Modelling hierarchical flocking

Abstract

We present a general framework for modeling a wide selection of flocking scenarios under free boundary conditions. Several variants have been considered-including examples for the widely observed behavior of hierarchically interacting units. The models we have simulated correspond to classes of various realistic situations. Our primary goal was to investigate the stability of a flock in the presence of noise. Some of our findings are counter-intuitive in the first approximation, e.g. if the hierarchy is based purely on dominance (an uneven contribution of the neighbors to the decision about the direction of flight of a given individual) the flock is more prone to loose coherence due to perturbations even when a comparison with the standard egalitarian flock is made. Thus, we concentrated on building models based on leader-follower relationships. And, indeed, our findings support the concept that hierarchical organization can be very efficient in important practical cases, especially if the leader-follower interactions (corresponding to an underlying directed network of interactions) have several levels. Efficiency here is associated with remaining stable (coherent and cohesive) even in cases when collective motion is destroyed by random perturbations. The framework we present allows allows the study of several further complex interactions among the members of flocking agents.

Adaptive leadership overcomes persistence–responsivity trade-off in flocking

“…Combination of two of the most widespread behavioural phenomena of collectives, namely flocking and hierarchies, seems almost inevitable. Hierarchical structures are prevalent from nature to society [53], because they can be shown to result in more efficient group performance [54][55][56] by optimizing the flow of information in systems. This feature is the reason we incorporate hierarchy into the new models presented in this paper.…”

Self-Organised Collision-Free Flocking Mechanism in Heterogeneous Robot Swarms

“…Therefore, direct communication in a swarm system without having an extra observer, central controller, is one of the challenges of implementing swarm scenarios using real mobile robots [20]. Some research studies [13,29,52,53] relied on the acquisition of the location of each robot from an external observer, whereas some [30,54] regarded each robot as an abstract particle without considering physical properties e.g. weight, size, motors speed and sensing range.…”

Towards Flocking Navigation and Obstacle Avoidance for Multi-UAV Systems through Hierarchical Weighting Vicsek Model

“…The flows of information among UAVs are shown in the Figure 2a. Only when the information of an individual flows to the given UAV, can it contribute to the given UAV [17]. The dominance matrix B N = b ij N×N is defined to describe the information flow direction between each pair of UAVs.…”