التفاصيل البيبلوغرافية
| العنوان: |
Rapid Robust Radiological Search and Mapping Algorithms |
| المؤلفون: |
Kemp, Samuel |
| المساهمون: |
Rogers, Jonathan, Erickson, Anna S., Hutchinson, Seth, Mazumdar, Anirban, Bakker, Craig, Mechanical Engineering |
| بيانات النشر: |
Georgia Institute of Technology |
| سنة النشر: |
2025 |
| المجموعة: |
Georgia Institute of Technology: SMARTech - Scholarly Materials and Research at Georgia Tech |
| مصطلحات موضوعية: |
Radiation, Particle Filter, Source Term Estimation, Coverage Path Planning, Information Theoretic Path Planning, Mapping, Gaussian Process Regression |
| الوصف: |
Radiological search and mapping are two separate problems that are currently performed by human operators in the field. These tasks could not only be more effective when performed by robotic agents, doing so would also keep human operators from being exposed to gamma radiation. Radiological mapping is the process of taking measurements to build an understanding of the contamination of an area as quickly as possible. This usually implies some degree of coverage for a predefined area. Radiological search is a similar problem that focuses on inferring what the parameters of a source of emissions might be and localizing them as quickly as possible. While a variety of techniques exist for both of these problems, they often have limitations that would prohibit effective and practical deployment. This work has two goals. The first is to improve current mapping methods. This is done by using information driven search with a novel configuration of air and ground robots equipped with counting instruments. The improvements gained are quantified with Monte Carlo simulations. The information driven method will be compared to the same configuration of robots performing random sampling and a configuration of ground robots performing a systematic rectilinear search. A linear reduction in mapping error with time is observed for the systematic search while exponential reductions are observed for the teams using both air and ground robots. The information driven search demonstrates the quickest reduction of mapping error with time. The second goal is to address the search problem with a refined particle filtering algorithm for localizing, identifying, and characterizing point sources of gamma radiation in the presence of obstacles. The proposed algorithm has five major improvements over the current state of the art. Firstly, it uses discrete precomputed attenuation kernels to perform radiation transport thousands of times per second. Secondly, it uses an introspective algorithm to dynamically adjust computational load to balance ... |
| نوع الوثيقة: |
doctoral or postdoctoral thesis |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
https://hdl.handle.net/1853/76763 |
| الاتاحة: |
https://hdl.handle.net/1853/76763 |
| رقم الانضمام: |
edsbas.775B7135 |
| قاعدة البيانات: |
BASE |