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).
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- Partial requirement for: M.S., Arizona State University, 2019Note typethesis
- Includes bibliographical references (pages 32-33)Note typebibliography
- Field of study: Computer Science