Affiliation:
1. Stevens Institute of Technology, Hoboken, NJ
2. University of Surrey, UK
Abstract
Embryonic development of multicellular organisms, also known as morphogenesis, is regarded as a robust self-organization process for pattern generation. Inspired by the recent findings in biology indicating that morphogen gradients, together with a Gene Regulatory Network (GRN), play a key role in biological patterning, we propose a framework for self-organized multirobot pattern formation and boundary coverage based on an artificial GRN model. The proposed framework does not need a global coordinate system, which makes it more practical to be implemented in a physical robotic system. Moreover, an adaptation mechanism is included in the framework so that the self-organization algorithm is robust to changes in the number of robots. Various case studies of multirobot pattern formation and boundary coverage show the effectiveness of the framework.
Publisher
Association for Computing Machinery (ACM)
Subject
Software,Computer Science (miscellaneous),Control and Systems Engineering
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