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The field of quadrotor motion planning has experienced significant advancements over the last decade. Most successful approaches rely on two stages: a front-end that determines the best path by incorporating geometric (and in some cases kinematic or input) constraints, that effectively specify the homotopy class of the trajectory; and a back-end that optimizes the path with a suitable objective function, constrained by the robot's dynamics as well as state/input constraints. However, there is no systematic approach or design guidelines to design both the front and the back ends for a wide range of environments, and no literature evaluates the performance of the trajectory planning algorithm with varying degrees of environment complexity. In this paper, we propose a modular approach to designing the software planning stack and offer a parameterized set of environments to systematically evaluate the performance of two-stage planners. Our parametrized environments enable us to access different front and back-end planners as a function of environmental clutter and complexity. We use simulation and experimental results to demonstrate the performance of selected planning algorithms across a range of environments. Finally, we open source the planning/evaluation stack and parameterized environments to facilitate more in-depth studies of quadrotor motion planning, available at https://github.com/KumarRobotics/kr_mp_design
