Ambiente de simulação para o sistema de exploração robótica subaquática UNEXMIN

Underwater mines exploration is a valued, complex, expensive and time-consuming task. The unstable nature of the underwater environment with lack of visibility and the existence of obstructions create the need for complex navigation software which requires numerous missions and hardware/software validations. When testing and verifying control algorithms for such an operation, a simulation environment can be a very helpful tool. This also includes tools for the development of unmanned vehicle software, algorithm benchmarking and system preliminary validation. The objective in this thesis was to start the development of a simulation platform that can be used when developing and testing control systems for AUV operations. The simulator will include a dynamic model of an AUV in addition to complex world and sensor models such as DVL, IMU, Multibeam, Mechanical Scanning Imaging Sonar (MSIS), cameras, SLS and others. The simulated world includes water graphics, mine meshes, underwater visibility, currents, and hydrodynamics. Control of the robot in simulation is performed by keyboard or joystick over thrusters. The platform must be universal, such that users can implement their own algorithms easily and get immediate simulation results without needing to implement a complete control system. There should also be an easy transition between testing the control system on the simulated AUV and applying it to the real AUV. Robot Operating System (ROS) and Gazebo were used in the development of the platform. The platform with sensors and navigation was validated with real-world tests comparison.A exploração de minas subaquáticas ´e uma tarefa valiosa, complexa, dispendiosa e demorada. A natureza instável do ambiente subaquático, com falta de visibilidade e a existência de obstruções, cria a necessidade de software de navegação complexo, qual requer inúmeras missões e validações de hardware/software. Ao testar e verificar os algoritmos de controle para tal operação, um ambiente de simulação pode ser uma ferramenta muito útil. Isto também inclui ferramentas para o desenvolvimento de software de veículos não tripulados, benchmarking de algoritmos e validação preliminar do sistema. O objetivo desta tese foi iniciar o desenvolvimento de uma plataforma de simulação que possa ser usada no desenvolvimento e teste de sistemas de controle para operações de AUV. O simulador incluirá um modelo dinâmico de um AUV, além de modelos complexos do mundo e sensores, como DVL, IMU, Multibeam, MSIS, câmaras, SLS e outros. O mundo simulado inclui gráficos de ´agua, malhas de minas, visibilidade subaquática, correntes e hidrodinâmica. O controle do robô ´e realizado por teclado ou joystick sob as dinâmicas de propulsão. O simulador deve ser universal, de modo que os usuários possam implementar seus próprios algoritmos facilmente e obter resultados imediatos de simulação sem a necessidade de implementar um sistema de controle completo. Também deve haver uma transição fácil entre testar o sistema de controle no AUV simulado e aplicá-lo ao AUV real. ROS e Gazebo foram usados no desenvolvimento da plataforma. A plataforma com sensores e navegação foi validada com comparação de testes reais

Image enhancement for underwater mining applications

The exploration of water bodies from the sea to land filled water spaces has seen a continuous increase with new technologies such as robotics. Underwater images is one of the main sensor resources used but suffer from added problems due to the environment. Multiple methods and techniques have provided a way to correct the color, clear the poor quality and enhance the features. In this thesis work, we present the work of an Image Cleaning and Enhancement Technique which is based on performing color correction on images incorporated with Dark Channel Prior (DCP) and then taking the converted images and modifying them into the Long, Medium and Short (LMS) color space, as this space is the region in which the human eye perceives colour. This work is being developed at LSA (Laboratório de Sistema Autónomos) robotics and autonomous systems laboratory. Our objective is to improve the quality of images for and taken by robots with the particular emphasis on underwater flooded mines. This thesis work describes the architecture and the developed solution. A comparative analysis with state of the art methods and of our proposed solution is presented. Results from missions taken by the robot in operational mine scenarios are presented and discussed and allowing for the solution characterization and validation

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

Local Generating Map System Using Rviz ROS and Kinect Camera for Rescue Robot Application

This paper presents a model to generate a 3D model of a room, where room mapping is very necessary to find out the existing real conditions, where this modeling will be applied to the rescue robot. To solve this problem, researchers made a breakthrough by creating a 3D room mapping system. The mapping system and 3D model making carried out in this study are to utilize the camera Kinect and Rviz on the ROS. The camera takes a picture of the area around it, the imagery results are processed in the ROS system, the processing carried out includes several nodes and topics in the ROS which later the signal results are sent and displayed on the Rviz ROS. From the results of the tests that have been carried out, the designed system can create a 3D model from the Kinect camera capture by utilizing the Rviz function on the ROS. From this model later every corner of the room can be mapped and modeled in 3

Underwater measurements with UX robots; a new and available tool developed by UNEXUP

The UNEXMIN (Horizon 2020) and UNEXUP (EIT RawMaterials) projects developed a novel technology to send robots and even autonomously deliver optical images, 3D maps and other georeferenced scientific data from flooded underground environments, like abandoned mines, caves or wells. The concept turned into a market ready solution in seven years, where the last few years of field trials of the development beautifully demonstrating the technology's premier capabilities. Here in this paper, we focus on the wide variety of environments, circumstances and measurements where the UNEXMIN technology can be the best solution or the only solution to deliver certain research or engineering data. These are obtained from both simple and complex environments like different mines and caves, small and large cavities, long and tight tunnels and shafts, different visibility conditions, even different densities of the liquid medium where UX robots operated.</p

UNEXMIN H2020 project: an autonomous underwater explorer for flooded mines

UNEXMIN (Underwater Explorer for Flooded Mines, Grant Agreement No. 690008, www.unexmin.eu) is a project funded by the European Commission’s HORIZON2020 Framework Programme. The project is developing a multi-platform robotic system for the autonomous exploration and mapping of flooded underground mines. The robotic system – UX-1 – will use non-invasive methods for the 3D mapping of abandoned underground flooded mines, bringing new important geological data that currently cannot be obtained by other means without having significant costs and safety risks. The deployment of a multi-robotic system in a confined and unknown environment poses challenges to the autonomous operation of the robot, and there is a risk of damaging the equipment and the mine itself. Key challenges are related to 1) structural design for robustness and resilience, 2) localization, navigation and 3D mapping, 3) guidance, propulsion and control, 4) autonomous operation and supervision, 5) data processing, interpretation and evaluation. Underwater environments constrain basic robotic functions as well as the size and weight of any operable robot. The limiting factors in these environments influence the type and amount of equipment able to be mounted onto a robotic system. Crucial abilities for an underwater robot’s functionality include unobstructed movement, autonomy, mapping and environmental awareness. To enable these critical functions, we employ components such as cameras, SONAR, thrusters, structured-light laser scanners, and on-board computers, rechargeable batteries and protective pressure hulls. In UNEXMIN, additional underwater instrumentation is being developed to measure pH, pressure, temperature, water chemistry and conductivity, magnetic fields, and gamma radiation levels. An on-board geophysical system will enable sub-bottom profiling, and multispectral and UV fluorescence imaging units are being installed for mineralogical identification. All these tailor-made instruments are been tested in laboratory and real environment conditions

Implementation Kinematics Modeling and Odometry of Four Omni Wheel Mobile Robot on The Trajectory Planning and Motion Control Based Microcontroller

The control of kinematic modeling in a four wheel omni-directional robot (FWOR) is very difficult. Because you have to adjust the speed of the four DC motors. The speed of DC motors is controlled so that the FWOR robot can be controlled. This paper will explain the application of kinematic modeling of four wheel omni directional robots as track tracking controllers and microcontroller based movement control. Kinematic is the study of robot motion based on geometric structure analysis of a stationary / moving reference coordinate frame system without considering the force, torque or certain moments that cause movement. By applying kinematic modeling and calculation of the odometric system as feedback, the control of the robot trajectory movement can be controlled with precision in accordance with the path planning that has been made. The robot track control technique is embedded in a 32-bit ARM microcontroller. The path planning system and observing robot movement are carried out using a friendly graphic interface using Processing to facilitate the robot monitoring process. The results of the experiments and tests carried out, the system is able to control the rate of movement of the robot with great precision in accordance with the path planning made

Interface Homem-Máquina Multi Robótica em Unity3D

More than ever the use of autonomous vehicles to accomplish objectives deemed too dangerous or even impossible by human standards is increasing. This demand puts to the test our capabilities for managing teams of multiple robots and creating intuitive interactions with these teams is a must. Creating means to abstract and condense the information that reaches the end user into a single kit of software would improve its manageability considerably. The development of a centralized graphical user interface is proposed to alleviate the workload of the human operator. This interface is thought out to be simple in delivering its information taking cues from video games, a well known industry in studying the theory behind the creation of user interfaces. Sensorial information is abstracted in a graphical perspective much like the attributes of a character inside a video game. The Unity game engine was used to implement such an interface, integrating ROS with a layer of DDS to manage the communications while providing QoS settings. The DDS solves the problem of multiple ROS masters by setting up a separate network where users can connect and disconnect seamlessly from the network, without the need to restart roscore on each machine. Interactions between these two software is made by using websockets on a local network. Visual representations of the sensors onboard the autonomous vehicles transform the huge stream of data into human understandable formats for immediate response by the operator. Dynamic generation of terrain was accomplished by the use of LiDAR and side-scan sensors, if available, to map the surroundings, while Mapbox provided prefetched terrain data from OpenStreetMaps.Mais do que nunca, o uso de veículos autónomos para cumprir objectivos considerados demasiado perigosos ou até mesmo impossíveis segundo os padrões humanos tem vindo a aumentar. Este requerimento testa as nossas capacidades de gestão de equipas de múltiplos robôs e torna a criação de interações intuitivas com estas equipas numa necessidade. Criar meios de abstrair e condensar a informação que chega ao utilizador final num só pacote de software iria melhorar a sua gestão consideravelmente. O desenvolvimento de uma interface gráfica centralizada é proposta de modo a aliviar a carga de trabalho do operador humano. Esta interface é pensada para transmitir a sua informação como um vídeo jogo, sendo que esta é uma indústria que conhecida pelo seu estudo de interfaces de utilizador. Informação sensorial é abstraída com uma perspectiva gráfica tal como os atributos de uma personagem de um vídeo jogo. O motor de jogo Unity foi o utilizado para implementar tal interface integrando funcionalidades de ROS com uma camada de DDS, responsável pela gestão das comunicações, fornecendo opções de QoS. O DDS resolve o problema de múltiplos ROS master estabelecendo uma rede separada em que os utilizadores podem conectar-se e desconectar-se simultaneamente sem haver a necessidade de reiniciar o roscore em cada máquina. Interações entre os dois software é efetuada através de websockets numa rede local. Representações visuais dos sensores a bordo dos veículos autónomos transformam os enormes fluxos de dados em formatos facilmente compreensíveis por humanos para resposta imediata por parte do operador. Geração dinâmica de ambientes virtuais foi tornado possível com recurso a sensores como LiDAR e side-scan, caso existam, enquanto que API’s como Mapbox e OpenStreetMaps forneceram dados estáticos destes ambientes

Underwater Robots Part I: Current Systems and Problem Pose

International audienceThis paper constitutes the first part of a general overview of underwater robotics. The second part is titled: Underwater Robots Part II: existing solutions and open issues

Physics-based Machine Learning Methods for Control and Sensing in Fish-like Robots

Underwater robots are important for the construction and maintenance of underwater infrastructure, underwater resource extraction, and defense. However, they currently fall far behind biological swimmers such as fish in agility, efficiency, and sensing capabilities. As a result, mimicking the capabilities of biological swimmers has become an area of significant research interest. In this work, we focus specifically on improving the control and sensing capabilities of fish-like robots. Our control work focuses on using the Chaplygin sleigh, a two-dimensional nonholonomic system which has been used to model fish-like swimming, as part of a curriculum to train a reinforcement learning agent to control a fish-like robot to track a prescribed path. The agent is first trained on the Chaplygin sleigh model, which is not an accurate model of the swimming robot but crucially has similar physics; having learned these physics, the agent is then trained on a simulated swimming robot, resulting in faster convergence compared to only training on the simulated swimming robot. Our sensing work separately considers using kinematic data (proprioceptive sensing) and using surface pressure sensors. The effect of a swimming body\u27s internal dynamics on proprioceptive sensing is investigated by collecting time series of kinematic data of both a flexible and rigid body in a water tunnel behind a moving obstacle performing different motions, and using machine learning to classify the motion of the upstream obstacle. This revealed that the flexible body could more effectively classify the motion of the obstacle, even if only one if its internal states is used. We also consider the problem of using time series data from a `lateral line\u27 of pressure sensors on a fish-like body to estimate the position of an upstream obstacle. Feature extraction from the pressure data is attempted with a state-of-the-art convolutional neural network (CNN), and this is compared with using the dominant modes of a Koopman operator constructed on the data as features. It is found that both sets of features achieve similar estimation performance using a dense neural network to perform the estimation. This highlights the potential of the Koopman modes as an interpretable alternative to CNNs for high-dimensional time series. This problem is also extended to inferring the time evolution of the flow field surrounding the body using the same surface measurements, which is performed by first estimating the dominant Koopman modes of the surrounding flow, and using those modes to perform a flow reconstruction. This strategy of mapping from surface to field modes is more interpretable than directly constructing a mapping of unsteady fluid states, and is found to be effective at reconstructing the flow. The sensing frameworks developed as a result of this work allow better awareness of obstacles and flow patterns, knowledge which can inform the generation of paths through the fluid that the developed controller can track, contributing to the autonomy of swimming robots in challenging environments