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Assessing Human Spatial Navigation in a Virtual Space and its Sensitivity to Exercise
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Rapidly-Exploring Roadmaps: Weighing Exploration vs. Refinement in Optimal Motion Planning.

Ron Alterovitz1, Sachin Patil, Anna Derbakova

  • 1Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA {ron,sachin,anya}@cs.unc.edu.

IEEE International Conference on Robotics and Automation : ICRA : [Proceedings]. IEEE International Conference on Robotics and Automation
|February 2, 2012
PubMed
Summary

The new rapidly-exploring roadmap (RRM) method balances exploring unknown areas and refining known paths for optimal motion planning. This approach enhances pathfinding efficiency in complex robotic applications.

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Area of Science:

  • Robotics
  • Artificial Intelligence
  • Computational Geometry

Background:

  • Optimal motion planning is crucial for robotics, requiring efficient exploration of configuration spaces.
  • Existing methods often struggle to balance exploration and refinement, leading to suboptimal paths or slow convergence.

Purpose of the Study:

  • To introduce the rapidly-exploring roadmap (RRM) method for single-query optimal motion planning.
  • To enable users to control the trade-off between exploring new regions and refining existing paths.

Main Methods:

  • RRM initially explores the configuration space similar to rapidly-exploring random trees (RRT).
  • After finding an initial path, RRM uses a user-defined parameter to balance further exploration versus refining the roadmap by adding edges.
  • This allows for dynamic adjustment between discovering new space and improving path quality.

Main Results:

  • Demonstrated RRM's effectiveness in optimizing motion plans by balancing exploration and refinement.
  • Showcased the method's performance on a point robot in a 2D plane and a concentric tube robot for medical procedures.
  • Highlighted the explicit control over the exploration-refinement trade-off.

Conclusions:

  • RRM offers a novel approach to single-query optimal motion planning by managing the exploration-refinement balance.
  • The method provides flexibility for applications requiring different priorities between path discovery and quality.
  • RRM shows promise for complex robotic systems, including those in minimally invasive medical procedures.