Although radiation monitoring can be performed on foot using handheld or backpack equipment that provides an excellent spatial resolution, the area of nuclear facilities is huge, so such a survey seems unrealistic. The deployment of unmanned vehicles reduces the time of manual monitoring significantly. The advantage of an unmanned survey over a manned survey is its low flight altitude and narrower line spacing, resulting in more effective monitoring, especially in hot spot activities. Moreover, an unmanned survey system can be used in areas that are hazardous to humans. By integrating smart radiation sensors with microprocessor and wireless communication devices and mounting them on any UAV, radiation monitoring in nuclear power plants or any associated activities can be performed remotely with minimal exposure for the radiation worker. Compared to the traditional method of radiation monitoring, flying a drone reduces the operation time significantly, and improves all of the issues of nuclear technologies such as safety, security, and safeguarding.
Since the Fukushima Daiichi accident, many researchers have been interested in drone technology for radiation monitoring. According to Miroslav Pinak, Head of the IAEA Radiation Safety and Monitoring Section, “UAV-based technologies will be crucial for advancing radiation and improving long-term monitoring of contaminated areas” [106]. The IAEA has been working with the Fukushima Prefecture in 2012 in developing and applying drones for radiological monitoring. From 2012 to 2020, the IAEA has assisted Fukushima Prefecture in providing a complete UAV-based instrumentation system for radiation measurements and post-measurement analysis and interpretation methodology under the IAEA Action Plan on Nuclear Safety framework.
The work in [107] quantitatively and qualitatively analyzes UAV-based radiation sensor systems. The authors examined various UAVs, radiation sensors, and radiological survey missions and categorized them by mission. In addition, they proposed a new figure of merit (FOM) formula that explains the mutual effects of parameters of both radiation sensors and UAVs on system performance. The proposed FOM can be used to efficiently assess whether the system achieves the required minimum detectable activity (MDA) without field tests. Based on the identified constraints from the FOM and MDA score, the authors provided several nuclear plant accident scenarios. It was shown through the MDA score that although a larger radiation sensor enhances photoelectron efficiency, it negatively increases the mass effects of UAV endurance when fast flight speed and high flight altitudes are preferred for wide-range monitoring.
In [108], research on radiation contamination mapping was performed using GPS waypoint and mounting a CZT detector on a drone. The drone received the data at the altitude of 2.5 m which was validated by comparing it with a ground survey at the height of 1 m. A similar approach was taken in [109] with different altitudes between 1–10 m height, for which the obtained data is presented in a colour scale heat map. Furthermore, [110] developed a radiation detection and mapping prototype for theoretical nuclear disaster response. A Teviso RD3024 radiation sensor, based on an array of customized PIN, was used to measure Cs-137 and Co-60 using a search algorithm to identify a safe path for a human to travel.
In [111], the researcher integrated a Compton gamma-ray detector with an optical camera on a drone hovering at 1.5 m from a cesium source to visualize the radiation distribution. A stick PC on the drone took 10 min to reconstruct the image before transference to the base PC via Wi-Fi. While the technique adopts the IoT technology, hovering for an extended time for image reconstruction may limit the operation of the drone in one flight.
The authors in [112] developed a remote radiation imaging system comprising a lightweight Compton camera and a 3D LiDAR mounted on a multi-copter drone to remotely measure the distribution of radioactive substances. The Compton camera mounted on the drone has the ability to visualize a 3D distribution of radioactive substances in difficult-to-return zones. The system was tested in the Hama-dori region, Fukushima, Japan, where the drone realized 3D visualization of several hotspots. In [113], a drone equipped with CsI(Tl) and SiPM was used to blindly locate a Cs-137 source in one of three boxes via a comparison of the radiation spectra with background radiation. A similar method was used in [114] to locate a lost source. The experiment was performed using an NaI(Tl) scintillator detector. A I-131 was placed at three different locations before the drone was located using the source location algorithm based on the inverse square law.
For a usability experiment, [115] uses a CZT detector on an octocopter drone in a coaxial configuration with the aim to localize the nuclear radiation source, for which the location is unknown to the drone operators, and can only be configured with the help of a 3DOF haptic device and a 3D augmented reality screen displayed on a computer screen. The evaluation was carried out via the allocation of the NASA-TLX questionnaire and the SPAM (situation present assessment method) to 10 drone operators and evaluating it mental demand, performance, effort, and frustration or stress.
In [116], a drone was used to detect radioactive material and classify the target’s radioactivity in transit. The authors proposed a motion planning framework by integrating visual and inertial localization approaches, in which a navigation function was constructed based on the available knowledge regarding the 3D workspace and the drone dynamics. The navigation function is able to avoid obstacles and generate a safe path to the moving target. The performance of the proposed approach was tested in a simulation environment. However, the framework’s performance needs to be examined in the presence of sensor noise and odometry errors.
The authors in [117] developed and examined a mini-drone gamma-ray spectrometer. The gamma-ray spectrometer has two 103 cm3 BGO scintillation detectors mounted on a hexacopter. The field measurements were obtained at a low flight altitude (5 m to 40 m) and a low flight speed of 1 m/s. The results revealed that the gamma-ray field rapidly decreases with an increasing flight altitude, which highlights the significant impact of flight altitude. It was shown that the flight altitude for mini-airborne surveys could be up to 40 m and may take into account all important conditions including the size and intensity of an assumed anomaly, detector sensitivity, flight speed, and vegetation characteristics. The developed drone is able to detect size-limited radiation anomalies with a comparable quality to a standard airborne survey.
In [118], an advanced gamma radiation detector was developed for UAV operation to exploit drones’ flight and payload capability at under 25 kg. To measure the gamma energy spectra and determine the direction of radioactivity, eight CsI(Tl) crystals were used, which utilised silicon photomultipliers. A small-sized drone with a 6 kg lift capability, and with up to 40 min of endurance, was used for development and measurement. The performance of the developed system was examined in both laboratory and outdoor trials. The results present how the developed system’s directional responses can be used to indicate the source location in real-time and to guide UAV during the survey mission.
The authors in [119] designed an IoT-based device to determine the absorbed dose of gamma and UV radiation. A set of sensors, including a humidity and temperature meter, UV grove radiation meter, Geiger-Muller meter, and height and atmospheric pressure meter were connected to a particle electron microcontroller. Then, the collected data were transferred to a Raspberry Pi for further processing, storage, and transmission to the cloud. The radiation-measuring prototype was mounted on a hexacopter drone. The developed system was compared with meters calibrated in certified laboratories, where the validation results matched those obtained by the other devices, with an error of 2%.
The authors of [120] equipped a quadcopter small-sized drone with a sensitive gamma detector to coordinate the flight based on the measured gamma data and to detect the small dose radiation distribution in a given area. One of the limiting factors faced when attempting to increase flight height is the sensor sensitivity, as a higher sensitivity would measure the highest possible altitude. In [121] a UAV path planning system was proposed for radiation dose mapping for a meter-level resolution. Two algorithms were used for path planning, including: (1) a flood-fill algorithm to plan a path within each chunk of adjacent void areas, and (2) a 2-opt algorithm for path planning between the chunks of void areas. In an affected area near the Fukushima Daiichi Nuclear Power Plant disaster, the field measurement showed that the proposed method is able to successfully minimize the overall flight time compared to the results obtained from the 2-opt only and flood-fill algorithms.
To enhance the safety of personnel during the cleanup process of nuclear facilities, the work in [122] developed a semi-autonomous UAV-based radiation cleanup system that is able to sense radiation contamination remotely, obtain sample contaminations of low-energy byproducts, and perform cleanups. In addition, the authors addressed the issue of quadrotors exerting all the required forces in all six DoF. The developed multirotor drone can exert arbitrary forces and torques, independently and instantaneously, and this improvement can allow UAVs to respond to external disturbances quickly and to maintain their position with precision during the mission.
After a severe accident at the Chernobyl Nuclear Power Plant in 1986, many liquidation materials that had been contaminated by the radioactive fallout were buried in so-called Radioactive Waste Temporary Storage Places (RWTSPs). Until 2020, more than 700 burial sites have been thoroughly investigated, but the location of around 300 burial sites remains unknown. The authors of [123] used sensor technologies such as UAV-based LiDar and multi-spectral imagery and combined the prominent features generated from the high-resolution sensors with a random forest classifier to detect the location of TWTSPs in the Chornobyl exclusion zone.
The authors of [124] used a UAV-based survey system to detect unknown radioactive biomass deposits in Chernobyl’s exclusion zone. UAV-based LiDar data, multi-spectral, and gamma spectrometry data, along with the machine learning methods, were used to precisely map trenches and clamps. Two different UAVs were used for measurements; the LiDar measurements were taken via an octocopter with a flight time of up to 20 min, while multi-spectral measurements were taken via Quantum Trinity VTOL with a flight time of up to 50 min. The measurement rate of the octocopter was 18.5 kHz, the flight altitude was 50 m, and the flight speed was 4–7 m/s. In comparison, the VTOL flight altitude and flight speed were 130 m and 17 m/s, respectively. The measurement results have shown that integrating UAV-based LiDar, multi-spectral image technology, and aerial gamma spectrometry surveys can successfully produce a map for the considered zone, with an overall accuracy of 95.6–99.0%.
UAV-based monitoring systems are considered to be a promising solution for radiation monitoring purposes, whereby the sensors are able to collect data which they analyze onboard, or send to the cloud for storage, instead analyzing the data remotely. These devices can also remotely identify the potential issues within an area and provide notify operators and even perform automated interventions without human interactions. This allows for faster response time, less risk of exposure, lower overall operational cost, and more reliable information compared to static monitoring strategies. However, while UAV-based monitoring systems provide new opportunities they create some critical technical challenges, which will be discussed in detail in the next section.
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