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

Skills Needs of the Civil Engineering Sector in the European Union Countries: Current Situation and Future Trends

The construction sector has always occupied a strategic place in the European economy. The European construction industry suffered during the 2007–2008 global financial crisis, and today the sector is undergoing a recovery process. Among all the construction subsectors, civil engineering has the highest growth rate. Currently, the sector has to face profound industrial changes emerging with digital transformations (Industry 4.0), sustainability, climate change and energy efficiency. To promote the growth of the civil engineering sector and accelerate the recovery, we need to create a highly qualified and competent workforce that can handle the challenges coming up with the technological progress and global competitiveness. The main condition to achieve this capable workforce is to define the expected evolution of skills requirements. For that purpose, our work focuses on identifying current and near-future key skills required by the civil engineering occupations. To achieve this, we developed an automated sectoral database for the current and near-future skills requirements of the selected professional profiles. It is our belief that this sectoral database is a fundamental framework that will guide the sector through the future changes. We also believe that our research can be used as a key tool for construction companies, policy-makers, academics and training centers to develop well-designed and efficient training programs for upskilling and reskilling the workforce.This research was partly cofunded by: the European Union through the Erasmus Plus Programme (Grant Agreement No. 2018-3019/001-001, Project No. 600886-1-2018-1-DE-EPPKA2-SSA-B), Accenture, Inzu Group, Fundación Telefónica and Fundación BBK, partners of the Deusto Digital Industry Chair

Piggybacking on an Autonomous Hauler: Business Models Enabling a System-of-Systems Approach to Mapping an Underground Mine

With ever-increasing productivity targets in mining operations, there is a growing interest in mining automation. In future mines, remote-controlled and autonomous haulers will operate underground guided by LiDAR sensors. We envision reusing LiDAR measurements to maintain accurate mine maps that would contribute to both safety and productivity. Extrapolating from a pilot project on reliable wireless communication in Boliden's Kankberg mine, we propose establishing a system-of-systems (SoS) with LIDAR-equipped haulers and existing mapping solutions as constituent systems. SoS requirements engineering inevitably adds a political layer, as independent actors are stakeholders both on the system and SoS levels. We present four SoS scenarios representing different business models, discussing how development and operations could be distributed among Boliden and external stakeholders, e.g., the vehicle suppliers, the hauling company, and the developers of the mapping software. Based on eight key variation points, we compare the four scenarios from both technical and business perspectives. Finally, we validate our findings in a seminar with participants from the relevant stakeholders. We conclude that to determine which scenario is the most promising for Boliden, trade-offs regarding control, costs, risks, and innovation must be carefully evaluated.Comment: Preprint of industry track paper accepted for the 25th IEEE International Conference on Requirements Engineering (RE'17

Camera Marker Networks for Pose Estimation and Scene Understanding in Construction Automation and Robotics.

The construction industry faces challenges that include high workplace injuries and fatalities, stagnant productivity, and skill shortage. Automation and Robotics in Construction (ARC) has been proposed in the literature as a potential solution that makes machinery easier to collaborate with, facilitates better decision-making, or enables autonomous behavior. However, there are two primary technical challenges in ARC: 1) unstructured and featureless environments; and 2) differences between the as-designed and the as-built. It is therefore impossible to directly replicate conventional automation methods adopted in industries such as manufacturing on construction sites. In particular, two fundamental problems, pose estimation and scene understanding, must be addressed to realize the full potential of ARC. This dissertation proposes a pose estimation and scene understanding framework that addresses the identified research gaps by exploiting cameras, markers, and planar structures to mitigate the identified technical challenges. A fast plane extraction algorithm is developed for efficient modeling and understanding of built environments. A marker registration algorithm is designed for robust, accurate, cost-efficient, and rapidly reconfigurable pose estimation in unstructured and featureless environments. Camera marker networks are then established for unified and systematic design, estimation, and uncertainty analysis in larger scale applications. The proposed algorithms' efficiency has been validated through comprehensive experiments. Specifically, the speed, accuracy and robustness of the fast plane extraction and the marker registration have been demonstrated to be superior to existing state-of-the-art algorithms. These algorithms have also been implemented in two groups of ARC applications to demonstrate the proposed framework's effectiveness, wherein the applications themselves have significant social and economic value. The first group is related to in-situ robotic machinery, including an autonomous manipulator for assembling digital architecture designs on construction sites to help improve productivity and quality; and an intelligent guidance and monitoring system for articulated machinery such as excavators to help improve safety. The second group emphasizes human-machine interaction to make ARC more effective, including a mobile Building Information Modeling and way-finding platform with discrete location recognition to increase indoor facility management efficiency; and a 3D scanning and modeling solution for rapid and cost-efficient dimension checking and concise as-built modeling.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113481/1/cforrest_1.pd

Vision-based legged robot navigation: localisation, local planning, learning

The recent advances in legged locomotion control have made legged robots walk up staircases, go deep into underground caves, and walk in the forest. Nevertheless, autonomously achieving this task is still a challenge. Navigating and acomplishing missions in the wild relies not only on robust low-level controllers but also higher-level representations and perceptual systems that are aware of the robot's capabilities. This thesis addresses the navigation problem for legged robots. The contributions are four systems designed to exploit unique characteristics of these platforms, from the sensing setup to their advanced mobility skills over different terrain. The systems address localisation, scene understanding, and local planning, and advance the capabilities of legged robots in challenging environments. The first contribution tackles localisation with multi-camera setups available on legged platforms. It proposes a strategy to actively switch between the cameras and stay localised while operating in a visual teach and repeat context---in spite of transient changes in the environment. The second contribution focuses on local planning, effectively adding a safety layer for robot navigation. The approach uses a local map built on-the-fly to generate efficient vector field representations that enable fast and reactive navigation. The third contribution demonstrates how to improve local planning in natural environments by learning robot-specific traversability from demonstrations. The approach leverages classical and learning-based methods to enable online, onboard traversability learning. These systems are demonstrated via different robot deployments on industrial facilities, underground mines, and parklands. The thesis concludes by presenting a real-world application: an autonomous forest inventory system with legged robots. This last contribution presents a mission planning system for autonomous surveying as well as a data analysis pipeline to extract forestry attributes. The approach was experimentally validated in a field campaign in Finland, evidencing the potential that legged platforms offer for future applications in the wild

Two Dimensional Positioning and Heading Solution for Flying Vehicles Using a Line-Scanning Laser Radar (LADAR)

Emerging technology in small autonomous flying vehicles requires the systems to have a precise navigation solution in order to perform tasks. In many critical environments, such as indoors, GPS is unavailable necessitating the development of supplemental aiding sensors to determine precise position. This research investigates the use of a line scanning laser radar (LADAR) as a standalone two dimensional position and heading navigation solution and sets up the device for augmentation into existing navigation systems. A fast histogram correlation method is developed to operate in real-time on board the vehicle providing position and heading updates at a rate of 10 Hz. LADAR navigation methods are adapted to 3 dimensions with a simulation built to analyze performance loss due attitude changes during flight. These simulations are then compared to experimental results collected using SICK LD-OEM 1000 mounted a cart traversing. The histogram correlation algorithm applied in this work was shown to successfully navigate a realistic environment where a quadrotor in short flights of less than 5 min in larger rooms. Application in hallways show great promise providing a stable heading along with tracking movement perpendicular to the hallway