The computer revolution changed the human societies in different important fields like communication, transportation, industrial production and technological advances science. However, some problems cannot be tackled with traditional hardware and software; hence we need a new computing technique to solve these hard problems in the most efficient and optimized way. The Bio-Computing technique is new alternative for the standard computing as it becomes one of the most important techniques in the new science’s era.
Swarm intelligence is a very hot topic related to the bio-computing which its main goal is the performance optimization and robustness.
What is Swarm Intelligence ?
SI (Swarm Intelligence) systems are typically made up of a population of simple agents or boids interacting locally with one another and with their environment. The inspiration often comes from nature, especially biological systems. The agents follow very simple rules, and although there is no centralized control structure dictating how individual agents should behave, local, and to a certain degree random, interactions between such agents lead to the emergence of “intelligent” global behavior, unknown to the individual agents.
What is Swarm Robotics ?
Swarm robotics is a relatively new field that focuses on controlling large-scale homogeneous multi-robot systems. These systems are used to develop useful macro-level behaviors while being made of modules that are very simple in design and compact in size. These properties allow robot swarms to reach populations ranging from a dozen modules to hundreds of modules. The theme of simplicity and elegance resonates throughout swarm robotics research in both the designs of the robots as well as the algorithms that are devised for these systems.
Swarm Robotics vs. Single Robot
Robotic swarms have several advantages over their more complex individual robot counterparts and are the results of using many robots instead of just one. This is made possible by the simple design of the robot modules because they are often less expensive and easier to build. When comparing the capabilities of a robot swarm to the capabilities of an individual robot, it is best to view the swarm as an individual entity performing complex behaviors at the macro-level.
The first improvement is an obvious one: robot swarms are able to cover more area than an individual robot. This is analogous to distributed search algorithms that are able to cover different parts of a search space at once.
The second improvement over individual robots is swarm robots are fault tolerant because the swarm robotics algorithms do not require robots to depend on one another. If a single module fails, the rest of the swarm can continue performing its actions as if that module never existed. Meanwhile, an individual robot system may become worthless if there is a failure in a critical component. This type of robustness is an extremely important feature in complex or hostile environments. Another feature of robot swarms is their effectiveness scales well with the number of members. Adding more robots is all that has to be done to increase the effectiveness of a swarm. The algorithms for swarms scale well and do not depend on the number of robots. On the other hand, it is not always clear how to improve the effectiveness of an individual robot system. Often times improvements in hardware require additional software upgrades, which is not the case with swarms. These properties make multi-robot systems suitable for several application domains.
- Swarm Robotics Algorithms : A Survey – Don Miner – 2007
- Survey of Swarm Robotics Techniques – A Tutorial – Angie Shia
- Swarm Robotics – Bio Inspired Artificial Intelligence