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Finding Critical Changes in Dynamic Configuration Spaces

This research delves into identifying critical changes in dynamic configuration spaces. Traditional motion planning methods often lack reusability and efficiency, struggling with obstacles that move along known trajectories. We propose a novel approach to detect topological changes based on contact and separation times, utilizing conservative advancement for objects with nonlinear motions. Our results demonstrate a significant improvement in efficiency – producing one order of magnitude faster outcomes while maintaining a comprehensive representation of free configuration space. This leads to more effective dynamic workspace navigation.

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Finding Critical Changes in Dynamic Configuration Spaces

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  1. Finding Critical Changes in Dynamic Configuration Spaces Yanyan Lu and Jyh-Ming Lien George Mason University

  2. Problem Statement Plan motion in dynamic workspace Dynamic obstacle moves along some known trajectory with bounded velocities Image from “Constraint-Based Motion Planning using Voronoi Diagrams” Garber and Lin, WAFR02

  3. Existing Problems • No reusability in • Traditional methods using Configuration-Time space decomposition • Direct application of Probabilistic Roadmap Methods (PRM) or Rapidly-Exploring Random Tree (RRT)

  4. Image from “An incremental learning approach to motion planning with roadmap management”, T-Y Li and Y-C Shie, ICRA ‘02 Existing Problems • More recent methods only repair the invalid portion but at fixed time interval • Fine time resolution results in low efficiency • Low time resolution results in low completeness

  5. Our Work: Detect Topological Changes of Free C-Space time of contact time of separation time of contact time of contact

  6. Our Work: Approximate Topological Changes time of contact time of contact time of separation time of contact

  7. Main Results • Detect topology changes of free space using • time of contact: based on conservative advancement for objects with non-linear motions • time of separation: based on penetration depth • Maintain high reusability between critical changes As a result, • A more complete representation of free CT-space than approaches with fixed time resolution • Significant improvements on efficiency (at least one order of magnitude faster) observed in our experiments

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