What is Swarm Behaviour?
From a flock of birds flying to a group of ant’s foraging for food, these natural behaviours of social species have been an interest to many biologists and computer scientists for years. This behaviour of aggregated motion is known as ‘swarm behaviour’ and is studied in the field of artificial intelligence to achieve a similar model of these biological swarms (Liu and Passino, 2000, p.1). Achieving coordinated movement in an artificial swarm that work together towards a common goal without a central leader or direction would allow engineers to apply this knowledge in optimization, robotic and military applications.
What is Swarm Intelligence
Swarm Intelligence is a branch of artificial intelligence which is closely related to swarm behaviour. This field of study is based on the collective behaviour of decentralized, self-organized systems that mimics swarm behaviour (Beni and Wang, 2009). In summary, SI relies on the idea that simple individual behaviours can result to sophisticated and intelligent group behaviours.
Swarm Intelligence Algorithims
SI Algorithims are usually decentralized and self-organized, which means there is no coordination or central control, so each individual follows a set of simple rules based on local information or neighboring behaviours of the swarm individuals.
Examples of Swarm Intelligence Algorithims: (click on the links)
Swarm Robotics
Swarm robotics extends the same swarm intelligence principles to physical robotics, whereas swarm intellegience is used to develop algorithims for different systems. It expands the study to large groups of simple robots that would carry out tasks together to accomplish challenging duties, which would be impossible for a single robot.
Advantages of Swarm Robotics
– Scalability: These robots can work in a larger area to accomplish a task,
– Robustness: Since swarm robots work in collective groups, if a single robot stops functioning, the task will not be affected. However a single robot system would not be able to continue the task.
– Due to Parallelism, which means that the robots accomplish a task concurrently, tasks can be accomplished faster than a single robot system.
– This technique is also a cheaper alternative because a group of simple robots is less costly, than one powerful expensive robot for each separate function.
– Econominical benefits: cheaper to design and manufacture.
(Gupta et al,. 2016)
Disadvantages of Swarm Robotics
-Cost: A large number of robots can be costly, which could limit scalibity of the soloutions.
-Security: With networked systems, vunerability to cyber attacks or security threats are always a concern. If attackers can take control of robots, it could lead to distrupting the communication between them and other security issues.
-Communication: It can be challenging to maintain continuous and accurate communication between a large swarm of robots because of channels distrupted by noise or interference.
-Complexity: Algorithims and control systems can be very difficult to coordinate and design.
Applications of Swarm Robotics
The rapid pace of technology advancements would increase and develop swarm robotics in various industries. Here are some possible applications:
Agriculture: Eventhough there has been no commercial applications of swarm robotics in agriculture yet, it could be used in precisiom agriculture to monitor crops or other large scale agricultural applications(Trianni et al.,2016).
Manufacturing: Swarm robotics could also be used to assemble complex products and to perform inspections for quality.
Security and surveillance: Swarm robotics can monitor large areas, detect intruders or work as other large security systems (Lapeña et al.,2014).
Construction: They can also be used to assist in difficult tasks, like bricklaying and scaffolding.
Enviromental monitoring: They can be implemented in enviromental monitoring applications, where they would collect data on weather patterns and wildlife populations.
references
Albani, D., IJsselmuiden, J., Haken, R. and Trianni, V., 2017, August. Monitoring and mapping with robot swarms for agricultural applications. In 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS) (pp. 1-6). IEEE.
Beni, G. and Wang, J., 2009. Swarm Intelligence.
Gupta, M., Saxena, D., Kumari, S. and Kaur, D., 2016. Issues and applications of swarm robotics. International Journal of Research in Engineering, Technology and Science, 6, pp.1-5.
Lapeña, P.M.L., Blanco, J.I.Q., Bunda, K.I., Cruz, A.G.S., Ramirez, A.I. and Bandala, A.A., 2014, October. Swarm algorithm implementation in mobile robots for security and surveillance. In TENCON 2014-2014 IEEE Region 10 Conference (pp. 1-5). IEEE.
Liu, Y. and Passino, K.M., 2000. Swarm intelligence: Literature overview. Department of electrical engineering, the Ohio State University.