Swarm Robotics

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Abstract:
This paper presents an introduction to the world of swarm robots and adumbrates its applications.
Swarm robotics is currently one of the most important application areas for swarm intelligence. Swarms provide the possibility of enhanced task performance, high reliability (fault tolerance), low unit complexity and decreased cost over traditional robotic systems. They can accomplish some tasks that would be impossible for a single robot to achieve. Swarm robots can be applied to many fields, such as flexible manufacturing systems, spacecraft, Inspection/maintenance, construction, agriculture, and medicine work.
Swarm-bots are a collection of mobile robots able to self-assemble and to self-organize in order to solve problems that cannot be solved by a single robot. These robots combine the power of swarm intelligence with the flexibility of self-reconfiguration as aggregate swarm-bots can dynamically change their structure to match environmental variations.
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. Robots are going to be an important part of the future. In the near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees. ?Once robots are useful, groups of robots are the next step, and will have tremendous potential to benefit mankind. Software designed to run on large groups of robots is the key needed to unlock this potential.

Swarm Robotics:
Swarm robotics is currently one of the most important application areas for swarm intelligence. Swarms provide the possibility of enhanced task performance, high reliability (fault tolerance), low unit complexity and decreased cost over traditional robotic systems. They can accomplish some tasks that would be impossible for a single robot to achieve. Swarm robots can be applied to many fields, such as flexible manufacturing systems, space crafts, inspection/maintenance, construction, agriculture and medicine work.
Swarm-bots are a collection of mobile robots able to self assemble and to self organise in order to solve problems that cannot be solved by a single robot. These robots combine the power of swarm intelligence with the flexibility of self reconfiguration as aggregate swarm-bots can dynamically change their structure to match environmental variations.

What is a ?SWARM??
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.

Evolution of swarm (Biological Basis and Artificial Life)
Researchers try to examine how collections of animals, such as flocks, herds and schools, move in a way that appears to be orchestrated. A flock of birds moves like a well choreographed dance troupe. They veer to the left in unison, and then suddenly they may all dart to the right and swoop down towards the ground. How can they coordinate their actions so well? In 1987, Reynolds created a ?boid? model, which is a distributed behavioral model, to simulate on a computer the motion of a flock of birds. Each boid is implemented as an independent actor that navigates according to its own perception of the dynamic environment. A boid must observe the following rules. First, the ?avoidance rule? says that a boid must move away from boids that are too close so as to reduce the chance of in-air collisions. Second, the ?copy rule? says a boid must fly in the general direction that the flock is moving by averaging the other boids? velocities and directions. Third, ?the center rule? says that a boid should minimize exposure flock?s exterior by moving toward the perceived center of the flock. Flake added a fourth rule, ?view? that indicates that a boid should move laterally away from any boid that blocks its view. This boid model seems reasonable if we consider it from another point of view, that of it acting according to attraction and repulsion between neighbors in a flock. The repulsion relationship results in the avoidance of collisions and attraction makes the flock keep shape, i.e., copying movements of neighbors can be seen as a kind of attraction. The centre rule plays a role in both attraction and repulsion. The swarm behavior of the simulated flock is the result of the dense interaction of the relatively simple behaviors of the individual boids.

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.

Ant colony optimization:
Ant colony optimization or ACO is a meta heuristic optimization algorithm that can be used to find approximate solutions to difficult combinatorial optimization problems. In ACO artificial ants build solutions by moving on the problem graph and they, mimicking real ants, deposit artificial pheromone on the graph in such a way that future artificial ants can build better solutions. ACO has been successfully applied to an impressive number of optimization problems.

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.
In near future, it may be possible to produce and deploy large numbers of inexpensive, disposable, meso-scale robots. Although limited in individual capability, such robots deployed in large numbers can represent a strong cumulative force similar to a colony of ants or swarm of bees.

Application of Robot Swarms:
There are many applications for swarms of robots. Multiple vacuum cleaner robots might need to share maps of areas where they have previously cleaned. A swarm of mars rovers might need to disperse throughout the environment to locate promising areas, while maintaining communications with each other. Robots used for earthquake rescue might come in three flavors: thousands for cockroach sized scouts to infiltrate the debris and locate survivors, a few dozen rat-sized structural engineers to get near the scene and solve the ?pick-up-sticks? problem of getting the rubble off, and a few brontosaurus-sized heavy lifters to carry out the rescue plan.
In all these applications, individual robots must work independently, only communicating with other nearby robots. It is either too expensive (robot vacuums need to be very cheap, too far (it takes 15 minutes for messages to get to Mars), or impossible (radio control signals cannot penetrate into earthquake rubble) to control all of the robots from a centralized location. However, a distributed control system can let robots from a centralized location. However, a distributed control system can let robots interact with other nearby robots, cooperating amongst themselves to accomplish their mission.

Journey into small spaces:
The mini-machines could travel in swarms like insects and go into locations too small for their bulkier cousins, communicating all the while with each other and human operators in a remote location.
Eventually fleets of robots could scamper through pipes looking for chemical releases of patrol buildings in search of prowlers. Taking the smaller robots in large numbers have the better chances of finding what we are looking for.
?Currently these robots can navigate a field of coins, puttering along at 20 inches (50 cm) a minute on track wheels similar to those on tanks. The treads give added mobility over predecessors with conventional wheels, allowing it to travel over thick carpet. Though they can?t zip along as fast as a spider or ant yet, with modifications it could go up to five times faster.

Covert uses possible:
The size of the robot is limited by the size of its power source. The frame must be large enough to hold three watch batteries, which drive its motors and instruments. The robot could play a major role in intelligence gathering. Over the next several years these mini robot can be fitted with impressive options, including video cameras and infrared or radio wireless two-way communications.

Terminators, Transformers and Other Self-Reconfiguring Robots:
The coolest thing about Transformers, of course, is that they can take two completely different shapes. Most can be bipedal robots or working vehicles. Some can instead transform into weapons or electronic devices. A Transformer?s two forms have vast different strengths and capabilities.
This is completely different from most real robots, which are usually only good at performing one task or a few related tasks. The Mars Exploration Rovers, for example, can do the following:

  • Generate power with solar calls and store it in batteries.
  • Drive across the landscape.
  • Take pictures.
  • Drill into rocks.
  • Use spectrometers to record temperatures, chemical compositions, X-rays and alpha particles
  • Send the recorded data back to Earth using radio waves.

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