EF-TTOA: Development of a UAV Path Planner and Obstacle Avoidance Control Framework for Static and Moving Obstacles

Abstract

With the increasing applications of unmanned aerial vehicles (UAVs) in surveying, mapping, rescue, etc., the security of autonomous flight in complex environments becomes a crucial issue. Deploying autonomous UAVs in complex environments typically requires them to have accurate dynamic obstacle perception, such as the detection of birds and other flying vehicles at high altitudes, as well as humans and ground vehicles at low altitudes or indoors. This work’s primary goal is to cope with both static and moving obstacles in the environment by developing a new framework for UAV planning and control. Firstly, the point clouds acquired from the depth camera are divided into dynamic and static points, and then the velocity of the point cloud clusters is estimated. The static point cloud is used as the input for the local mapping. Path finding is simplified by identifying key points among static points. Secondly, the design of a trajectory tracking and obstacle avoidance controller based on the control barrier function guarantees security for moving and static obstacles. The path-finding module can stably search for the shortest path, and the controller can deal with moving obstacles with high-frequency. Therefore, the UAV can deal with both long-term planning and immediate emergencies. The framework proposed in this work enables a UAV to operate in a wider field, with better security and real-time performance.