Radiative Contour Mapping Using UAS Swarm

The work is related to the simulation and design of small and medium scale unmanned aerial system (UAS), and its implementation for radiation measurement and contour mapping with onboard radiation sensors. The compact high-resolution CZT sensors were integrated to UAS platforms as the plug-and-play components using Robot Operation System. The onboard data analysis provides time and position-stamped intensities of gamma-ray peaks for each sensor that are used as the input data for the swarm flight control algorithm. In this work, a UAS swarm is implemented for radiation measurement and contour mapping. The swarm of UAS has advantages over a single agent based approach in detecting radiative sources and effectively mapping the area. The proposed method can locate sources of radiation as well as mapping the contaminated area for enhancing situation awareness capabilities for first responders. This approach uses simultaneous radiation measurements by multiple UAS flying in a circular formation to find the steepest gradient of radiation to determine a bulk heading angle for the swarm for contour mapping, which can provide a relatively precise boundary of safety for potential human exploration

Haptic Teleoperation of UAV Equipped with Gamma-Ray Spectrometer for Detection and Identification of Radio-Active Materials in Industrial Plants

Large scale factories such as steel, wood, construction, recycling plants and landfills involve the procurement of raw material which may include radiating parts, that must be monitored, because potentially dangerous for workers. Manufacturing operations are carried out in unstructured environments, where fully autonomous unmanned aerial vehicle (UAV) inspection is hardly applicable. In this work we report on the development of a haptic teleoperated UAV for localization of radiation sources in industrial plants. Radiation sources can be localized and identified thanks to a novel CZT-based custom gamma-ray detector integrated on the UAV, providing light, compact, spectroscopic, and low power operation. UAV operation with a human in the loop allows an expert operator to focus on selected candidate areas, thereby optimizing short flight mission in face of the constrained acquisition times required by nuclear inspection. To cope with the reduced situational awareness of the remote operator, force feedback is exploited as an additional sensory channel. The developed prototype has been demonstrated both in relevant and operational environments

A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors

Understanding atmospheric transport and dispersal events has an important role in a range of scenarios. Of particular importance is aiding in emergency response after an intentional or accidental chemical, biological or radiological (CBR) release. In the event of a CBR release, it is desirable to know the current and future spatial extent of the contaminant as well as its location in order to aid decision makers in emergency response. Many dispersion phenomena may be opaque or clear, thus monitoring them using visual methods will be difficult or impossible. In these scenarios, relevant concentration sensors are required to detect the substance where they can form a static network on the ground or be placed upon mobile platforms. This paper presents a review of techniques used to gain information about atmospheric dispersion events using static or mobile sensors. The review is concluded with a discussion on the current limitations of the state of the art and recommendations for future research

Autonomous Planning and Mapping for the Characterization of Gamma Contaminated Environments

The past $100$ years of research and development in the fields of nuclear power, weapons, and industrial radiation applications have imbibed regions across the world with facilities and terrain which is contaminated with radioactive material. Such locations can pose significant hazards to human health, thus requiring vigilant monitoring and mitigation efforts. The use of autonomous robots is well suited to this task. Motivated by this fact, this work contributes a holistic perspective on the deployment, design, and use of autonomous robots for the characterization of radioactively contaminated environments. The set of developments presented in this dissertation incorporate principles of gamma radiation detection and measurement, techniques for mapping and localizing a variety of radioactive sources, path planning strategies tailored to both ground and aerial platforms, as well as prototype systems implementing methods for perception and navigation in dirty, dangerous, and degraded conditions. Specifically, Chapter \ref{chap:intro} presents the motivation behind this work, including its practical application, as well as a brief description of the approach utilized to accomplish environmental radiation characterization. Chapter \ref{chap:contrib} presents a detailed overview of the presented radiation mapping contributions and associated publications in addition to a brief note on other synergistic contributions made towards enabling autonomy in the perceptually degraded environments associated in particular with waste decommissioning facilities. Subsequently the core contributions of this thesis are presented in detail. Chapter \ref{chap:single_source} presents a method for autonomous single source localization using an aerial robot, alongside details regarding principles of radiation measurement and detection. Chapter \ref{chap:radbot} describes a technique developed to map distributed radiation fields in 2D using a ground platform, while Chapter \ref{chap:radmf} extends the work to perform the mapping task in 3D using a collision tolerant micro aerial vehicle. Subsequently, Chapter \ref{chap:auro} presents autonomous distributed 3D radiation mapping coupled with an intelligent path planning algorithm tailored to source seeking behaviors in confined environments. Finally, conclusions and an outlook for future research are discussed in Chapter \ref{chap:conclusions}.Overall, this dissertation contributes a body of work enabling autonomous radiological surveying in challenging conditions, demonstrating robust functionality through a series of field experiments using real radiation sources. Each of the presented methods is associated with a tested and reliable robotic system purpose-built for its designated task. This combination of performance robotic hardware demonstrating novel autonomous functionality in realistic use-case scenarios showcases the applicability and dependability of the presented systems and methods

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Department of Mehcanical EngineeringUnmanned aerial vehicles (UAVs) are widely used in various areas such as exploration, transportation and rescue activity due to light weight, low cost, high mobility and intelligence. This intelligent system consists of highly integrated and embedded systems along with a microprocessor to perform specific task by computing algorithm or processing data. In particular, image processing is one of main core technologies to handle important tasks such as target tracking, positioning, visual servoing using visual system. However, it often requires heavy amount of computation burden and an additional micro PC controller with a flight computer should be additionally used to process image data. However, performance of the controller is not so good enough due to limited power, size, and weight. Therefore, efficient image processing techniques are needed considering computing load and hardware resources for real time operation on embedded systems. The objective of the thesis research is to develop an efficient image processing framework on embedded systems utilizing neural network and various optimized computation techniques to satisfy both efficient computing speed versus resource usage and accuracy. Image processing techniques has been proposed and tested for management computing resources and operating high performance missions in embedded systems. Graphic processing units (GPUs) available in the market can be used for parallel computing to accelerate computing speed. Multiple cores within central processing units (CPUs) are used like multi-threading during data uploading and downloading between the CPU and the GPU. In order to minimize computing load, several methods have been proposed. The first method is visualization of convolutional neural network (CNN) that can perform both localization and detection simultaneously. The second is region proposal for input area of CNN through simple image processing, which helps algorithm to avoid full frame processing. Finally, surplus computing resources can be saved by control the transient performance such as the FPS limitation. These optimization methods have been experimentally applied to a ground vehicle and quadrotor UAVs and verified that the developed methods offer an optimization to process in embedded environment by saving CPU and memory resources. In addition, they can support to perform various tasks such as object detection and path planning, obstacle avoidance. Through optimization and algorithms, they reveal a number of improvements for the embedded system compared to the existing. Considering the characteristics of the system to transplant the various useful algorithms to the embedded system, the method developed in the research can be further applied to various practical applications.ope

On the use of autonomous unmanned vehicles in response to hazardous atmospheric release incidents

Recent events have induced a surge of interest in the methods of response to releases of hazardous materials or gases into the atmosphere. In the last decade there has been particular interest in mapping and quantifying emissions for regulatory purposes, emergency response, and environmental monitoring. Examples include: responding to events such as gas leaks, nuclear accidents or chemical, biological or radiological (CBR) accidents or attacks, and even exploring sources of methane emissions on the planet Mars. This thesis presents a review of the potential responses to hazardous releases, which includes source localisation, boundary tracking, mapping and source term estimation. [Continues.]</div

Unmanned Aerial Systems for Wildland and Forest Fires

Wildfires represent an important natural risk causing economic losses, human death and important environmental damage. In recent years, we witness an increase in fire intensity and frequency. Research has been conducted towards the development of dedicated solutions for wildland and forest fire assistance and fighting. Systems were proposed for the remote detection and tracking of fires. These systems have shown improvements in the area of efficient data collection and fire characterization within small scale environments. However, wildfires cover large areas making some of the proposed ground-based systems unsuitable for optimal coverage. To tackle this limitation, Unmanned Aerial Systems (UAS) were proposed. UAS have proven to be useful due to their maneuverability, allowing for the implementation of remote sensing, allocation strategies and task planning. They can provide a low-cost alternative for the prevention, detection and real-time support of firefighting. In this paper we review previous work related to the use of UAS in wildfires. Onboard sensor instruments, fire perception algorithms and coordination strategies are considered. In addition, we present some of the recent frameworks proposing the use of both aerial vehicles and Unmanned Ground Vehicles (UV) for a more efficient wildland firefighting strategy at a larger scale.Comment: A recent published version of this paper is available at: https://doi.org/10.3390/drones501001