Reactive model for autonomous vehicles formation following a mobile reference

The emergence of collective motion in nature is ubiquitous and can be observed from colonies of bacteria to flocks of birds. The scientific community is interested in understanding how the local interactions drive the crowd toward global behaviors. This paper presents an agent-based reactive model for groups of vehicles that aims to make the formation to follow a moving reference, represented as a virtual agent. The model is called reactive because the agents do not keep previous information but only respond to the current system state. Moreover, they only communicate with their close neighbors, limited by their sensory radius, except with the virtual agent that can be seen by everyone at the whole time. The aim of the model is to group the agents around the virtual agent while it moves to desirable directions. We solve the inverse problem of parameter estimation in order to drive the model toward specific objectives. This task is performed with the Generalized Extremal Optimization (GEO) algorithm, and the results are tested with path planning scenarios.