3D planning and trajectory optimization for real-time generation of smooth MAV trajectories

Complex indoor and outdoor missions for autonomous micro aerial vehicles (MAV) constitute a demand for fast generation of collision-free paths in 3D space. Often not all obstacles in an environment are known prior to the mission execution. Consequently, the ability for replanning during a flight is key for success. Our approach utilizes coarse grid-based path planning with an approximate model of flight dynamics to determine collision-free trajectories. To account for the flight dynamics and to mitigate discretization effects, these trajectories are further optimized with a gradient-based motion optimization method. We evaluate our method on an outdoor map with buildings and report trajectory costs and runtime results.