Integrating Fast Regional Optimization into Sampling-based Kinodynamic Planning for Multirotor Flight

TL;DR

This paper introduces a hybrid kinodynamic motion planning approach for multirotors that combines fast local optimization with global sampling, significantly improving success rates and efficiency in challenging environments.

Contribution

A novel hybrid scheme integrating local optimization into sampling-based kinodynamic planning for multirotors, enhancing success rates and reducing planning time.

Findings

Improved success rates in narrow passages and complex environments.

Faster convergence compared to state-of-the-art methods.

Enhanced trajectory smoothness with minimal computation.

Abstract

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.