intelligence).
Historically, implementation of robotic colonies may have been
viewed as strictly more difficult than implementation of
a single robot. However, restricting robotic colonies
to be composed of simpler application-specific robots
can compensate for the added complexity of coordination.
 gives example application domains.
In recent years, works such as [31,35]
have established frameworks for task decomposition and control
in multiple-robot systems.
Several of these have been implemented using real robots
(see  for a detailed survey).
Yu Uny Cao
Fri May 12 16:04:55 PDT 1995