Efficient Sampling-based Kinodynamic Planning with Regional Optimization and Bidirectional Search

Efficient Sampling-based Kinodynamic Planning with Regional Optimization and Bidirectional Search for Multirotors
Video for the RAL submission.
preprint:

For real-time multirotor kinodynamic 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 bidirectional global sampling process. 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 improves its smoothness with nearly 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 quadrotor system in both simulation and real-world, and its capability is validated in real-time applications.