This work discusses the algorithms and implementation of a multi-robot system for locating radioactive sources. The estimation algorithm presented in this work is able to fuse measurements collected by γ-ray spectrometers carried by an unmanned aerial and unmanned ground vehicle into a single consistent estimate of the probability distribution over the position of a point source in an environment. By constructing a set of hypotheses on the position of the point source, this method converts a non-linear problem into many independent linear ones. Since the underlying model is probabilistic, candidate paths may be evaluated by their expected reduction in uncertainty, allowing the algorithm to select good paths for vehicles to take. An initial hardware test conducted at Savannah River National Laboratory served as a proof of concept and demonstrated that the algorithm successfully locates a radioactive source in the environment, and moves the vehicle to that location. This approach also demonstrated the capability to utilize radiation data collected from an unmanned aerial vehicle to aid the ground vehicle’s exploration. Subsequent numerical experiments characterized the performance of several reward functions and different exploration algorithms in scenarios covering a range of source strengths and region sizes. These experiments demonstrated the improved performance of planning-based algorithms over the myopic method initially tested in the hardware experiments. / Doctor of Philosophy / This work discusses the use of unmanned aerial and ground vehicles to autonomously locate radioactive materials. Using radiation detectors onboard each vehicle, they are commanded to search the environment using a method that incorporates measurements as they are collected. A mathematical model allows measurements taken from different vehicles in different positions to be combined together. This approach decreases the time required to locate sources by using previously collected measurements to improve the quality of later measurements. This approach also provides a best estimate of the location of a source as data is collected. This algorithm was tested in an experiment conducted at Savannah River National Laboratory. Further numerical experiments were conducted testing different reward functions and exploration algorithms.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/97954 |
| Date | 01 May 2020 |
| Creators | Peterson, John Ryan |
| Contributors | Mechanical Engineering, Kochersberger, Kevin B., Pierson, Mark Alan, Wicks, Alfred L., Tokekar, Pratap |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Detected Language | English |
| Type | Dissertation |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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