Collective Motion in Pelotons and Fish Schools: Observation and Modeling

In recent years there has been a sharp increase in deriving inspiration from nature for engineering applications. However there has been lack of high-resolution data from which insights into collective behavior can be drawn and models can be validated. In this imaging based thesis, data from high-speed, overhead cameras, and GPS tracking are used to collect position and sensory data for large groups of 40 or more members. These data helps us understand how different members (fish, cyclists) of a group interact with each other in collective motion. The focus of this work is to find how individual behavior leads to collective behavior in different groups. In some of these groups, all individuals had the same goal whereas in other groups, some individuals had different agendas. The ultimate goal is to create models which can be used to explain and predict individual behaviors in collective motion. A visual sensor based model has been build which is able to replicate collective motion from experiments with minimal input. A cross correlation based algorithm has also been developed which can identify individuals with different purpose within a group. These models can be used for robotic warming, autonomous vehicles and naval purposes