Characterizing 1-Dof Henneberg-I graphs with efficient configuration spaces

TL;DR

This paper provides combinatorial characterizations of 1-degree-of-freedom Henneberg-I graphs with efficient configuration spaces, enabling automatic sampling of realizations and advancing understanding of underconstrained Euclidean Distance Constraint Systems.

Contribution

It introduces purely combinatorial criteria for identifying graphs with efficient configuration spaces and for choosing parameters to achieve efficiency, extending beyond connected and convex spaces.

Findings

Characterizations of graphs with efficient configuration spaces

An algorithm for sampling realizations efficiently

Extension beyond connected and convex configuration spaces

Abstract

We define and study exact, efficient representations of realization spaces of a natural class of underconstrained 2D Euclidean Distance Constraint Systems(EDCS) or Frameworks based on 1-dof Henneberg-I graphs. Each representation corresponds to a choice of parameters and yields a different parametrized configuration space. Our notion of efficiency is based on the algebraic complexities of sampling the configuration space and of obtaining a realization from the sample (parametrized) configuration. Significantly, we give purely combinatorial characterizations that capture (i) the class of graphs that have efficient configuration spaces and (ii) the possible choices of representation parameters that yield efficient configuration spaces for a given graph. Our results automatically yield an efficient algorithm for sampling realizations, without missing extreme or boundary realizations. In addition, our results formally show that our definition of efficient configuration space is robust and that our characterizations are tight. We choose the class of 1-dof Henneberg-I graphs in order to take the next step in a systematic and graded program of combinatorial characterizations of efficient configuration spaces. In particular, the results presented here are the first characterizations that go beyond graphs that have connected and convex configuration spaces.