  Quadrotors with large thrust-to-weight ratios are able to track aggressive
trajectories with sharp turns and high accelerations. In this work, we develop
a search-based trajectory planning approach that exploits the quadrotor
maneuverability to generate sequences of motion primitives in cluttered
environments. We model the quadrotor body as an ellipsoid and compute its
flight attitude along trajectories in order to check for collisions against
obstacles. The ellipsoid model allows the quadrotor to pass through gaps that
are smaller than its diameter with non-zero pitch or roll angles. Without any
prior information about the location of gaps and associated attitude
constraints, our algorithm is able to find a safe and optimal trajectory that
guides the robot to its goal as fast as possible. To accelerate planning, we
first perform a lower dimensional search and use it as a heuristic to guide the
generation of a final dynamically feasible trajectory. We analyze critical
discretization parameters of motion primitive planning and demonstrate the
feasibility of the generated trajectories in various simulations and real-world
experiments.
