Abstract

Recent events have highlighted the need for unmanned remote sensing in dangerous areas, particularly where structures have collapsed or explosions have occurred, to limit hazards to first responders and increase their efficiency in planning response operations. In the case of the Fukushima nuclear reactor explosion, an unmanned helicopter capable of obtaining overhead images, gathering radiation measurements, and mapping both the structural and radiation content of the environment would have given the response team invaluable data early in the disaster, thereby allowing them to understand the extent of the damage and areas where dangers to personnel existed. With this motivation, the Unmanned Systems Lab at Virginia Tech has developed a remote sensing system for radiation detection and aerial imaging using a 90 kg autonomous helicopter and sensing payloads for the radiation detection and imaging operations. The radiation payload, which is the sensor of focus in this paper, consists of a scintillating type detector with associated software and novel search algorithms to rapidly and effectively map and locate sources of high radiation intensity. By incorporating this sensing technology into an unmanned aerial vehicle system, crucial situational awareness can be gathered about a post-disaster environment and response efforts can be expedited. This paper details the radiation mapping and localization capabilities of this system as well as the testing of the various search algorithms using simulated radiation data. The various components of the system have been flight tested over a several-year period and a new production flight platform has been built to enhance reliability and maintainability. The new system is based on the Aeroscout B1-100 helicopter platform, which has a one-hour flight endurance and uses a COFDM radio system that gives the helicopter an effective range of 7 km.

Highlights

  • Introduction and System OverviewThe Unmanned Systems Laboratory (USL) at Virginia Tech has developed a number of systems to facilitate rapid and safe response to an explosive event, one involving the dispersal of radioactive material

  • The USL has developed a sensing system to fit within the constraints of a small helicopter unmanned aerial vehicles (UAVs) platform and still complete the required mapping and localization tasks

  • When a gamma ray intersects with a scintillator, such as the NaI scintillator in the USL’s radiation detector, one of three types of interaction occur: (1) a photoelectric interaction, where a photoelectron absorbs the energy of the gamma ray and emits a visible-light photon; (2) a Compton interaction, in which only a part of the gamma ray’s energy is absorbed and re-emitted as visible light, and a weaker gamma ray escapes the detector; or (3) a Compton interaction followed by a photoelectric interaction

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Summary

Introduction and System Overview

The Unmanned Systems Laboratory (USL) at Virginia Tech has developed a number of systems to facilitate rapid and safe response to an explosive event, one involving the dispersal of radioactive material. Obtaining situational awareness of the post-event environment requires mapping the radiation distribution of the area and localization sources of high radioactive intensity. Conventional systems for these tasks require large, heavy, expensive equipment that necessitate the use of a full-size helicopter instead of an inexpensive, transportable UAV. In addition to this detector, the sensing system features a stereovision system to generate terrain maps of a region of interest, a DST OTUS-L170. Low-weight, low-cost, expendable system, these personnel can rapidly acquire the information they need to plan effective response operations

Autonomous Radiation Mapping
Radiation Detection System
Radiation Detection Background
Prior Work in Radiation Source Localization
Recursive Bayesian Estimation Method
Localization Algorithm
Grid-Based Localization Results
Radiation Contour Mapping
Algorithm Overview
Contour Detection
Contour Following
Contour Following Results
Source Localization
Contour-Based Localization Results
Conclusions
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