TGK-Planner: An Efficient Topology Guided Kinodynamic Planner for Autonomous Quadrotors
This addresses the challenge of efficient autonomous navigation for quadrotors in complex environments, though it appears incremental as it builds on traditional hierarchical planning with novel enhancements.
The paper tackles the problem of enabling aggressive quadrotor flights with limited computing resources by proposing TGK-Planner, which improves efficiency and trajectory quality in pathfinding and optimization, outperforming state-of-the-art methods in benchmarks.
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.