  In this paper, we propose a lightweight yet effective Topology Guided
Kinodynamic planner (TGK-Planner) for quadrotor aggressive flights with limited
onboard computing resources. The proposed system follows the traditional
hierarchical planning workflow, with novel designs to improve the robustness
and efficiency in both the pathfinding and trajectory optimization sub-modules.
Firstly, we propose the topology guided graph, which roughly captures the
topological structure of the environment and guides the state sampling of a
sampling-based kinodynamic planner. In this way, we significantly improve the
efficiency of finding a safe and dynamically feasible trajectory. Then, we
refine the smoothness and continuity of the trajectory in an optimization
framework, which incorporates the homotopy constraint to guarantee the safety
of the trajectory. The optimization program is formulated as a sequence of
quadratic programmings (QPs) and can be iteratively solved in a few
milliseconds. Finally, the proposed system is integrated into a fully
autonomous quadrotor and validated in various simulated and real-world
scenarios. Benchmark comparisons show that our method outperforms
state-of-the-art methods with regard to efficiency and trajectory quality.
Moreover, we will release our code as an open-source package.

Robotics