Foraging optimization in swarm robotic systems based on an adaptive response threshold model

Developing an animal-like highly organized swarm system, which is capable to adapt to environmental changes as well as dynamic situations is undoubtedly complex. A good task allocation method, which can regulate and achieve an efficient labor division among the swarm agents is a crucial element for this kind of systems. In this paper, we propose an extended model of the simple Response Threshold Model using a discretized version of the Attractor Selection paradigm in order to dynamically control the threshold parameter. Simulation experiments are carried out with the purpose of studying the effects of these optimization measures on the performance of a foraging mission. Simulation experiments verified that the resultant optimized model can improve adaptation capabilities of previous systems, making a swarm of robots able to adapt more efficiently to dynamical situations, and therefore increase its survival rate. Graphical Abstract

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