STD-Trees: Spatio-temporal Deformable Trees for Multirotors Kinodynamic Planning

TL;DR

This paper introduces STD-Trees, a novel method that enhances multirotor kinodynamic planning by locally deforming trajectory trees in space and time, significantly improving convergence speed.

Contribution

It proposes a new spatio-temporal deformation approach for trajectory trees that accelerates convergence in kinodynamic planning for multirotors.

Findings

Spatio-temporal deformation improves convergence speed.

Compatible with various RRT-based methods.

Outperforms spatial-only deformation in experiments.

Abstract

In constrained solution spaces with a huge number of homotopy classes, stand-alone sampling-based kinodynamic planners suffer low efficiency in convergence. Local optimization is integrated to alleviate this problem. In this paper, we propose to thrive the trajectory tree growing by optimizing the tree in the forms of deformation units, and each unit contains one tree node and all the edges connecting it. The deformation proceeds both spatially and temporally by optimizing the node state and edge time durations efficiently. The unit only changes the tree locally yet improves the overall quality of a corresponding sub-tree. Further, variants to deform different tree parts considering the computation burden and optimizing level are studied and compared, all showing much faster convergence. The proposed deformation is compatible with different RRT-based kinodynamic planning methods, and numerical experiments show that integrating the spatio-temporal deformation greatly accelerates the convergence and outperforms the spatial-only deformation.