By Topic

Adaptive node sampling method for probabilistic roadmap planners

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Byungjae Park ; Robot. Lab., Pohang Univ. of Sci. & Technol. (POSTECH), Pohang, South Korea ; Wan Kyun Chung

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.

Published in:

Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on

Date of Conference:

10-15 Oct. 2009