Abstract
Collective navigation and swarming have been studied in animal groups, such as fish schools, bird flocks, bacteria, and slime molds. Computer modeling has shown that collective behavior of simple agents can result from simple interactions between the agents, which include short range repulsion, intermediate range alignment, and long range attraction. Here we study collective navigation of bacteria-inspired smart agents in complex terrains, with adaptive interactions that depend on performance. More specifically, each agent adjusts its interactions with the other agents according to its local environment – by decreasing the peers' influence while navigating in a beneficial direction, and increasing it otherwise. We show that inclusion of such performance dependent adaptable interactions significantly improves the collective swarming performance, leading to highly efficient navigation, especially in complex terrains. Notably, to afford such adaptable interactions, each modeled agent requires only simple computational capabilities with short-term memory, which can easily be implemented in simple swarming robots.
Highlights
Many organisms exhibit complex group behavior [1,2,3,4,5,6,7,8,9,10], including collective navigation observed in the flight of birds [11], trail organization in ants [12], and swarming of locust [13], fish [14] and bacteria [15], among others
In computational models, swarming behavior can arise from simple rules, and in particular demonstrate qualitive features of collective behavior observed in nature: Vicsek et al [16] introduced the ‘self-propelling particles’ (SPP) model, in which the motion of each individual is determined by the mean orientation of its local neighborhood with some noise induced perturbation
We found that inclusion of such adaptable interactions dramatically improves the collective swarming performance leading to highly efficient navigation especially in very complex terrains
Summary
Many organisms exhibit complex group behavior [1,2,3,4,5,6,7,8,9,10], including collective navigation observed in the flight of birds [11], trail organization in ants [12], and swarming of locust [13], fish [14] and bacteria [15], among others. Simple interaction models, which describe how each agent acts according to the result of a ‘computation’ it performs on the locations of the other agents, have been used to demonstrate and study the fundamental building blocks of complex group behavior [16,17,18,19,20,21,22,23,24]. In computational models, swarming behavior can arise from simple rules, and in particular demonstrate qualitive (and sometimes quantitative) features of collective behavior observed in nature: Vicsek et al [16] introduced the ‘self-propelling particles’ (SPP) model, in which the motion of each individual is determined by the mean orientation of its local neighborhood with some noise induced perturbation. A special feature of this model is that it leads to the emergence of collective navigation each agent does not possess individual navigation capabilities
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