A novel framework for designing and implementing morphogenetic robotic swarms by using very simple, particle-like mobile robots is presented. This simple model extension naturally enables growth and self-assembly of robotic swarms by local information transmission and stochastic differentiation, and moreover, self-repair by stochastic re-differentiation of robots. Swarm Chemistry has been used as the basic model for the proposed design framework. For a given swarm, specifications for its macroscopic properties are indirectly and implicitly woven into a list of different kinetic parameter settings for each swarm component. Once a robot is activated, it simply reacts kinetically to nearby robots and independently re-differentiates with small probability. This architecture demonstrates dynamic production and maintenance of patterns, which allows robust, adaptive behaviors under variable environmental conditions, including self-repair with no central controller. If the number of robots is increased too much, the swarm loses coherence and the design embedded in the original recipe is not reproduced correctly.
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