  In this paper, we present a real-time approach to local trajectory replanning
for microaerial vehicles (MAVs). Current trajectory generation methods for
multicopters achieve high success rates in cluttered environments, but assume
that the environment is static and require prior knowledge of the map. In the
presented study, we use the results of such planners and extend them with a
local replanning algorithm that can handle unmodeled (possibly dynamic)
obstacles while keeping the MAV close to the global trajectory. To ensure that
the proposed approach is real-time capable, we maintain information about the
environment around the MAV in an occupancy grid stored in a three-dimensional
circular buffer, which moves together with a drone, and represent the
trajectories by using uniform B-splines. This representation ensures that the
trajectory is sufficiently smooth and simultaneously allows for efficient
optimization.
