Mobile Robotic Radiation Surveying Using Recursive Bayesian Estimation. Anderson, B., Pryor, M., & Landsberger, S. In 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), pages 1187–1192, August, 2019. ISSN: 2161-8089
doi  abstract   bibtex   
Nuclear facilities require wide-area surveys and remote response to the detection of abnormal radiation levels. These typically require a large number of measurement locations using fixed search patterns. Such approaches are time-consuming, require extended radiation exposure, and are difficult to routinely replicate by technicians. This paper presents an automated method of detecting and locating single or multiple small gamma-ray sources in an unstructured environment, requiring significantly fewer measurements than traditional methods and without a need for post-processing. A mobile robot can collect higher-precision data than practically possible by a human and removes the technician from the radiation area. This is enabled by addressing complexities that previously made automation difficult including supervisory control, obstacle avoidance, sensor positioning over a large height range, recognizing environmental complexities (shielding, etc and modifying survey parameters based on aberrant readings. The developed solution uses a mobile platform with a height-adjustable (up to 2.44 meters) radiation detector. Recursive Bayesian Estimation (RBE) is used to update a probability distribution of the location and intensity of source(s) after each measurement. The likelihood function is determined using radiation transport and detector models. Isotopic identification via a gamma library search aids data analysis by distinguishing counts from different sources. Computation considerations are discussed including predicting and localizing multiple sources.
@inproceedings{anderson_mobile_2019,
	title = {Mobile {Robotic} {Radiation} {Surveying} {Using} {Recursive} {Bayesian} {Estimation}},
	doi = {http://doi.org/10.1109/COASE.2019.8843064},
	abstract = {Nuclear facilities require wide-area surveys and remote response to the detection of abnormal radiation levels. These typically require a large number of measurement locations using fixed search patterns. Such approaches are time-consuming, require extended radiation exposure, and are difficult to routinely replicate by technicians. This paper presents an automated method of detecting and locating single or multiple small gamma-ray sources in an unstructured environment, requiring significantly fewer measurements than traditional methods and without a need for post-processing. A mobile robot can collect higher-precision data than practically possible by a human and removes the technician from the radiation area. This is enabled by addressing complexities that previously made automation difficult including supervisory control, obstacle avoidance, sensor positioning over a large height range, recognizing environmental complexities (shielding, etc and modifying survey parameters based on aberrant readings. The developed solution uses a mobile platform with a height-adjustable (up to 2.44 meters) radiation detector. Recursive Bayesian Estimation (RBE) is used to update a probability distribution of the location and intensity of source(s) after each measurement. The likelihood function is determined using radiation transport and detector models. Isotopic identification via a gamma library search aids data analysis by distinguishing counts from different sources. Computation considerations are discussed including predicting and localizing multiple sources.},
	booktitle = {2019 {IEEE} 15th {International} {Conference} on {Automation} {Science} and {Engineering} ({CASE})},
	author = {Anderson, Blake and Pryor, Mitch and Landsberger, Sheldon},
	month = aug,
	year = {2019},
	note = {ISSN: 2161-8089},
	keywords = {Bayes methods, Detectors, Estimation, Hardware, Probability and Statistical Methods, Robot sensing systems, Robotics in Hazardous Fields, Uncertainty, abnormal radiation levels, automated method, collision avoidance, data analysis, detector models, environmental complexities, fixed search patterns, gamma library search, gamma-ray sources, height range, height-adjustable, higher-precision data, measurement locations, mobile platform, mobile robot, mobile robotic radiation surveying, mobile robots, nuclear facilities, probability, radiation area, radiation detection, radiation exposure, radiation transport, recursive Bayesian Estimation, recursive Bayesian estimation, remote response, search problems, supervisory control, survey parameters, wide-area surveys},
	pages = {1187--1192},
}

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