  For accomplishing a variety of missions in challenging environments, the
capability of navigating with full autonomy while avoiding unexpected obstacles
is the most crucial requirement for UAVs in real applications. In this paper,
we proposed such a computationally efficient obstacle avoidance trajectory
planner that can be used in cluttered unknown environments. Because of the
narrow view field of single depth camera on a UAV, the information of obstacles
around is quite limited thus the shortest entire path is difficult to achieve.
Therefore we focus on the time cost of the trajectory planner and safety rather
than other factors. This planner is mainly composed of a point cloud processor,
a waypoint publisher with Heuristic Angular Search(HAS) method and a motion
planner with minimum acceleration optimization. Furthermore, we propose several
techniques to enhance safety by making the possibility of finding a feasible
trajectory as big as possible. The proposed approach is implemented to run
onboard in real-time and is tested extensively in simulation and the average
control output calculating time of iteration steps is less than 18 ms.
