This chapter presents a kinodynamic motion planner for quad-rotor-like aerial robots in constrained environments. The planner is based on a simple dynamic model of the UAV and aims to generate agile and time-efficient trajectories. The local planner uses fourth-order splines to create flyable trajectories, ensuring minimal time and respecting kinodynamic constraints. This planner is integrated into a decoupled approach for global motion planning, where a geometrically valid path is first planned, followed by trajectory generation. The chapter also introduces an efficient quasi-metric and an incremental sampling strategy to enhance the performance of the planner. Simulation results demonstrate significant improvements in CPU time and trajectory quality, particularly for constrained problems. Preliminary experiments with a real quad-rotor show the ability to follow planned trajectories, although some tracking errors at high speeds are observed. Future work includes further experimental validation and improvements in localization and control to execute more agile maneuvers in constrained environments.This chapter presents a kinodynamic motion planner for quad-rotor-like aerial robots in constrained environments. The planner is based on a simple dynamic model of the UAV and aims to generate agile and time-efficient trajectories. The local planner uses fourth-order splines to create flyable trajectories, ensuring minimal time and respecting kinodynamic constraints. This planner is integrated into a decoupled approach for global motion planning, where a geometrically valid path is first planned, followed by trajectory generation. The chapter also introduces an efficient quasi-metric and an incremental sampling strategy to enhance the performance of the planner. Simulation results demonstrate significant improvements in CPU time and trajectory quality, particularly for constrained problems. Preliminary experiments with a real quad-rotor show the ability to follow planned trajectories, although some tracking errors at high speeds are observed. Future work includes further experimental validation and improvements in localization and control to execute more agile maneuvers in constrained environments.