27-09-2012, 04:54 PM
Swarm Robotics
Swarm Robotics.docx (Size: 249.13 KB / Downloads: 26)
“When everybody thinks alike, nobody is thinking much”, is so rightly said. Think out-of-the-box and you potent some innovation or maybe an invention; credits to your gamut. To speak in line with the concept here, swarming population; not always, is a bad idea. How about rescuing some disaster hit zone with swarming intelligent population or maintaining a warehouse with moving, self-operational shelves? A great idea indeed. Well, this is all about a seemingly new concept of Swarm Robotics. Everybody, in active adolescence or passive maturity may be, must have noticed the movement of ants or similar insects. It is awesomely coordinated and aligned with respect to each other. They accomplish their task collectively by keeping an eye on each other’s movement. This type of coordinated movement in insects is termed as “Swarm” and when this movement is performed by some group of robots then in technical terms it is called as “Swarm Robotics” inspired by colonies of ants and swarms of bees. Simply put, Swarm Robotics is a multi robot system which consists of a large number of simple, physical autonomous robots. It was first coined by Gerardo Beni; professor at University of California and Jing Wang in 1989 in order to impart a notion of swarm intelligence to cellular robotic systems.
Introduction to Swarm Intelligence; the wisdom of crowd
Swarm Intelligence is a property of a system or group of systems wherein the members of the group interact locally with each other and the environment in a decentralized manner thereby attaining the desired goal via self-organization. By self-organization we mean the emergence of a global, complex pattern by local level interaction between low-level, simple but autonomous components of the system. The application of swarm intelligence to robotics has conceived to the very idea of swarm robotics. Studies of self-organization in biological species like insects had acted as the biggest inspiration for swarm robotics. Some of the legendary examples are ant colonies, birds flocking, food foraging, schooling of fishes, etc. Let’s have a good look at one of these to find the crux.
Foraging of food by ant colonies: Ants are social insects that do not have eyes or ears. Ants communicate by touch and smell. It sniffs with its antennae to discover whether an intruder is a friend or a foe. They usually set out of their nests in groups for food foraging. Before they leave the nest each day, foragers normally wait for early morning patrollers to return. As the patrollers return and enter their nest, they touch their antennas shortly with the foragers’. Taking this signal as a trigger, the foragers set out for foraging. But not just one contact does the job, foragers require several contacts not more than ten seconds apart before it go out. Foragers use the rate of their encounters with patrollers to tell if it's safe to go out. So, this is how swarm intelligence works, each ant works on its own using local information and without any centralized control. Even if one or two members accidentally run out of the group, the group dynamics remain unaltered and it goes on.
Swarm Robotics Algorithms
So far, we have witnessed some quintessential notions of swarm robotics. In order to implement the notion of decentralization, self-organization, flocking and many others, a lot of research has been done and some control algorithms had been designed, both for real and simulated robots. Some of these algorithms are discussed below:
1. Flocking Algorithm (FA) - A flock can be defined as an aggregation of thousands of individuals/birds in an elegant and organized manner. The FA was given by Craig Reynolds. The algorithm is based on three basic rules simulating which a much organized flock is obtainable. Before taking up the rules let us also consider the prerequisites for its application. The FA is applied for limited computational power and only minimalist swarm robot equipment. Such basic equipment is a set of distance sensors, which are usually used for collision avoidance. Each robot in the swarm emits IR pulses periodically. The first rule in FA is called the collision avoidance rule. Here, the front sensor’s active IR response is checked for obstacle detection. If the recorded observation exceeds the threshold, the robot takes a random turn. If not, then the passive values of all the other sensors are checked for threshold. In case any of the sensors is found to exceed the threshold limit, the robot takes a turn presuming another robot close to it. This rule is called separation rule. The third rule in flocking algorithms is usually the alignment rule which generates the common direction of movement in a flock.
Swarm Robotics.docx (Size: 249.13 KB / Downloads: 26)
“When everybody thinks alike, nobody is thinking much”, is so rightly said. Think out-of-the-box and you potent some innovation or maybe an invention; credits to your gamut. To speak in line with the concept here, swarming population; not always, is a bad idea. How about rescuing some disaster hit zone with swarming intelligent population or maintaining a warehouse with moving, self-operational shelves? A great idea indeed. Well, this is all about a seemingly new concept of Swarm Robotics. Everybody, in active adolescence or passive maturity may be, must have noticed the movement of ants or similar insects. It is awesomely coordinated and aligned with respect to each other. They accomplish their task collectively by keeping an eye on each other’s movement. This type of coordinated movement in insects is termed as “Swarm” and when this movement is performed by some group of robots then in technical terms it is called as “Swarm Robotics” inspired by colonies of ants and swarms of bees. Simply put, Swarm Robotics is a multi robot system which consists of a large number of simple, physical autonomous robots. It was first coined by Gerardo Beni; professor at University of California and Jing Wang in 1989 in order to impart a notion of swarm intelligence to cellular robotic systems.
Introduction to Swarm Intelligence; the wisdom of crowd
Swarm Intelligence is a property of a system or group of systems wherein the members of the group interact locally with each other and the environment in a decentralized manner thereby attaining the desired goal via self-organization. By self-organization we mean the emergence of a global, complex pattern by local level interaction between low-level, simple but autonomous components of the system. The application of swarm intelligence to robotics has conceived to the very idea of swarm robotics. Studies of self-organization in biological species like insects had acted as the biggest inspiration for swarm robotics. Some of the legendary examples are ant colonies, birds flocking, food foraging, schooling of fishes, etc. Let’s have a good look at one of these to find the crux.
Foraging of food by ant colonies: Ants are social insects that do not have eyes or ears. Ants communicate by touch and smell. It sniffs with its antennae to discover whether an intruder is a friend or a foe. They usually set out of their nests in groups for food foraging. Before they leave the nest each day, foragers normally wait for early morning patrollers to return. As the patrollers return and enter their nest, they touch their antennas shortly with the foragers’. Taking this signal as a trigger, the foragers set out for foraging. But not just one contact does the job, foragers require several contacts not more than ten seconds apart before it go out. Foragers use the rate of their encounters with patrollers to tell if it's safe to go out. So, this is how swarm intelligence works, each ant works on its own using local information and without any centralized control. Even if one or two members accidentally run out of the group, the group dynamics remain unaltered and it goes on.
Swarm Robotics Algorithms
So far, we have witnessed some quintessential notions of swarm robotics. In order to implement the notion of decentralization, self-organization, flocking and many others, a lot of research has been done and some control algorithms had been designed, both for real and simulated robots. Some of these algorithms are discussed below:
1. Flocking Algorithm (FA) - A flock can be defined as an aggregation of thousands of individuals/birds in an elegant and organized manner. The FA was given by Craig Reynolds. The algorithm is based on three basic rules simulating which a much organized flock is obtainable. Before taking up the rules let us also consider the prerequisites for its application. The FA is applied for limited computational power and only minimalist swarm robot equipment. Such basic equipment is a set of distance sensors, which are usually used for collision avoidance. Each robot in the swarm emits IR pulses periodically. The first rule in FA is called the collision avoidance rule. Here, the front sensor’s active IR response is checked for obstacle detection. If the recorded observation exceeds the threshold, the robot takes a random turn. If not, then the passive values of all the other sensors are checked for threshold. In case any of the sensors is found to exceed the threshold limit, the robot takes a turn presuming another robot close to it. This rule is called separation rule. The third rule in flocking algorithms is usually the alignment rule which generates the common direction of movement in a flock.