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Tags: LIFE, ROBOTS, swarm robotics tutorial, swarm robotics research, swarm robotics projects, swarm robotics pdf, swarm robotics book, swarm robotics applications, swarm robotics, swarm robotic, swarm bots, robotic swarms, robotic swarm, open source micro robotic project, distributed robotics, applications of swarm robotics, SWARM, APPLICATION, APPLICATION OF SWARM ROBOTS IN DAY TO DAY LIFE,
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.doc  swarm robots.doc (Size: 498 KB / Downloads: 96)
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. The researcher has a great idea of developing swarm robots for the security purpose. These robots can spy in any region without any hindrance of locating it form. 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
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. [1]
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.
Post: #2
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