Real-Time Long Range Trajectory Replanning for UAVs in Presence of Dynamics Obstacles

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. It requires to provide the desired trajectory to be followed in the system initialization phase. Subsequently, it 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 trajectory, velocity components(x,y and z) and yaw rate are calculated. Those values are sent to the controller at a constant frequency to navigate the vehicle autonomously. Finally, we have evaluated each of the components separately and tested the complete system in the simulated and real environment.