Unifying microscopic flocking motion models for virtual, robotic, and biological flock members

Flocking motions have been the subject of hundreds of studies over the past six decades. The vast majority of models have nearly identical aims: bottom-up demonstration of basic emergent flocking motions. Despite a significant fraction of the literature providing algorithmic descriptions of models, incompleteness and imprecision are also readily identifiable in flocking algorithms, algorithmic input, and validation of the models. To address this issue, this meta-study introduces a data-flow template, which unifies many of the existing approaches. Additionally, there are small differences and ambiguities in the flocking scenarios being studied by different researchers; unfortunately, these differences are of considerable significance. For example, much subtlety is needed to specify sensory requirements exactly and minor modifications may critically alter a flock’s exhibited motions. We introduce two taxonomies that minimize both incompleteness and imprecision, and enable us to highlight those publications that study flocking motions under comparable assumptions. Furthermore, we aggregate and translate the publications into a consolidated notation. The common notation along with the data-flow template and the two taxonomies constitute a collection of tools, that together, facilitates complete and precise flocking motion models, and enables much of the work to be unified. To conclude, we make recommendations for more diverse research directions and propose criteria for rigorous problem definitions and descriptions of future flocking motion models.