Toward Optimal Sampling in the Space of Paths

By Colin J. Green, Alonzo James Kelly. 2007

81 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Probabilistic & Bayesian Methods

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Citations in the corpus

Learning Trajectory Preferences for Manipulators via Iterative Improvement (2013, background): “5 Since our primary goal is to learn a score function on sampled set of trajectories we now describe our learning algorithm and for more literature on sampling trajectories we refer the readers to [9].”
A robot path planning framework that learns from experience (2012, methodology): “We seek to create a framework for planning paths in highdimensional spaces that is able to improve with experience, with the aim of reducing computation time.”
Learning preferences for manipulation tasks from online coactive feedback (2015, methodology): “For more details on sampling trajectories we refer interested readers to the work by Erickson and LaValle [16], and Green and Kelly [19].”
Funnel libraries for real-time robust feedback motion planning (2016, unknown): “Further theoretical investigations have focused on the offline generation of diverse but sparse trajectories that ensure the robot’s ability to perform the necessary tasks online in an efficient manner (Green and Kelly, 2007).”
Survivability: Measuring and ensuring path diversity (2009, background): “Choosing samples in configuration space that reduce dispersion (radius of the largest empty ball) is desirable in optimization [11], [14], motion planning [7], and was applied directly to the problem of selecting path sets in [4].”
A unified approach to local trajectory planning and control for autonomous driving along a reference path (2014, background): “The simplicity and easily implementation has resulted in the introduction of a number of control space sampling-based motion planners [6] [7] [8].”