Proactive Highly Ambulatory Sensor Routing (PHASeR) protocol for mobile wireless sensor networks

This paper presents a novel multihop routing protocol for mobile wireless sensor networks called PHASeR (Proactive Highly Ambulatory Sensor Routing). The proposed protocol uses a simple hop-count metric to enable the dynamic and robust routing of data towards the sink in mobile environments. It is motivated by the application of radiation mapping by unmanned vehicles, which requires the reliable and timely delivery of regular measurements to the sink. PHASeR maintains a gradient metric in mobile environments by using a global TDMA MAC layer. It also uses the technique of blind forwarding to pass messages through the network in a multipath manner. PHASeR is analysed mathematically based on packet delivery ratio, average packet delay, throughput and overhead. It is then simulated with varying mobility, scalability and traffic loads. The protocol gives good results over all measures, which suggests that it may also be suitable for a wider array of emerging applications

Information Surfing for Model-driven Radiation Mapping

In this report we develop a control scheme to coordinate a group of mobile sensors for radiation mapping of a given planar polygon region. The control algorithm is based on the concept of information surfing, where navigation is done by means of following information gradients, taking into account sensing performance as well as inter-robot communication range limitations. The control scheme provably steers mobile sensors to locations at which they maximize the information content of their measurement data, and the asymptotic properties of our information metric with respect to time ensures that no local information metric extremum traps the sensors indefinitely. In addition, the inherent synergy of the mobile sensor group facilitates the temporal erosion of such extremum configurations. Information surfing allows for reactive mobile sensor network behavior and adaptation to environmental changes, as well as human retasking

Information Surfing for Radiation Building

We develop a control scheme for a group of mobile sensors to map radiation over a given planar polygonal region. The advantage of this methodology is that it provides quick situational awareness regarding radiation levels, which is being updated and refined in real- time as more measurements become available. The control algorithm is based on the concept of information surfing, where navigation is done by following information gradients, taking into account sensing performance and the dynamics of the observed proces

Robot Protection in the Hazardous Environments

Rescue missions for chemical, biological, radiological, nuclear, and explosive (CBRNE) incidents are highly risky and sometimes it is impossible for rescuers to perform, while these accidents vary dramatically in features and protection requirements. The purpose of this chapter is to present several protection approaches for rescue robots in the hazardous conditions. And four types of rescue robots are presented, respectively. First, design factors and challenges of the rescue robots are analyzed and indicated for these accidents. Then the rescue robots with protective modification are presented, respectively, meeting individual hazardous requirements. And finally several tests are conducted to validate the effectiveness of these modified robots. It is clear that these well-designed robots can work efficiently for the CBRNE response activities

Autonomous Robotic Gamma Radiation Measurement

Tato práce se zaměřuje na autonomní lokalizaci radiologických zdrojů v definované oblasti zájmu. Jejím cílem je vyvinout lokalizační strategie a platformu, na které je bude možné vyzkoušet. Platforma je sestavena z průzkumného robotu Orpheus-X3, scintilačních detektorů a přesného GNSS přijímače. Algoritmus pro vytváření mapy distribuce radiačního pole je rozšířen. Jsou představeny nové metody založené na směrové citlivosti navrhovaného detekčního systému. Počáteční průzkum oblasti zájmu je uskutečněn pomocí kružnicových trajektorií. Všechny algoritmy byly otestována jak simulacemi, tak reálnými experimenty. Dosažená přesnosti lokalizace je v řádu desítek centimetrů. Časová efektivita je použitím nových algoritmů zvýšena přibližně dvakrát až pětkrát. Jedním z~přínosů práce je vývoj modulárního systému, který může být přesunut a uzpůsoben na jiné platformy. Výhodou použitého systému je vysoký stupeň autonomie a bezpečnost pro lidského operátora.This thesis focuses on autonomous localization of radiological sources in a defined area of interest. Its aim is to develop localization strategies and a platform on which they can be tested. The platform is based on reconnaissance robot Orpheus-X3, scintillation detectors and a precise GNSS receiver. Algorithm for creating a radiation distribution map is extended. New methods based on directional sensitivity of proposed detection system are introduced. Initial exploration of the area of interest is done by using circular trajectories. All algorithms are tested both by simulation and real experiments. The achieved precision of localization is in order of tens of centimeters. Time efficiency is increased approximately two to five times by applying new algorithms. One of the contributions of the thesis is a development of a modular system that could be transferred and adjusted to different platforms. The advantage of the used system is a high degree of autonomy and safety for a human operator.

Reconstruction of urban radiation landscape using machine learning methods

Efficiently monitoring a geographic region's radiation level and detecting anomalous radiation sources is an essential issue in homeland security. This task includes identifying illicit movement of special nuclear material, locating unusual radioactive events, and estimating the intensity of radioactive sources to name a few. Besides those anomalous radiation sources, there is naturally occurring radioactive material presented in air, soil and building materials. Radiation emitted from those materials compose the background radiation, which fluctuates in both space and time. The urban radiation landscape consists of the anomalous radiation sources and the background radiation. In this thesis, we present our work on reconstructing the urban radiation landscape using mobile sensor networks, which has two interconnected focuses. One is to model the background radiation; the other is to detect and search for anomalous radiation sources. Modeling of background radiation is conducted in two steps: retrospective modeling and prospective modeling. The retrospective modeling focuses on estimating visited positions’ radiation intensities, in which a maximum likelihood estimation method is developed to decouple and estimate temporal fluctuation and spatial distribution of background radiation. The prospective modeling focuses on predicting background radiation intensities, in which the Gaussian process regression is applied to predict unvisited positions' background spatial distributions, and recurrent neural network models are trained to predict future background temporal fluctuations. An integrated anomalous radiation source detection algorithm is developed to detect radiation sources in urban radiation landscape. Background radiation models are combined in the algorithm to eliminate false alarms produced by high background regions and temporal background fluctuations. A double Q-learning based anomalous source searching algorithm is investigated to navigate the detector searching for sources

Smart radiation sensor management; radiation search and mapping using mobile robots

The current geopolitical situation requires automated tools for quick and effective assessement of threats. Modern threats are subtle and ephemeral, and can be hidden across large areas. Classical information extraction methods, where data is randomly collected and then subsequently filtered and analyzed by human operators in search of particular signatures, are no longer effective against today’s modern threats. Data collection must be guided by querying world models that afford the span and resolution needed for multi-scale problems. Currently, searching for radiation sources is usually done manually, by operators waving radiation counters in front of them as they walk. This method does not provide any visual or statistical data map of the area in question. To quickly characterize the severity of the situation, an efficient way of obtaining this radiation map is needed. When searching fo