Abstract
In recent days, self-assembling swarm robots have been studied by a number of researchers due to their advantages such as high efficiency, stability, and scalability. However, there are still critical issues in applying them to practical problems in the real world. The main objective of this study is to develop a novel self-assembling swarm robot algorithm that overcomes the limitations of existing approaches. To this end, multitree genetic programming is newly designed to efficiently discover a set of patterns necessary to carry out the mission of the self-assembling swarm robots. The obtained patterns are then incorporated into their corresponding robot modules. The computational experiments prove the effectiveness of the proposed approach.
Highlights
When robots try to successfully complete their mission in various environments, it is necessary to retain high autonomy and intelligence like humans
Swarm robotics is a field of research on the swarm of robots, which is working in conjunction with the natureinspired algorithms
This paper develops a new control mechanism for the swarm robots by using evolutionary techniques as an effort to get over these limitations
Summary
When robots try to successfully complete their mission in various environments, it is necessary to retain high autonomy and intelligence like humans. Robots should employ precise sensors and complex controllers and mount high performance processors in order to attain complete autonomy and intelligence. These enhanced devices bring forth the extremely expensive cost in constructing an autonomous robot system. The efficiency of the autonomous robot system dramatically decreases as the working space enlarges [1]. Swarm intelligence makes a swarm of robots perform their tasks in collaboration with themselves. It denotes that the swarm robots can have a lot of advantages: stability, scalability, robustness, efficiency, and so on. The swarm robotics can be applied to a lot of areas in the sense of improving efficiency on the cost of installation and maintenance [2]
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