Use of Multi-Rotor Unmanned Aerial Vehicles for Radioactive Source Search

Design of the sparrow search algorithm (SSA) for airborne radioactive hotspot detection

In the context of using aircraft as a pivotal tool for detecting radioactive hotspots, the acquisition of radioactivity data was conducted through a CeBr3 scintillation crystal detector mounted on a helicopter. However, challenges arose, including managing extensive data volumes, computationally demanding tasks, and susceptibility to local optima issues. To address these challenges and leverage the benefits of the Sparrow Search Algorithm (SSA) in global optimization and convergence speed, an improved SSA was devised. This improved version integrated SSA principles with the intricacies of searching for radioactive hotspots. The algorithm employed a matrix segmentation method to process data matrices derived from measured data, aiming to enhance efficiency and accuracy. An empirical analysis was conducted, performing 100 iterations on an experimental matrix to scrutinize the impact of matrix segmentation. Computation times and results were compared across different segmentation levels, confirming the favorable algorithmic outcomes of the method. The practical viability and convergence stability of the algorithm were further assessed using genuine measured data, with segmented matrices generated for evaluation. Remarkably, a comparison between computational outcomes and manually identified data reaffirmed the algorithm's reliability in effectively detecting radioactive hotspots.

CARMA II: A ground vehicle for autonomous surveying of alpha, beta and gamma radiation

Surveying active nuclear facilities for spread of alpha and beta contamination is currently performed by human operators. However, a skills gap of qualified workers is emerging and is set to worsen in the near future due to under recruitment, retirement and increased demand. This paper presents an autonomous ground vehicle that can survey nuclear facilities for alpha, beta and gamma radiation and generate radiation heatmaps. New methods for preventing the robot from spreading radioactive contamination using a state-machine and radiation costmaps are introduced. This is the first robot that can detect alpha and beta contamination and autonomously re-plan around the contamination without the wheels passing over the contaminated area. Radiation avoidance functionality is proven experimentally to reduce alpha and beta contamination spread as well as gamma radiation dose to the robot. The robot’s survey area is defined using a custom designed, graphically controlled area coverage planner. It was concluded that the robot is highly suited to certain monotonous room scale radiation surveying tasks and therefore provides the opportunity for financial savings, to mitigate a future skills gap, and provision of radiation surveys that are more granular, accurate and repeatable than those currently performed by human operators.

MCNPX simulation and experimental validation of an unmanned aerial radiological system (UARS) for rapid qualitative identification of weak hotspots

Nuclear threats such as dirty bombs and illicit trafficking of radioactive sources are major concerns of humanity. Fast detection and accurate localization of radioactive material out of regulatory control (MORC) by autonomous and semi-autonomous monitoring systems like robots can help to reduce radiation exposure to the public and workers, and it will improve security and peace in the world. This study proposes an autonomous radiological monitoring system consisting of a 2-inch NaI detector coupled to a PM tube and mounted on a multi-rotor UAV to detect radioactive sources. First, an experimental scenario was modeled using the MCNPX Monte Carlo (MC) code. In this modeling, the gamma spectra in 15 detectors were recorded from the rays emitted simultaneously from the areas' sources. The total count under the spectrum was measured for each of the detectors at different heights. The experimental tests were also performed to detect the simultaneous effect of five low-level Co-60 and Cs-137 point sources on a soccer field. Next, the modeling results were compared with the experimental ones, which showed good agreement and the capability to use MC modeling to simulate different radiological scenarios. The experimental results also showed that at 50 cm, all radioactive sources were successfully detected in their actual location. By decreasing the flight height, the ability of the monitoring unmanned aerial to detect radioactive sources was increased significantly.

Evaluating cost and benefit of air pollution control policies in China: A systematic review

China has put great efforts into air pollution control over the past years and recently committed to its most ambitious climate target. Cost and benefit analysis has been widely used to evaluate the control policies in terms of past performance, future reduction potential, and direct and indirect impacts. To understand the cost and benefit analysis for air pollution control in China, we conducted a bibliometric review of more than 100 studies published over the past two decades, including the current research progress, most commonly adopted methods, and core findings. The control target in cost and benefit analysis has shifted in three stages, from individual and primary pollution control, moving to joint prevention of multiple and secondary pollutants, and then towards synergistic control of air pollution and carbon. With the expansion of the research scope, the integrated assessment model has gradually demonstrated the necessity for long-term ex-anti policy simulation, especially for dealing with complex factors. To ensure long-term air quality, climate, public health, and sustainable economic development, substantial evidence from published studies has suggested that China needs to continue its efforts in the upstream adjustment of the energy system and industrial structure with multi-regional and -sector collaboration. This cost and benefit review paper provides decision-makers with the fundamental information and knowledge gaps in air pollution control strategies in China, and direction for facing future challenges.

Neutron and Gamma-Ray Detection System Coupled to a Multirotor for Screening of Shipping Container Cargo

In order to detect special nuclear materials and other radioactive materials in Security and Defense scenarios, normally, a combination of neutron and gamma-ray detection systems is used. In particular, to avoid illicit traffic of special nuclear materials and radioactive sources/materials, radiation portal monitors are placed at seaports to inspect shipping-container cargo. Despite their large volume (high efficiency), these detection systems are expensive, and therefore only a fraction of these containers are inspected. In this work, a novel mobile radiation detection system is presented, based on an EJ-200 plastic scintillator for the detection of gamma rays and beta particles, and a neutron detector EJ-426HD plastic scintillator (with ⁶Li) embedded in a compact and modular moderator. The use of silicon photomultipliers in both detectors presented advantages such as lightweight, compactness, and low power consumption. The developed detection system was integrated in a highly maneuverable multirotor. Monte Carlo simulations were validated by laboratory measurements and field tests were performed using real gamma-ray and neutron sources. The detection and localization within one meter was achieved using a maximum likelihood estimation algorithm for ¹³⁷Cs sources (4 MBq), as well as the detection of ²⁴¹Am-beryllium (1.45 GBq) source placed inside the shipping container.

MODEL CALIBRATION OF AN AIRBORNE GAMMA SPECTROMETER FOR SEARCHING OF RADIONUCLIDE SOURCES USING UAV

A study of a small airborne gamma spectrometer usability, carried by unmanned aerial vehicle (UAV), during a search for radionuclide sources and estimating their activity was performed. Model calculations of calibration coefficients for Georadis D230A spectrometer (with two NaI (Tl) 2″ x 2″ detectors), 241Am, 57Co, 137Cs, 192Ir, 60Co and 152Eu radionuclides and set of flight altitudes from 1 up to 25 m were done. The results were compared with the results of experimental measurements for 137Cs and 60Co sources using vertical heights up to 10 m above the source on the ground. Simulated and discussed is the possible significant influence of the source construction and housing and the position/orientation of the source (due to non-isotropic emission) to estimate the activity of an unknown source. The influence of the natural background and estimation of flight line spacing requirements when planning a UAV route to ensure source localization depending on its activity are discussed.

Enhancing the sensitivity for weak radioactive source detection

Considering the difficulties of the low signal-to-noise ratio in weak radioactive source detections, this study proposes an abandon Gaussian tails method based on the analysis of the characteristic information denoted by the full-energy peak of the gamma spectrum of a gamma-emitting radioactive source. Based on the study of the signal-to-background ratio and the statistical fluctuations in the signal of the weak radioactive source, a factor ζ, incorporating the statistical fluctuations of signal and background and the signal-to-background ratio, is suggested to characterize the sensitivity of a radioactive source detection. When ζ reaches its maximum value, the optimal counting window around the centroid of the full-energy peak can be obtained. To evaluate the effectiveness of the proposed approach, comparisons between the proposed abandon Gaussian tails, the conventional full-energy counting, and other experiential methods were performed. The results show that the sensitivity can be significantly improved. Further, experiments with different intensity of radiation sources and duplicated experiments were conducted to examine the stability of the proposed method.

Boost Precision Agriculture with Unmanned Aerial Vehicle Remote Sensing and Edge Intelligence: A Survey

In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-effective technology to capture high spatial and temporal resolution remote sensing (RS) images for a wide range of precision agriculture applications, which can help reduce costs and environmental impacts by providing detailed agricultural information to optimize field practices. Furthermore, deep learning (DL) has been successfully applied in agricultural applications such as weed detection, crop pest and disease detection, etc. as an intelligent tool. However, most DL-based methods place high computation, memory and network demands on resources. Cloud computing can increase processing efficiency with high scalability and low cost, but results in high latency and great pressure on the network bandwidth. The emerging of edge intelligence, although still in the early stages, provides a promising solution for artificial intelligence (AI) applications on intelligent edge devices at the edge of the network close to data sources. These devices are with built-in processors enabling onboard analytics or AI (e.g., UAVs and Internet of Things gateways). Therefore, in this paper, a comprehensive survey on the latest developments of precision agriculture with UAV RS and edge intelligence is conducted for the first time. The major insights observed are as follows: (a) in terms of UAV systems, small or light, fixed-wing or industrial rotor-wing UAVs are widely used in precision agriculture; (b) sensors on UAVs can provide multi-source datasets, and there are only a few public UAV dataset for intelligent precision agriculture, mainly from RGB sensors and a few from multispectral and hyperspectral sensors; (c) DL-based UAV RS methods can be categorized into classification, object detection and segmentation tasks, and convolutional neural network and recurrent neural network are the mostly common used network architectures; (d) cloud computing is a common solution to UAV RS data processing, while edge computing brings the computing close to data sources; (e) edge intelligence is the convergence of artificial intelligence and edge computing, in which model compression especially parameter pruning and quantization is the most important and widely used technique at present, and typical edge resources include central processing units, graphics processing units and field programmable gate arrays.

Conceptual Design and Multi-Disciplinary Computational Investigations of Multirotor Unmanned Aerial Vehicle for Environmental Applications

This study focuses on establishing a conceptual design for a multirotor unmanned aerial vehicle (UAV). The objectives of this octocopter are to reduce the number of flight cancelations and car accidents owing to low-visibility issues and to improve abnormal environmental conditions due to the presence of smoke. The proposed octocopter contains a convergent–divergent [CD] duct-based storage tank, which provides a platform to store saltwater and allows it to fly in foggy zones. Fine saltwater is sprayed from the octocopter and dispersed into the low clouds, thereby altering the vapor’s microphysical processes to break it up and improve visibility. The nature of the seawater and its enhanced fluid properties, due to the involvement of octocopter, creates the fluid flow mixing between atmospheric fluids and spraying particles, which increases the settling of foggy and smokey content groundward. For deployment, the conceptual design of the octocopter was initially constructed through analytical approaches. Additionally, three unique historical relationships were created. The standard engineering approaches involved in this work were stability analysis through MATLAB and fluid-property analysis through computational fluid dynamics (CFD) cum multiple reference frame (MRF) tools. The systematic model of this octocopter was developed by CATIA, and thereafter CFD and fluid–structure-interaction (FSI) analyses were computed, in ANSYS Workbench, on the octocopter for various environmental conditions. The aerodynamic forces on the drone, the enhancement of dynamic pressure by the presence of high amounts of rotors and nozzle sprayer, suitable material to resist aerodynamic loadings, and tests on the efficiency of the controller and its electronic components were investigated in detail. Finally, the proposed octocopter-based dynamic system was conceptually constructed.

Robotic Exploration of an Unknown Nuclear Environment Using Radiation Informed Autonomous Navigation

This paper describes a novel autonomous ground vehicle that is designed for exploring unknown environments which contain sources of ionising radiation, such as might be found in a nuclear disaster site or a legacy nuclear facility. While exploring the environment, it is important that the robot avoids radiation hot spots to minimise breakdowns. Broken down robots present a real problem: they not only cause the mission to fail but they can block access routes for future missions. Until now, such robots have had no autonomous gamma radiation avoidance capabilities. New software algorithms are presented that allow radiation measurements to be converted into a format in which they can be integrated into the robot’s navigation system so that it can actively avoid receiving a high radiation dose during a mission. An unmanned ground vehicle was fitted with a gamma radiation detector and an autonomous navigation package that included the new radiation avoidance software. The full system was evaluated experimentally in a complex semi-structured environment that contained two radiation sources. In the experiment, the robot successfully identified both sources and avoided areas that were found to have high levels of radiation while navigating between user defined waypoints. This advancement in the state-of-the-art has the potential to deliver real benefit to the nuclear industry, in terms of both increased chance of mission success and reduction of the reliance on human operatives to perform tasks in dangerous radiation environments.

State-of-the-Art Mobile Radiation Detection Systems for Different Scenarios

In the last decade, the development of more compact and lightweight radiation detection systems led to their application in handheld and small unmanned systems, particularly air-based platforms. Examples of improvements are: the use of silicon photomultiplier-based scintillators, new scintillating crystals, compact dual-mode detectors (gamma/neutron), data fusion, mobile sensor networks, cooperative detection and search. Gamma cameras and dual-particle cameras are increasingly being used for source location. This study reviews and discusses the research advancements in the field of gamma-ray and neutron measurements using mobile radiation detection systems since the Fukushima nuclear accident. Four scenarios are considered: radiological and nuclear accidents and emergencies; illicit traffic of special nuclear materials and radioactive materials; nuclear, accelerator, targets, and irradiation facilities; and naturally occurring radioactive materials monitoring-related activities. The work presented in this paper aims to: compile and review information on the radiation detection systems, contextual sensors and platforms used for each scenario; assess their advantages and limitations, looking prospectively to new research and challenges in the field; and support the decision making of national radioprotection agencies and response teams in respect to adequate detection system for each scenario. For that, an extensive literature review was conducted.

Design and assembly of an IoT-based device to determine the absorbed dose of gamma and UV radiation

Ionizing and non-ionizing radiations are part of our daily life, and when organisms are exposed to them for a long time, they may experience their lethal or sublethal effects. For this reason, technologies have been created to quantify them. In this study, Internet of Things (IoT) was used through connecting gamma meters and a low-cost UV radiation device. The validation of this structure was performed with meters calibrated in certified laboratories. The validation results matched those obtained by the other devices, with an error of 2%.

Autonomous Search of Radioactive Sources through Mobile Robots

The research of robotic autonomous radioactivity detection or radioactive source search plays an important role in the monitoring and disposal of nuclear safety and biological safety. In this paper, a method for autonomously searching for radioactive sources through mobile robots was proposed. In the method, by using a partially observable Markov decision process (POMDP), the search of autonomous unknown radioactive sources was realized according to a series of radiation information measured by mobile robot. First, the factors affecting the accuracy of radiation measurement during the robot’s movement were analyzed. Based on these factors, the behavior set of POMDP was designed. Secondly, the parameters of the radioactive source were estimated in the Bayesian framework. In addition, through the reward strategy, autonomous navigation of the robot to the position of the radiation source was achieved. The search algorithm was simulated and tested, and the TurtleBot robot platform was used to conduct a real search experiment on the radio source Cs-137 with an activity of 37 MBq indoors. The experimental results showed the effectiveness of the method. Additionally, from the experiments, it could been seen that the robot was affected by the linear velocity, angular velocity, positioning accuracy and the number of measurements in the process of autonomous search for the radioactive source. The proposed mobile robot autonomous search method can be applied to the search for lost radioactive sources, as well as for the leakage of substances (nuclear or chemical) in nuclear power plants and chemical plants.