A cellular mechanism for multi-robot construction via evolutionary multi-objective optimization of a gene regulatory network.
A major research challenge of multi-robot systems is to predict the emerging behaviors from the local interactions of the individual agents. Biological systems can generate robust and complex behaviors through relatively simple local interactions in a world characterized by rapid changes, high uncertainty, infinite richness, and limited availability of information. Gene Regulatory Networks (GRNs) play a central role in understanding natural evolution and development of biological organisms from cells. In this paper, inspired by biological organisms, we propose a distributed GRN-based algorithm for a multi-robot construction task. Through this algorithm, multiple robots can self-organize autonomously into different predefined shapes, and self-reorganize adaptively under dynamic environments. This developmental process is evolved using a multi-objective optimization algorithm to achieve a shorter travel distance and less convergence time. Furthermore, a theoretical proof of the system's convergence is also provided. Various case studies have been conducted in the simulation, and the results show the efficiency and convergence of the proposed method.
Guo
Hongliang
H
Department of Electrical and Computer Engineering, Stevens Institute of Technology, NJ 07030, USA. hguo@stevens.edu
Meng
Yan
Y
Jin
Yaochu
Y
D000465:Algorithms; D005075:Biological Evolution; D053263:Gene Regulatory Networks; D008962:Models, Theoretical; D012371:Robotics