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Task Allocation for a robotic swarm based on an Adaptive Response Threshold Model

Biological systems are often composed of many well-organized elements, for instance the flock of birds or social insect communities such as bees or ants. However, developing a swarm robotic system with similar functions, which could be flexible and adapt to environmental changes is undoubtedly complex. In order to achieve such high goal, a good task allocation method, which can regulate and achieve an efficient labor division is crucial. In this paper we propose an optimized version of the simple Response Threshold Model [8] using a discretized version of the Attractor Selection paradigm, in order to dynamically change the threshold parameter (θ). Simulation experiments are carried out in order to study the effects of these optimization measures on the performance of a foraging mission. Simulation experiments verified that the resultant optimized model can improve the adaptation capabilities of previous systems, making a swarm of robots able to adapt more efficiently to dynamical situations.