Abstract :
Swarm
robotics is the study of how large number of relatively simple physically
embodied agents can be designed such that a desired collective behavior emerges
from the local interactions among agents and between the agents and the
environment. It is a novel approach to the coordination of large numbers of
robots. It is inspired from the observation of social insects ---ants,
termites, wasps and bees--- which stand as fascinating examples of how a large
number of simple individuals can interact to create collectively intelligent
systems.
Social
insects are known to coordinate their actions to accomplish tasks that are
beyond the capabilities of a single individual: termites build large and
complex mounds, army ants organize impressive foraging raids, ants can
collectively carry large preys. Such coordination capabilities are still beyond
the reach of current multi-robot systems.
As robots
become more and more useful, multiple robots working together on a single task
will become common place. Many of the most useful applications of robots are
particularly well suited to this “swarm” approach. Groups of robots can perform
these tasks more efficiently, and can perform them in fundamentally difficult
to program and co-ordinate.
Swarm
robots are more than just networks of independent agents, they are potentially
reconfigurable networks of communicating agents capable of coordinated sensing
and interaction with the environment.
WORKING OF
SWARM :
Swarm
Intelligence:
Swarm
intelligence describes the way that complex behaviors can arise from large
numbers of individual agents each following very simple rules. For example,
ants use the approach to find the most efficient route to the food
source.Individual ants do nothing more than follow the strongest pheromone
trail left by other ants. But, by repeated process of trial and error by many
ants, the best route to the food is quickly revealed.
Software
from insects
Local
interactions between nearby robots are being used to produce large scale group
behaviors from the entire swarm. Ants , bees and termites are beautifully
engineered examples of this kind of software in use. These insects do not use
centralized communication; there is no strict hierarchy, and no one in charge.
However,
developing swarm software from the “top down”, i.e., by starting with the group
application and trying to determine the individual behaviors that it arises
from, is very difficult. Instead a “group behavior building blocks” that can be
combined to form larger, more complex applications are being developed. The
robots use these behaviors to communicate, cooperate, and move relative to each
other. Some behaviors are simple, like following, dispersing, and counting.
Some are more complex, like dynamic task assignment, temporal synchronization,
and gradient tree navigation. There are currently about forty of these
behaviors. They are designed to produce predictable outcomes when used individually,
are when combined with other library behaviors, allowing group applications to
be constructed much more easily.
Particle
swarm Optimization:
Particle
swarm optimization or PSO is a global optimization algorithm for dealing with
problems in which a best solution can be represented as a point or surface in
an n-dimensional space. Hypotheses are plotted in this space and seeded with an
initial velocity, as well as a communication channel between the particles.
Particles then move through the solution space, and are evaluated according to
some fitness criterion after each time step. Over time, particles are
accelerated towards those particles within their communication grouping which
have better fitness values. The main advantage of such an approach over other
global minimization strategies such as simulated annealing is that the large
numbers of members that make up the particle swarm make the technique
impressively resilient to the problem of local minima.
TYPES OF
SWARM:
Modular
Robots:
A module is
essentially a small, relatively simple robot or piece of a robot. Modular
robots are made of lots of these small, identical modules. A modular robot can
consist of a few modules or many, depending on the robot’s design and the task
it needs to perform. Some modular robots currently exist only as computer
simulations; others are still in the early stages of development. But they all
operate on the same basic principle- lots of little robots can combine to create
one big one. Modules can’t do much by themselves. A reconfiguring system also
has to have: Connections between the modules Systems that govern how the modules move in relation
to one another. Most modular, reconfiguring robots fit into one of the three
categories: chain, lattice and modular configuration.
Chain
robots :
Chain
robots are long chains that can connect to one another at specific points.
Depending on the number of chains and where they connect, these robots can
resemble snakes or spiders. They can also become rolling loops or bipedal,
walking robots. A set of modular chains could navigate an obstacle course by
crawling through a tunnel as a snake, crossing rocky terrain as a spider and
riding a tricycle across a bridge as a biped. Examples of chain robots are Palo
Alto Research Center’s (PARC) Polybot and Polypod and NASA’s snakebot. Most
need a human or, in theory, another robot, to manually secure the connections
with screws.
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