Abstract
Many studies on collective animal behavior seek to identify the individual rules that underlie collective patterns. However, it was not until the recent advancements of micro-electronic and embedded systems that scientists were able to create mixed groups of sensor-rich robots and animals and study collective interactions from the within a bio-hybrid group. In recent work, scientists showed that a robot-controlled lure is capable of influencing the collective decisions of zebrafish Danio rerio shoals moving in a ring and a two-room setup. Here, we study a closely related topic, that is, the collective behavior patterns that emerge when different behavioral models are reproduced through the use of a robotic lure. We design a behavioral model that alternates between obeying and disobeying the collective motion decisions in order to become socially accepted by the shoal members. Subsequently, we compare it against two extreme cases: a reactive and an imposing decision model. For this, we use spatial, directional and information theoretic metrics to measure the degree of integration of the robotic agent. We show that our model leads to similar information flow as in freely roaming shoals of zebrafish and exhibits leadership skills more often than the open-loop models. Thus, in order for the robot to achieve higher degrees of integration in the zebrafish shoal, it must, like any other shoal member, be bidirectionally involved in the decision making process. These findings provide insight on the ability to form mixed societies of animals and robots and yield promising results on the degree to which a robot can influence the collective decision making.
Highlights
From the middle of the twentieth century, modelling collective behavior has been in the center of attention of many biological, physical, and computational studies
A more detailed comparison revealed that both the Follower model (FM) and Despotic model (DM) significantly differ from the fish-only (T-HSD post hoc test, p < 0.0001 and p < 0.001, respectively) while the Feedback-Initiative model (FIM) differs significantly from the FM and DM (T-HSD post hoc test, p 0.22 and p 0.53, respectively) but not from the fish-only distribution (T-HSD post hoc test, p 0.072)
While FIM still did not perform as well as the control experiments (16 degrees 8 cm arc distance), its ability to mimic the collective decision making allowed the robot to maintain the cohesion of the shoal with on average 14 degrees better than FM and 9 degrees better than DM
Summary
From the middle of the twentieth century, modelling collective behavior has been in the center of attention of many biological, physical, and computational studies. In the past years, robots and artificial lures allowed scientists to put these theoretical models to the test in real-life scenarios and with true feedback from the animals, in order to study their collective behavior. They have since been increasingly involved in inferring the rules of interaction among animals such as bees [19,20,21], fish [22,23,24,25,26,27], birds [28,29,30] and cockroaches [31]
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