Natural Language processing is a growing field and widely used in both industrial and and commercial cases. Though it is difficult to create a natural language system that can robustly react to and handle every situation it is quite possible to design the system to react to specific instruction or scenario. The problem with current natural language systems used in machines, though, is that they are focused on single instructions, working to complete the instruction given then waiting for the next instruction. In this way they are not set to respond to possible conditions that are explained to them.
In the system designed and explained in this thesis, the goal is to fix this problem by introducing a method of adjusting to these conditions. The contributions made in this thesis are to design a set of instruction types that can be used in order to allow for conditional statements within natural language instructions. To create a modular system using ROS in order to allow for more robust communication and integration. Finally, the goal is to also allow for an interconnection between the written text and derived instructions that will make the sentence construction more seamless and natural for the user.
The work in this thesis will be limited in its focus to pertaining to the objective of obstacle traversal. The ideas and methodology, though, can be seen to extend into future work in the area. / Master of Science / With the growth of natural language processing and the development of artificial intelligence, it is important to take a look how to best allow these to work together. The main goal of this project is to find a way of integrating natural language so that it can be used in order to program a robot and in so doing, develop a method of translating that is not only efficient but also easy to understand. We have found we can accomplish this by creating a system that not only creates a direct correlation between the sentence and the instruction generated for the robot to understand, but also one that is able to break down complex sentences and paragraphs into multiple different instructions. This allows for a larger amount of robustness in the system.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/84521 |
| Date | 08 August 2018 |
| Creators | Kakusa, Takondwa Lisungu |
| Contributors | Electrical and Computer Engineering, Hsiao, Michael S., Zeng, Haibo, Patterson, Cameron D. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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