  Real-time long-range local planning is a challenging task, especially in the
presence of dynamics obstacles. We propose a complete system which is capable
of performing the local replanning in real-time. Desired trajectory is needed
in the system initialization phase; system starts initializing sub-components
of the system including point cloud processor, trajectory estimator and
planner. Afterwards, the multi-rotary aerial vehicle starts moving on the given
trajectory. When it detects obstacles, it replans the trajectory from the
current pose to pre-defined distance incorporating the desired trajectory.
Point cloud processor is employed to identify the closest obstacles around the
vehicle. For replanning, Rapidly-exploring Random Trees (RRT*) is used with two
modifications which allow planning the trajectory in milliseconds scales. Once
we replanned the desired path, velocity components(x,y and z) and yaw rate are
calculated. Those values are sent to the controller at a constant frequency to
maneuver the vehicle autonomously. Finally, we have evaluated each of the
components separately and tested the complete system in the simulated and real
environments.
