STD-Trees: Spatio-temporal Deformable Trees for Multirotors Kinodynamic Planning
This work addresses efficiency in kinodynamic planning for multirotors, an incremental improvement over existing RRT-based methods.
The paper tackles the problem of inefficient convergence in sampling-based kinodynamic planners for multirotors in constrained spaces by proposing STD-Trees, which optimize trajectory trees using spatio-temporal deformation units. The result shows much faster convergence, with numerical experiments indicating it outperforms spatial-only deformation methods.
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.