Path Planning for Simple Robots using Soft Subdivision Search

Authors Ching-Hsiang Hsu, John Paul Ryan, Chee Yap

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Ching-Hsiang Hsu
John Paul Ryan
Chee Yap

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Ching-Hsiang Hsu, John Paul Ryan, and Chee Yap. Path Planning for Simple Robots using Soft Subdivision Search. In 32nd International Symposium on Computational Geometry (SoCG 2016). Leibniz International Proceedings in Informatics (LIPIcs), Volume 51, pp. 68:1-68:5, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2016)


The concept of resolution-exact path planning is a theoretically sound alternative to the standard exact algorithms, and provides much stronger guarantees than probabilistic or sampling algorithms. It opens the way for the introduction of soft predicates in the context of subdivision algorithm. Taking a leaf from the great success of the Probabilistic Road Map (PRM) framework, we formulate an analogous framework for subdivision, called Soft Subdivision Search (SSS). In this video, we illustrate the SSS framework for a trio of simple planar robots: disc, triangle and 2-links. These robots have, respectively, 2, 3 and 4 degrees of freedom. Our 2-link robot can also avoid self-crossing. These algorithms operate in realtime and are relatively easy to implement.
  • Robot Path Planning
  • Soft Predicates
  • Resolution-Exact Algorithm
  • Subdivision Search


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