Hierarchical Annotated Skeleton-Guided Tree-based Motion Planning

Abstract-- We present a hierarchical tree-based motion planning strategy, HAS-RRT, guided by the workspace skeleton to solve motion planning problems in robotics and computational biology. Relying on the information about the connectivity of the workspace and the ranking of available paths in the workspace, the strategy prioritizes paths indicated by the workspace guidance to find a valid motion plan for the moving object efficiently. We offer an extensive comparative analysis against other (a) Basic RRT [4] (b) DR-RRT [5] tree-based planning strategies and demonstrate that HAS-RRT reliably and efficiently finds low-cost paths. In contrast to methods prone to inconsistent performance across different environments or reliance on specific parameters, HAS-RRT is robust to workspace variability. Motion planning algorithms have applications in many fields, from robotics [1] to microbiology [2].