Radiation safety is the biggest concern of the nuclear industry, and co-robots are a crucial component to insuring that safety. Currently, radiation mapping data is typically gathered using hand held detectors or other detection systems requiring constant human interaction. This results in direct exposure to radiation of the individual performing the survey. Co-robots can coordinate computer algorithms and human input to determine the most efficient and accurate methods of surveying these same regions while eliminating health hazards. These surveying methods can then be adapted for multiple uses in the industry including nonproliferation, maintenance, and accident response scenarios. This work describes the process by which two vehicles were modified to detect radiation with minimal human interaction. An algorithm was developed to control the robot and to navigate the area of interest while ensuring that all sources are found. A compact detector system was used to keep the vehicles as small and light as possible. The vehicles were constructed to satisfy the requirements of the detector system and relay the necessary information back to the control station. The process, which is nearly fully autonomous, can map an area of interest and proceed to characterize the radiation materials that are found using neutron and gamma spectroscopy. The vehicles were tested in several scenarios which included obstacles, multiple sources, and shielding of the sources to determine the practicality of these co-robots. The evaluation of these co-robots was critical, as the future of radiation safety lies in the research and construction of small autonomous radiation detection systems to minimize the risk that radiation exposure poses to humans.
| Identifer | oai:union.ndltd.org:GATECH/oai:smartech.gatech.edu:1853/55052 |
| Date | 27 May 2016 |
| Creators | Zavala, Martin |
| Contributors | Erickson, Anna |
| Publisher | Georgia Institute of Technology |
| Source Sets | Georgia Tech Electronic Thesis and Dissertation Archive |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | application/pdf |
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