TY - JOUR
T1 - An introduction to swarming robotics
T2 - application development trends
AU - Abidin, Z. Z.
AU - Arshad, M. R.
AU - Ngah, U. K.
N1 - Publisher Copyright:
© CSpringer Science+Business Media Dordrecht 2013.
PY - 2015/4
Y1 - 2015/4
N2 - Animals help to sustain the environment’s life cycle and ecosystem. Without human intervention, these creatures carry out their ‘spontaneous routine’ jobs and contribute towards balance in nature. Any natural system that congregates as a result of some form of collective intelligence of nature is also known as swarm intelligence (SI). This metaphor inspires a variety of techniques to solve the problem of calculating, in most cases dealing with optimization problems and has sparked interest amongst scientists. It is very trying for a new researcher to understand the whole concept of swarming robotics (SR) and optimization algorithm (i.e. realizing the idea from animal’s perception to the SR application). In addition, the existing algorithms are computationally complicated, difficult to be understood by beginners as there are too many parameters. Thus, in this paper, we simplify the three branches of the main applications which are frequently used for SI namely: (1) optimization and networks design, (2) prediction and forecasting, and (3) SR. This paper summarizes the basic understanding overview of swarming robotics and discusses their basic concepts and principles.
AB - Animals help to sustain the environment’s life cycle and ecosystem. Without human intervention, these creatures carry out their ‘spontaneous routine’ jobs and contribute towards balance in nature. Any natural system that congregates as a result of some form of collective intelligence of nature is also known as swarm intelligence (SI). This metaphor inspires a variety of techniques to solve the problem of calculating, in most cases dealing with optimization problems and has sparked interest amongst scientists. It is very trying for a new researcher to understand the whole concept of swarming robotics (SR) and optimization algorithm (i.e. realizing the idea from animal’s perception to the SR application). In addition, the existing algorithms are computationally complicated, difficult to be understood by beginners as there are too many parameters. Thus, in this paper, we simplify the three branches of the main applications which are frequently used for SI namely: (1) optimization and networks design, (2) prediction and forecasting, and (3) SR. This paper summarizes the basic understanding overview of swarming robotics and discusses their basic concepts and principles.
KW - Artificial intelligence (AI)
KW - Swarm intelligence (SI)
KW - Swarming robot (SR)
UR - http://www.scopus.com/inward/record.url?scp=84924851650&partnerID=8YFLogxK
U2 - 10.1007/s10462-013-9397-8
DO - 10.1007/s10462-013-9397-8
M3 - Article
AN - SCOPUS:84924851650
SN - 0269-2821
VL - 43
SP - 501
EP - 514
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
IS - 4
ER -