An overview of robotics and autonomous systems for harsh environments

Across a wide range of industries and applications, robotics and autonomous systems can fulfil the crucial and challenging tasks such as inspection, exploration, monitoring, drilling, sampling and mapping in areas of scientific discovery, disaster prevention, human rescue and infrastructure management, etc. However, in many situations, the associated environment is either too dangerous or inaccessible to humans. Hence, a wide range of robots have been developed and deployed to replace or aid humans in these activities. A look at these harsh environment applications of robotics demonstrate the diversity of technologies developed. This paper reviews some key application areas of robotics that involve interactions with harsh environments (such as search and rescue, space exploration, and deep-sea operations), gives an overview of the developed technologies and provides a discussion of the key trends and future directions common to many of these areas

Design of a modular Autonomous Underwater Vehicle for archaeological investigations

MARTA (MARine Tool for Archaeology) is a modular AUV (Autonomous Underwater Vehicle) designed and developed by the University of Florence in the framework of the ARROWS (ARchaeological RObot systems for the World's Seas) FP7 European project. The ARROWS project challenge is to provide the underwater archaeologists with technological tools for cost affordable campaigns: i.e. ARROWS adapts and develops low cost AUV technologies to significantly reduce the cost of archaeological operations, covering the full extent of an archaeological campaign (underwater mapping, diagnosis and cleaning tasks). The tools and methodologies developed within ARROWS comply with the "Annex" of the 2001 UNESCO Convention for the protection of Underwater Cultural Heritage (UCH). The system effectiveness and MARTA performance will be demonstrated in two scenarios, different as regards the environment and the historical context, the Mediterranean Sea (Egadi Islands) and the Baltic Sea

A Multi-Sensor Fusion-Based Underwater Slam System

This dissertation addresses the problem of real-time Simultaneous Localization and Mapping (SLAM) in challenging environments. SLAM is one of the key enabling technologies for autonomous robots to navigate in unknown environments by processing information on their on-board computational units. In particular, we study the exploration of challenging GPS-denied underwater environments to enable a wide range of robotic applications, including historical studies, health monitoring of coral reefs, underwater infrastructure inspection e.g., bridges, hydroelectric dams, water supply systems, and oil rigs. Mapping underwater structures is important in several fields, such as marine archaeology, Search and Rescue (SaR), resource management, hydrogeology, and speleology. However, due to the highly unstructured nature of such environments, navigation by human divers could be extremely dangerous, tedious, and labor intensive. Hence, employing an underwater robot is an excellent fit to build the map of the environment while simultaneously localizing itself in the map. The main contribution of this dissertation is the design and development of a real-time robust SLAM algorithm for small and large scale underwater environments. SVIn – a novel tightly-coupled keyframe-based non-linear optimization framework fusing Sonar, Visual, Inertial and water depth information with robust initialization, loop-closing, and relocalization capabilities has been presented. Introducing acoustic range information to aid the visual data, shows improved reconstruction and localization. The availability of depth information from water pressure enables a robust initialization and refines the scale factor, as well as assists to reduce the drift for the tightly-coupled integration. The complementary characteristics of these sensing v modalities provide accurate and robust localization in unstructured environments with low visibility and low visual features – as such make them the ideal choice for underwater navigation. The proposed system has been successfully tested and validated in both benchmark datasets and numerous real world scenarios. It has also been used for planning for underwater robot in the presence of obstacles. Experimental results on datasets collected with a custom-made underwater sensor suite and an autonomous underwater vehicle (AUV) Aqua2 in challenging underwater environments with poor visibility, demonstrate performance never achieved before in terms of accuracy and robustness. To aid the sparse reconstruction, a contour-based reconstruction approach utilizing the well defined edges between the well lit area and darkness has been developed. In particular, low lighting conditions, or even complete absence of natural light inside caves, results in strong lighting variations, e.g., the cone of the artificial video light intersecting underwater structures and the shadow contours. The proposed method utilizes these contours to provide additional features, resulting into a denser 3D point cloud than the usual point clouds from a visual odometry system. Experimental results in an underwater cave demonstrate the performance of our system. This enables more robust navigation of autonomous underwater vehicles using the denser 3D point cloud to detect obstacles and achieve higher resolution reconstructions

Operation Strategy for a Low-Cost Easy-Operation Cassino Hexapod

This paper presents operation strategies for a hexapod walking machine that has been designed and built at the Laboratory of Robotics and Mechatronics (LARM) at the University of Cassino. Special care has been addressed in proposing and describing a suitable mechanical design and architecture that can be easily operated by a PLC with on–off logic. Experimental tests are reported in order to show feasibility and operational capability of the proposed design

A Sustainable Approach for the Management and Valorization of Underwater Cultural Heritage: New Perspectives from the TECTONIC Project

Documentation and conservation of underwater cultural heritage (UCH) are crucial to preserving humankind’s history and traditions, safeguarding tangible testimonies of past human life while ensuring its accessibility to future generations. The TECTONIC (Technological Consortium TO develop sustainability of underwater Cultural Heritage) project is promoting an intersectoral collaboration between academic and non-academic professionals (i.e., archaeologists, conservators, geologists, engineers, etc.) working on different topics related to UCHs, to find solutions to the issues still existing in the field. The overall aim is the exchange of skills for the improvement and assessment of innovative materials and techniques to develop solutions and marketable products for the conservation and management of the UCH, sustainably. To achieve its overall aim, TECTONIC is undertaking activities driven by the following objectives: (a) the study, documentation, and mapping of selected UCHs; (b) the creation of decision-support tools for UCH risk assessment in a changing environment; (c) the initiation of conservation studies and protocols for conservation activities; (d) the development of open and low-cost robotic solutions for the inspection of UCH; and (e) the raising of public awareness and knowledge about UCH. All the objectives are devoted to stimulating new sustainable ideas that would bring the growth of cultural tourism and the development of new marketable products by capitalizing on the research results.Fil: Ricca, Michela. Università della Calabria; ItaliaFil: Alexandrakis, George. Foundation For Research And Technology ? Hellas.; GreciaFil: Bonazza, Alessandra. Consiglio Nazionale Delle Ricerche. Istituto Di Scienze Dell Atmosfera E del Clima.; ItaliaFil: Bruno, Fabio. Università della Calabria; ItaliaFil: Petriaggi, Barbara Davidde. Instituto Superiore per la Conservazione ed il Restauro; ItaliaFil: Elkin, Dolores Carolina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Secretaría de Cultura de la Nación. Dirección Nacional de Cultura y Museos. Instituto Nacional de Antropología y Pensamiento Latinoamericano; ArgentinaFil: Lagudi, Antonio. Università della Calabria; ItaliaFil: Nicolas, Stephane. Centre d'Activité des Playes ZE Jean Monnet; FranciaFil: Novák, Michal. Synpo; República ChecaFil: Papatheodorou, George. University Of Patras; GreciaFil: Prieto, Javier. Universidad de Salamanca; EspañaFil: Ricci, Marco. Università della Calabria; ItaliaFil: Vasilijevic, Antonio. H2O Robotics; CroaciaFil: La Russa, Mauro Francesco. Università della Calabria; Italia. Foundation For Research And Technology ? Hellas.; Greci

Topologic Maps for Robotic Exploration of Underground Flooded Mines

The mapping of confined environments in mobile robotics is traditionally tackled in dense occupancy maps, requiring large amounts of storage. For some use cases, such as the exploration of flooded mines, the use of dense maps in processing slow down processes like path generation. I introduce a method of generating topological maps in constrained spaces such as mines. By taking a structure with fewer points, traversal and storage of explored space can be made more efficient, avoiding com plex graphs generated by methods like RRT and it’s variants. It’s simpler structure also allows for more intuitive human-machine interactions with it’s fewer points. I also introduce an autonomous frontier-based exploration approach to generate the topological map during exploration, taking advantage of it’s traversal to navigate through known space. With this work, simulation tests show it is possible to success fully extract a simpler graph structure describing the topology during autonomous exploration and that this structure is robust through explored regionsO mapeamento de ambientes confinados em robótica móvel, é tradicionalmente abordado em mapas densos de ocupação, necessitando de grandes quantidades de armazenamento. Para certos casos, tal como a exploração de minas submersas, o uso de mapas densos no processamento, atrasa processos como geração de caminhos. Utilizando uma estrutura com menos pontos, a travessia e o armazenamento de espaço explorado tornam-se mais eficientes, evitando grafos complexos gerados por métodos como RRT e variantes. A sua estrutura mais simples permite também interações homem-máquina com o seu número reduzido de pontos. Introduzo também uma abordagem autónoma de exploração baseada em fronteiras, para gerar o mapa topo lógico durante a exploração, tirando vantagem da travessia do mesmo para navegar por espaço conhecido. Com este trabalho, testes em simulação mostram ser possível extrair uma estrutura sob forma de grafo, descrevendo a topologia ao longo de explorações autónomas e que esta estrutura é robusta para a travessia em regiões explorada

Mechanical Design of Long Reach Super Thin Discrete Manipulator for Inspections in Fragile Historical Environments

Long reach and small diameter manipulators are ideal for borehole deployments into search and rescue scenarios and fragile historical environments. Small diameter passageways impose constraints on a snake arm manipulator which severely limit its performance and capabilities. This work investigates the effects of tendon tensions on the maximum working length of a snake arm under tight size constraints and how the maximum length is achieved through an algorithmic approach and consideration of how and when key parts fail

A survey on active simultaneous localization and mapping: state of the art and new frontiers

Active simultaneous localization and mapping (SLAM) is the problem of planning and controlling the motion of a robot to build the most accurate and complete model of the surrounding environment. Since the first foundational work in active perception appeared, more than three decades ago, this field has received increasing attention across different scientific communities. This has brought about many different approaches and formulations, and makes a review of the current trends necessary and extremely valuable for both new and experienced researchers. In this article, we survey the state of the art in active SLAM and take an in-depth look at the open challenges that still require attention to meet the needs of modern applications. After providing a historical perspective, we present a unified problem formulation and review the well-established modular solution scheme, which decouples the problem into three stages that identify, select, and execute potential navigation actions. We then analyze alternative approaches, including belief-space planning and deep reinforcement learning techniques, and review related work on multirobot coordination. This article concludes with a discussion of new research directions, addressing reproducible research, active spatial perception, and practical applications, among other topics