Integrating Fast Regional Optimization into Sampling-based Kinodynamic Planning for Multirotor Flight
This work addresses real-time motion planning for multirotor drones, offering incremental improvements in efficiency for specific domain applications.
The paper tackles the problem of inefficient sampling-based kinodynamic motion planning for multirotors in challenging environments like narrow passages, by integrating a fast regional optimizer into global sampling, resulting in significantly improved success rates and reduced planning time.
For real-time multirotor kinodynamic motion planning, the efficiency of sampling-based methods is usually hindered by difficult-to-sample homotopy classes like narrow passages. In this paper, we address this issue by a hybrid scheme. We firstly propose a fast regional optimizer exploiting the information of local environments and then integrate it into a global sampling process to ensure faster convergence. The incorporation of local optimization on different sampling-based methods shows significantly improved success rates and less planning time in various types of challenging environments. We also present a refinement module that fully investigates the resulting trajectory of the global sampling and greatly improves its smoothness with negligible computation effort. Benchmark results illustrate that compared to the state-of-the-art ones, our proposed method can better exploit a previous trajectory. The planning methods are applied to generate trajectories for a simulated quadrotor system, and its capability is validated in real-time applications.