Full metadata
Title
The Investigation of Low Cost Computer Vision Application for First Responder Co-robotics
Description
The use of Artificial Intelligence in assistive systems is growing in application and efficiency. From self-driving cars, to medical and surgical robots and industrial tasked unsupervised co-robots; the use of AI and robotics to eliminate human error in high-stress environments and perform automated tasks is something that is advancing society’s status quo. Not only has the understanding of co-robotics exploded in the industrial world, but in research as well. The National Science Foundation (NSF) defines co-robots as the following: “...a robot whose main purpose is to work with people or other robots to accomplish a goal” (NSF, 1). The latest iteration of their National Robotics Initiative, NRI-2.0, focuses on efforts of creating co-robots optimized for ‘scalability, customizability, lowering barriers to entry, and societal impact’(NSF, 1). While many avenues have been explored for the implementation of co-robotics to create more efficient processes and sustainable lifestyles, this project’s focus was on societal impact co-robotics in the field of human safety and well-being. Introducing a co-robotics and computer vision AI solution for first responder assistance would help bring awareness and efficiency to public safety. The use of real-time identification techniques would create a greater range of awareness for first responders in high-stress situations. A combination of environmental features collected through sensors (camera and radar) could be used to identify people and objects within certain environments where visual impairments and obstructions are high (eg. burning buildings, smoke-filled rooms, ect.). Information about situational conditions (environmental readings, locations of other occupants, etc.) could be transmitted to first responders in emergency situations, maximizing situational awareness. This would not only aid first responders in the evaluation of emergency situations, but it would provide useful data for the first responder that would help materialize the most effective course of action for said situation.
Date Created
2020-12
Contributors
- Scott, Kylel D (Author)
- Benjamin, Victor (Thesis director)
- Liu, Xiao (Committee member)
- Engineering Programs (Contributor)
- College of Integrative Sciences and Arts (Contributor)
- Department of Information Systems (Contributor)
- Barrett, The Honors College (Contributor)
Topical Subject
Resource Type
Extent
21 pages
Language
eng
Copyright Statement
In Copyright
Primary Member of
Series
Academic Year 2020-2021
Handle
https://hdl.handle.net/2286/R.I.62627
Level of coding
minimal
Cataloging Standards
System Created
- 2020-12-05 11:11:44
System Modified
- 2021-08-11 04:09:57
- 3 years 2 months ago
Additional Formats