Adaptive node sampling method for probabilistic roadmap planners

Authors:
Byungjae Park;Wan Kyun Chung
Affiliations:
Robotics Lab., Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea;Robotics Lab., Mechanical Engineering, Pohang University of Science and Technology, Pohang, Korea
Venue:
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Year:
2009

Citing 3
Cited 0

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The Approximate Cell Decomposition with Local Node Refinement Global Path Planning Method: Path Nodes Refinement and Curve Parametric Interpolation

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Probabilistic methods for centroidal Voronoi tessellations and their parallel implementations

Parallel Computing

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Abstract

This paper proposes an adaptive node sampling method for the probabilistic roadmap (PRM) planner. The proposed method substitutes the random sampling in the learning phase of the PRM planner and improves the configuration of the roadmap. This method uses two phase to determine nodes in order to construct the roadmap. First, the proposed method extracts initial nodes using the approximated cell decomposition and the Harris corner detector. Second, the positions of these nodes are optimized using a construction process of the centroidal voronoi tessellation (CVT). The proposed method determines the adequate number and positions of the nodes to represent the entire free space, and the PRM planner based on the proposed method finds out efficient paths even in narrow passages. These properties have been verified though experiments.