Rapidly Exploring Random Trees-based initialization of MPC technique designed for formations of MAVs

Motion planning techniques suited for initialization of Model Predictive Control based methodology applied for complex maneuvering and stabilization of formations of Micro Aerial Vehicles are proposed in this paper. Two approaches to initialization of the formation driving method will be described, experimentally verified, evaluated and compared. The first proposed method is based on multiobjective optimization of the trajectory guess obtained by a Rapidly Exploring Random Trees technique. It represents an easy to implement and robust method suited for off-line initialization of the formation driving algorithm. The second proposed method is based on sequential processing of parts of the obtained trajectory. This method is well scalable and thus applicable in large workspaces with complex obstacles. In addition, the second method enables a significant reduction of computational time as is shown by comparison of series of simulations in different environments.