Foraging in Real and Simulated environments for a Robotic Swarm based on an Adaptive Response Threshold Model

Developing a realistic animal-like highly organized swarm system, which is capable to adapt to environmental changes as well as dynamic situations is undoubtedly complex. A good labour division model, which can regulate and achieve an efficient work distribution among swarm agents is a crucial element for this kind of systems. In this paper, we analyze and compare the use of an extended model of the classical Response Threshold Model named Adaptive Response Threshold Model (ARTM) in real-robot and simulation scenarios. Experiments were carried out with the purpose of studying the effects and the performance of this new model under foraging missions. Results presented in this paper verify, that the resultant optimized model can improve adaptation capabilities of previous systems, such as reducing collision duration among robots in foraging missions, therefore making a swarm of robots able to adapt more efficiently to dynamical situations and hence increase its survival rate.