Description

In this thesis, a new approach to learning-based planning is presented where critical regions of an environment with low probability measure are learned from a given set of motion plans.

In this thesis, a new approach to learning-based planning is presented where critical regions of an environment with low probability measure are learned from a given set of motion plans. Critical regions are learned using convolutional neural networks (CNN) to improve sampling processes for motion planning (MP).

1.73 MB application/pdf

Download count: 0

Details

Contributors
Date Created
  • 2019
Resource Type
  • Text
  • Collections this item is in
    Note
    • Partial requirement for: M.S., Arizona State University, 2019
      Note type
      thesis
    • Includes bibliographical references (pages 32-33)
      Note type
      bibliography
    • Field of study: Computer Science

    Citation and reuse

    Statement of Responsibility

    by Daniel Molina

    Machine-readable links