Simulation Generator: Procedural Generation
Research Mentor(s)
Deneke, Wesley
Description
Collecting data is an enormous undertaking. To ameliorate this workload for those looking to collect data about human workflows, this project aims to synthesize human workflow data using a procedurally generated environment. In order to achieve the procedurally generated environment, a grid system was created. This was then populated by a rules engine and content generation engine working in tandem. The population, as well as global aspects of the environment, (such as the home model) can be determined by means of an authoring tool, which a range of different parameters from the user. The current project features a model kitchen in which cabinets and select appliances are placed procedurally generated, with each executing producing a different layout, statistically speaking. the grid-based system was found to be near essential for efficient object population, as well as enabling the app to be scalable and extensible should another team want to build upon the current work.
Document Type
Event
Start Date
May 2020
End Date
May 2020
Department
Computer Science
Genre/Form
student projects, posters
Type
Image
Rights
Copying of this document in whole or in part is allowable only for scholarly purposes. It is understood, however, that any copying or publication of this document for commercial purposes, or for financial gain, shall not be allowed without the author’s written permission.
Language
English
Format
application/pdf
Simulation Generator: Procedural Generation
Collecting data is an enormous undertaking. To ameliorate this workload for those looking to collect data about human workflows, this project aims to synthesize human workflow data using a procedurally generated environment. In order to achieve the procedurally generated environment, a grid system was created. This was then populated by a rules engine and content generation engine working in tandem. The population, as well as global aspects of the environment, (such as the home model) can be determined by means of an authoring tool, which a range of different parameters from the user. The current project features a model kitchen in which cabinets and select appliances are placed procedurally generated, with each executing producing a different layout, statistically speaking. the grid-based system was found to be near essential for efficient object population, as well as enabling the app to be scalable and extensible should another team want to build upon the current work.