Simulating self-organization for multi-robot systems

How do multiple robots self-organize into global patterns based on local communications and interactions? This paper describes a theoretical and simulation model called "Digital Hormone Model" (DHM) for such a self-organization task. The model is inspired by two facts: complex biological patterns are results of self-organization of homogenous cells regulated by hormone-like chemical signals, and distributed controls can enable self-reconfigurable robots to performance locomotion and reconfiguration. The DHM is an integration and generalization of reaction-diffusion model and stochastic cellular automata. The movements of robots (or cells) in DHM are computed not by the Turing's differential equations, nor the Metropolis rule, but by stochastic rules that are based on the concentration of hormones in the neighboring space. Experimental results have shown that this model can produce results that match and predict the actual findings in the biological experiments of feather bud formation among uniform skin cells. Furthermore, an extension of this model may be directly applicable to self-organization in multirobot systems using simulated hormone-like signals.

Authors

• Wei-Min Shen
• Cheng-Ming Chuong
• Peter M. Will

Keywords

• Multirobot systems
• Biological system modeling
• Biochemistry
• Cells (biology)
• Robotics and automation
• Stochastic processes
• Robots
• Chemicals
• Distributed control
• Orbital robotics
• Multi-agent Systems
• Differential Equations
• Complex Patterns
• Digital Model
• Biological Experiments
• Skin Cells
• Hormone Concentrations
• Local Interactions
• Homogeneous Cell
• Cellular Automata
• Reaction-diffusion Model
• Local Communication
• Robot Movement
• Random Number
• Diffusion Rate
• Pheromone
• Pattern Formation
• Activity Ratio
• Size Pattern
• Autonomous Elements
• Ring Of Cells
• Cell Population Density
• Dynamic Topology
• First Set Of Experiments
• Stochastic Character
• Diffusion Maps
• Rule States