Real-Time Long Range Trajectory Replanning for MAVs in the Presence of Dynamic Obstacles
This addresses the problem of autonomous navigation for drones in cluttered, dynamic settings, representing an incremental improvement with specific optimizations.
The paper tackles real-time local replanning for multi-rotor aerial vehicles in dynamic obstacle environments by proposing a system using a modified RRT* algorithm, achieving trajectory planning in milliseconds and demonstrating functionality in simulations and real-world tests.
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.