270 research outputs found

    Sampling-Based Exploration Strategies for Mobile Robot Autonomy

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    A novel, sampling-based exploration strategy is introduced for Unmanned Ground Vehicles (UGV) to efficiently map large GPS-deprived underground environments. It is compared to state-of-the-art approaches and performs on a similar level, while it is not designed for a specific robot or sensor configuration like the other approaches. The introduced exploration strategy, which is called Random-Sampling-Based Next-Best View Exploration (RNE), uses a Rapidly-exploring Random Graph (RRG) to find possible view points in an area around the robot. They are compared with a computation-efficient Sparse Ray Polling (SRP) in a voxel grid to find the next-best view for the exploration. Each node in the exploration graph built with RRG is evaluated regarding the ability of the UGV to traverse it, which is derived from an occupancy grid map. It is also used to create a topology-based graph where nodes are placed centrally to reduce the risk of collisions and increase the amount of observable space. Nodes that fall outside the local exploration area are stored in a global graph and are connected with a Traveling Salesman Problem solver to explore them later

    On the motion planning & control of nonlinear robotic systems

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    In the last decades, we saw a soaring interest in autonomous robots boosted not only by academia and industry, but also by the ever in- creasing demand from civil users. As a matter of fact, autonomous robots are fast spreading in all aspects of human life, we can see them clean houses, navigate through city traffic, or harvest fruits and vegetables. Almost all commercial drones already exhibit unprecedented and sophisticated skills which makes them suitable for these applications, such as obstacle avoidance, simultaneous localisation and mapping, path planning, visual-inertial odometry, and object tracking. The major limitations of such robotic platforms lie in the limited payload that can carry, in their costs, and in the limited autonomy due to finite battery capability. For this reason researchers start to develop new algorithms able to run even on resource constrained platforms both in terms of computation capabilities and limited types of endowed sensors, focusing especially on very cheap sensors and hardware. The possibility to use a limited number of sensors allowed to scale a lot the UAVs size, while the implementation of new efficient algorithms, performing the same task in lower time, allows for lower autonomy. However, the developed robots are not mature enough to completely operate autonomously without human supervision due to still too big dimensions (especially for aerial vehicles), which make these platforms unsafe for humans, and the high probability of numerical, and decision, errors that robots may make. In this perspective, this thesis aims to review and improve the current state-of-the-art solutions for autonomous navigation from a purely practical point of view. In particular, we deeply focused on the problems of robot control, trajectory planning, environments exploration, and obstacle avoidance

    Ultrasound and Photoacoustic Techniques for Surgical Guidance Inside and Around the Spine

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    Technological advances in image-guidance have made a significant impact in surgical standards, allowing for safer and less invasive procedures. Ultrasound and photoacoustic imaging are promising options for surgical guidance given their real-time capabilities without the use of ionizing radiation. However, challenges to improve the feasibility of ultrasound- and photoacoustic-based surgical guidance persists in the presence of bone. In this thesis, we address four challenges surrounding the implementation of ultrasound- and photoacoustic-based surgical guidance in clinical scenarios inside and around the spine. First, we introduce a novel regularized implementation of short-lag spatial coherence (SLSC) beamforming, named locally-weighted short-lag spatial coherence (LW-SLSC). LW-SLSC improves the segmentation of bony structures in ultrasound images, thus reducing the hardware and software cost of registering pre and intra-operative volumes. Second, we describe a contour analysis framework to characterize and differentiate photoacoustic signals originating from cancellous and cortical bone, which is critical for a safety navigation of surgical tools through small bony cavities such as the pedicle. This analysis is also useful for localizing tool tips within the pedicle. Third, we developed a GPU approach to SLSC beamforming to improve the signal-to-noise ratio of photoacoustic targets using low laser energies, thus improving the performance of robotic visual servoing of tooltips and enabling miniaturization of laser systems in the operating room. Finally, we developed a novel acoustic-based atlas method to identify photoacoustic contrast agents and discriminate them from tissue using only two laser wavelengths. This approach significantly reduces acquisition times in comparison to conventional spectral unmixing techniques. These four contributions are beneficial for the transition of a combined ultrasound and photoacoustic-based image-guidance system towards more challenging scenarios of surgical navigation. Focusing on bone structures inside and surrounding the spine, the newly combined systems and techniques demonstrated herein feature robust, accurate, and real-time capabilities to register to preoperative images, localize surgical tool tips, and characterize biomarkers. These contributions strengthen the range of possibilities for spinous and transthoracic ultrasound and photoacoustic navigation, broaden the scope of this field, and shorten the road to clinical implementation in the operating room

    Mars delivery service - development of the electro-mechanical systems of the Sample Fetch Rover for the Mars Sample Return Campaign

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    This thesis describes the development of the Sample Fetch Rover (SFR), studied for Mars Sample Return (MSR), an international campaign carried out in cooperation between the National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA). The focus of this document is the design of the electro-mechanical systems of the rover. After placing this work into the general context of robotic planetary exploration and summarising the state of the art for what concerns Mars rovers, the architecture of the Mars Sample Return Campaign is presented. A complete overview of the current SFR architecture is provided, touching upon all the main subsystems of the spacecraft. For each area, it is discussed what are the design drivers, the chosen solutions and whether they use heritage technology (in particular from the ExoMars Rover) or new developments. This research focuses on two topics of particular interest, due to their relevance for the mission and the novelty of their design: locomotion and sample acquisition, which are discussed in depth. The early SFR locomotion concepts are summarised, covering the initial trade-offs and discarded designs for higher traverse performance. Once a consolidated architecture was reached, the locomotion subsystem was developed further, defining the details of the suspension, actuators, deployment mechanisms and wheels. This technology is presented here in detail, including some key analysis and test results that support the design and demonstrate how it responds to the mission requirements. Another major electro-mechanical system developed as part of this work is the one dedicated to sample tube acquisition. The concept of operations of this machinery was defined to be robust against the unknown conditions that characterise the mission. The design process led to a highly automated robotic system which is described here in its main components: vision system, robotic arm and tube storage

    LIPIcs, Volume 274, ESA 2023, Complete Volume

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    LIPIcs, Volume 274, ESA 2023, Complete Volum

    Fundamentals

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    Volume 1 establishes the foundations of this new field. It goes through all the steps from data collection, their summary and clustering, to different aspects of resource-aware learning, i.e., hardware, memory, energy, and communication awareness. Machine learning methods are inspected with respect to resource requirements and how to enhance scalability on diverse computing architectures ranging from embedded systems to large computing clusters

    Technologies and Applications for Big Data Value

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    This open access book explores cutting-edge solutions and best practices for big data and data-driven AI applications for the data-driven economy. It provides the reader with a basis for understanding how technical issues can be overcome to offer real-world solutions to major industrial areas. The book starts with an introductory chapter that provides an overview of the book by positioning the following chapters in terms of their contributions to technology frameworks which are key elements of the Big Data Value Public-Private Partnership and the upcoming Partnership on AI, Data and Robotics. The remainder of the book is then arranged in two parts. The first part “Technologies and Methods” contains horizontal contributions of technologies and methods that enable data value chains to be applied in any sector. The second part “Processes and Applications” details experience reports and lessons from using big data and data-driven approaches in processes and applications. Its chapters are co-authored with industry experts and cover domains including health, law, finance, retail, manufacturing, mobility, and smart cities. Contributions emanate from the Big Data Value Public-Private Partnership and the Big Data Value Association, which have acted as the European data community's nucleus to bring together businesses with leading researchers to harness the value of data to benefit society, business, science, and industry. The book is of interest to two primary audiences, first, undergraduate and postgraduate students and researchers in various fields, including big data, data science, data engineering, and machine learning and AI. Second, practitioners and industry experts engaged in data-driven systems, software design and deployment projects who are interested in employing these advanced methods to address real-world problems

    Future Transportation

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    Greenhouse gas (GHG) emissions associated with transportation activities account for approximately 20 percent of all carbon dioxide (co2) emissions globally, making the transportation sector a major contributor to the current global warming. This book focuses on the latest advances in technologies aiming at the sustainable future transportation of people and goods. A reduction in burning fossil fuel and technological transitions are the main approaches toward sustainable future transportation. Particular attention is given to automobile technological transitions, bike sharing systems, supply chain digitalization, and transport performance monitoring and optimization, among others
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