This paper presents a novel Iterated Extended Set Membership Filter (IESMF) with an application to relative localization. For safe operation of formations of automatic vehicles, consistent uncertainty estimates are of crucial importance.Here, a localization filter that provides ellipsoidal regions that are guaranteed to contain another vehicles position is presented. The proposed iterative update step can appreciably reduce the size of the a posteriori state ellipsoid. The idea of using SIVIA as a baseline to quantify conservativeness is introduced. Another novelty is that we take into account parametric uncertainty of the observation equation.The proposed filter is applied to a two unmanned aircraft systems (UAS) localization problem in simulation with observation noise obtained from real sensors. Simulation results illustrate the effective reduction of filter conservativeness by a small number of iterative updates.

This article discusses the rendezvous maneuver for a fleet of small fixed-wing Unmanned Aerial Vehicles (UAVs). Trajectories have to be generated on-line while avoiding collision with static and dynamic obstacles and minimizing rendezvous time. An approach based on Model Predictive Control (MPC) is investigated which assures that the dynamic constraints of the UAVs are satisfied at every time step. By introducing binary variables, a Mixed Integer Linear Programming (MILP) problem is formulated. Computation time is limited by incorporating the receding horizon technique. A shorter planning horizon strongly reduces computation time, but delays detection of obstacles which can lead to an infeasible path. The result is a robust path planning algorithm that satisfies the imposed constraints. However, further relaxation of the constraints and fine-tuning is necessary to limit complexity.

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