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Identifying Critical Regions for Robot Planning Using Convolutional Neural Networks

abstract: 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).

In addition to an identification network, a new sampling-based motion planner, Learn and Link, is introduced. This planner leverages critical regions to overcome the limitations of uniform sampling while still maintaining guarantees of correctness inherent to sampling-based algorithms. Learn and Link is evaluated against planners from the Open Motion Planning Library (OMPL) on an extensive suite of challenging navigation planning problems. This work shows that critical areas of an environment are learnable, and can be used by Learn and Link to solve MP problems with far less planning time than existing sampling-based planners. / Dissertation/Thesis / Masters Thesis Computer Science 2019

Identiferoai:union.ndltd.org:asu.edu/item:53626
Date January 2019
ContributorsMolina, Daniel Antonio (Author), Srivastava, Siddharth (Advisor), Li, Baoxin (Committee member), Zhang, Yu (Committee member), Arizona State University (Publisher)
Source SetsArizona State University
LanguageEnglish
Detected LanguageEnglish
TypeMasters Thesis
Format45 pages
Rightshttp://rightsstatements.org/vocab/InC/1.0/

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