Identifying Critical Regions for Robot Planning Using Convolutional Neural Networks

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 fo...

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Bibliographic Details
Other Authors: Molina, Daniel Antonio (Author)
Format: Dissertation
Language:English
Published: 2019
Subjects:
Artificial intelligence
Robotics
Convolutional neural networks
Deep learning
Learning-based planning
Motion planning
Robot planning
Sampling-based motion planning
Online Access:http://hdl.handle.net/2286/R.I.53626

Internet

http://hdl.handle.net/2286/R.I.53626

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