Self-adaptive multi-robot construction using gene regulatory networks

Biological organisms have evolved to perform and survive in a world characterized by rapid changes, high uncertainty, infinite richness, and limited availability of information. Gene regulatory networks (GRNs) are models of genes and gene interactions at the expression level. In this paper, inspired by the biological organisms and GRNs models, a distributed multi-robot self-construction method is proposed. By using this method, a multi-robot system can self-construct to different predefined shapes, and self-reorganize to adapt to dynamic environments. Various case studies have been conducted in the simulation, and the simulation results demonstrate the efficiency and convergence of the proposed method.

[1]  Nicholas Geard,et al.  Modelling gene regulatory networks: systems biology to complex systems , 2004 .

[2]  R. Pfeifer,et al.  Self-Organization, Embodiment, and Biologically Inspired Robotics , 2007, Science.

[3]  D. A. Baxter,et al.  Modeling transcriptional control in gene networks—methods, recent results, and future directions , 2000, Bulletin of mathematical biology.

[4]  P. Maini,et al.  Spatial pattern formation in chemical and biological systems , 1997 .

[5]  Bernhard Sendhoff,et al.  Evolving in silico bistable and oscillatory dynamics for gene regulatory network motifs , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[6]  A. Arkin,et al.  Simulation of prokaryotic genetic circuits. , 1998, Annual review of biophysics and biomolecular structure.

[7]  Arantxa Etxeverria The Origins of Order , 1993 .

[8]  Wei-Min Shen,et al.  Hormone-Inspired Self-Organization and Distributed Control of Robotic Swarms , 2004, Auton. Robots.

[9]  Marco Dorigo,et al.  Ant system: optimization by a colony of cooperating agents , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[10]  Manuela M. Veloso,et al.  Multiagent Systems: A Survey from a Machine Learning Perspective , 2000, Auton. Robots.

[11]  Erfu Yang,et al.  Multiagent Reinforcement Learning for Multi-Robot Systems: A Survey , 2004 .

[12]  Yan Meng,et al.  A distributed swarm intelligence based algorithm for a cooperative multi-robot construction task , 2008, 2008 IEEE Swarm Intelligence Symposium.

[13]  Stuart A. Kauffman,et al.  The origins of order , 1993 .

[14]  Eric Klavins,et al.  Communication Complexity of Multi-robot Systems , 2002, WAFR.

[15]  A. Gierer Generation of biological patterns and form: some physical, mathematical, and logical aspects. , 1981, Progress in biophysics and molecular biology.

[16]  Farren J. Isaacs,et al.  Computational studies of gene regulatory networks: in numero molecular biology , 2001, Nature Reviews Genetics.

[17]  Tim Taylor A Genetic Regulatory Network-Inspired Real-Time Controller for a Group of Underwater Robots , 2005 .

[18]  R. Brent,et al.  Modelling cellular behaviour , 2001, Nature.

[19]  Yan Meng,et al.  LIVS: Local Interaction via Virtual Stigmergy coordination in distributed search and collective cleanup , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[20]  Behrokh Khoshnevis,et al.  Centralized sensing and control of multiple mobile robots , 1998 .

[21]  Justin Werfel,et al.  Building Blocks for Multi-robot Construction , 2004, DARS.

[22]  Hidde de Jong,et al.  Modeling and Simulation of Genetic Regulatory Systems: A Literature Review , 2002, J. Comput. Biol..

[23]  R. Solé,et al.  Gene networks capable of pattern formation: from induction to reaction-diffusion. , 2000, Journal of theoretical biology.

[24]  David H. Sharp,et al.  A connectionist model of development. , 1991, Journal of theoretical biology.

[25]  Alcherio Martinoli,et al.  Inspiring and Modeling Multi-Robot Search with Particle Swarm Optimization , 2007, 2007 IEEE Swarm Intelligence Symposium.

[26]  A. M. Turing,et al.  The chemical basis of morphogenesis , 1952, Philosophical Transactions of the Royal Society of London. Series B, Biological Sciences.

[27]  W. Jatmiko,et al.  A pso-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: theory, simulation and measurement , 2007, IEEE Computational Intelligence Magazine.

[28]  Takashi Gomi,et al.  Book Review: Evolutionary Robotics: the Biology, Intelligence, and Technology of Self-Organizing Machines , 2003, Genetic Programming and Evolvable Machines.