114 research outputs found

    Robot Mapping and Localisation in Water Pipes

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    The demand for inspection and repair technologies for the water industries on their water mains and distribution pipes is increasing. In urban water distribution systems, due to the fact that water pipes are ageing, pipe leakages and serious damage may occur. Compared with the cost of pipe replacement in the underground distribution system, regular pipe inspection and repair is more cost effective to water companies and local communities. However, small-diameter pipes are not accessible to humans because they are small in size and often buried underground. Therefore, inspection robotic systems are more suited to this task in terms of underground pipe networks mapping and damage localisation, in order to target repair from above ground. There are a number of challenges for robot mapping and localisation in water pipes, which are: 1) feature sparsity in water pipes – lack of navigation landmarks, 2) in-pipe robot can only detect nearby features, and 3) unlike indoor/outdoor SLAM problems, in-pipe robot has less movement flexibility. The main aim of this thesis is to solve these challenges and address the problem of robot mapping and localisation in small-diameter feature-sparse water pipes. This thesis presents a number of novel contributions. Firstly, for the front end, where raw sensor data is transformed into signals useful for mapping and localisation algorithms, new types of maps are developed here for water pipes: for plastic pipes, ultrasound data is used to map the ground profile through the plastic pipe wall, whilst for metal pipes a hydrophone is used to determine a map based on pipe vibration amplitude over space. Secondly, a new sequential approach to mapping and localisation is developed, based on alignment of multiple maps based on dynamic time warping averaging. Thirdly, a new simultaneous localisation and mapping algorithm is developed, which overcomes the limitation of the sequential approach in that the map is not updated. Finally, a new sensor fusion algorithm is developed that transforms the robot location in the local coordinate frame to the world coordinate frame, which would be essential for targeting repairs from above ground

    An alternative approach for robot localization inside pipes using RF spatial fadings

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    Accurate robot localization represents a challenge inside pipes due to the particular conditions that characterize this type of environment. Outdoor techniques (GPS in particular) do not work at all inside metal pipes, while traditional indoor localization methods based on camera or laser sensors do not perform well mainly due to a lack of external illumination and distinctive features along pipes. Moreover, humidity and slippery surfaces make wheel odometry unreliable. In this paper, we estimate the localization of a robot along a pipe with an alternative Radio Frequency (RF) approach. We first analyze wireless propagation in metallic pipes and propose a series of setups that allow us to obtain periodic RF spatial fadings (a sort of standing wave periodic pattern), together with the influence of the antenna position and orientation over these fadings. Subsequently, we propose a discrete RF odometry-like method, by means of counting the fadings while traversing them. The transversal fading analysis (number of antennas and cross-section position) makes it possible to increase the resolution of this method. Lastly, the model of the signal is used in a continuous approach serving as an RF map. The proposed localization methods outperform our previous contributions in terms of resolution, accuracy, reliability and robustness. Experimental results demonstrate the effectiveness of the RF-based strategy without the need for a previously known map of the scenario or any substantial modification of the existing infrastructure

    Robot localization in water pipes using acoustic signals and pose graph optimization

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    One of the most fundamental tasks for robots inspecting water distribution pipes is localization, which allows for autonomous navigation, for faults to be communicated, and for interventions to be instigated. Pose-graph optimization using spatially varying information is used to enable localization within a feature-sparse length of pipe. We present a novel method for improving estimation of a robot’s trajectory using the measured acoustic field, which is applicable to other measurements such as magnetic field sensing. Experimental results show that the use of acoustic information in pose-graph optimization reduces errors by 39% compared to the use of typical pose-graph optimization using landmark features only. High location accuracy is essential to efficiently and effectively target investment to maximise the use of our aging pipe infrastructure

    A Robot to Measure Water Parameters in Water Distribution Systems

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    Water distribution systems (WDS) are critical infrastructures that transfer drinking water to consumers. In the U.S., around 42 billion gallons of water are being delivered per day via one million miles of pipes to be used in different sectors. Incidents to pipelines cause leak or let contaminants enter purified water in pipe that is harmful to public health. Hence, periodic condition assessments of pipelines and water inside it are required. However, due to the long and complicated configurations of these networks, access to all parts of the pipelines is a cumbersome task. To this aim, in-pipe robots are promising solution that facilitate access to different locations inside pipelines and perform different in-pipe missions. In this project, we design and fabricate an in-pipe robotic system is that is used for water quality monitoring. The robot is equipped with a wireless sensor module and the sensor module is synchronized with the motion unit of the robot. The wireless sensor module facilitates bi-directional data transmission between the robot and base station aboveground. The integrated robotic system navigates in different configurations of the pipeline with smart motion. To this aim, the mechanical design of the self-powered robot based on three adjustable arm modules and three actuator modules is designed. The components of the robot are characterized based on real operation conditions in pipes. A multi-phase motion control algorithm is developed for the robot to move in straight path and non-straight configurations like bends and T-junctions. A bi-directional wireless sensor module is designed to send data packets through underground environment. Finally, the multi-phase motion controller is synchronized with the wireless sensor module and we propose an operation procedure for the robot. In the operation procedure, some radio transceivers are located at non-straight configurations of pipelines and receive the sensor measurements from the robot and guide the robot in the desired direction. The proposed operation procedure provides smart navigation and data transmission during operation for the robot

    Continuous fusion of motion data using an axis-angle rotation representation with uniform B-spline

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    The fusion of motion data is key in the fields of robotic and automated driving. Most existing approaches are filter-based or pose-graph-based. By using filter-based approaches, parameters should be set very carefully and the motion data can usually only be fused in a time forward direction. Pose-graph-based approaches can fuse data in time forward and backward directions. However, pre-integration is needed by applying measurements from inertial measurement units. Additionally, both approaches only provide discrete fusion results. In this work, we address this problem and present a uniform B-spline-based continuous fusion approach, which can fuse motion measurements from an inertial measurement unit and pose data from other localization systems robustly, accurately and efficiently. In our continuous fusion approach, an axis-angle is applied as our rotation representation method and uniform B-spline as the back-end optimization base. Evaluation results performed on the real world data show that our approach provides accurate, robust and continuous fusion results, which again supports our continuous fusion concept

    Robot Localization in Tunnel-like Environments.

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    Los entornos confinados como tuberías, túneles o minas constituyen infraestructuras clave para el desarrollo de las economías de los diferentes países. La existencia de estas infraestructuras conlleva la necesidad de llevar a cabo una serie de tareas de mantenimiento mediante inspecciones regulares para asegurar la integridad estructural de las mismas. Así mismo, existen otras tareas que se tienen que realizar en estos entornos como pueden ser misiones de rescate en caso de accidentes e incluso las propias tareas derivadas de la construcción de los mismos. La duras condiciones de este tipo de entornos, ausencia de luz, polvo, presencia de fluidos e incluso de sustancias tóxicas, hace que la ejecución de las mismas suponga un trabajo tedioso e incluso peligroso para las personas. Todo esto, unido a los continuos avances en las tecnologías robóticas, hacen que los robots sean los dispositivos más adecuados para la realización de estas tareas.Para que un robot pueda desempeñar su cometido de manera autónoma, es fundamental que pueda localizarse de manera precisa, no sólo para poder decidir las acciones a llevar a cabo sino también para poder ubicar de manera inequívoca los posibles daños que se puedan detectar durante las labores de inspección. El problema de la localización ha sido ampliamente estudiado en el mundo de la robótica, existiendo multitud de soluciones tanto para interiores como para exteriores mediante el uso de diferentes sensores y tecnologías. Sin embargo, los entornos tipo túnel presentan una serie de características específicas que hacen que la tarea de localización se convierta en todo un reto. La ausencia de iluminación y de características distinguibles tanto visuales como estructurales, hacen que los métodos tradicionales de localización basados en sensores láser y cámaras no funcionen correctamente. Además, al tratarse de entornos confinados, no es posible utilizar sensores típicos de exteriores como es el caso del GPS. La presencia de fluidos e incluso de superficies irregulares hacen poco fiables los métodos basados en odometría utilizando encoders en las ruedas del robot.Por otra parte, estos entornos presentan un comportamiento peculiar en lo que a la propagación de la señal de radiofrecuencia se refiere. Por un lado, a determinadas frecuencias, se comportan como guías de onda extendiendo el alcance de la comunicación, pero por otro, la señal radio sufre fuertes desvanecimientos o fadings. Trabajos previos han demostrado que es posible obtener fadings periódicos bajo una configuración determinada.Partiendo de estos estudios, en esta tesis se aborda el problema de la localización en tuberías y túneles reaprovechando esta naturaleza periódica de la señal radio. Inicialmente, se propone un método de localización para tuberías metálicas basado en técnicas probabilísticas, utilizando el modelo de propagación de la señal como un mapa de radiofrecuencia. Posteriormente, se aborda la localización en túneles siguiendo una estrategia similar de reaprovechar la naturaleza periódica de la señal y se presenta un método de localización discreta. Yendo un paso más allá, y con el objetivo de mejorar la localización a lo largo del túnel incluyendo otras fuentes de información, se desarrolla un método inspirado en el paradigma del graph-SLAM donde se incorporan los resultados obtenidos de la detección de características discretas proporcionadas por el propio túnel.Para ello, se implementa un sistema de detección que proporciona la posición absoluta de características relevantes de la señal periódica radio. Del mismo modo, se desarrolla un método de detección de características estructurales del túnel (galerías) que devuelve la posición conocida de las mismas. Todos estos resultados se incorporan al grafo como fuentes de información.Los métodos de localización desarrollados a lo largo de la tesis han sido validados con datos recolectados durante experimentos llevados a cabo con plataformas robóticas en escenarios reales: la tubería de Santa Ana en Castillonroy y el túnel ferroviario de Somport.<br /

    Autonomous control for miniaturized mobile robots in unknown pipe networks

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    Despite recent advances in robotic technology, sewer pipe inspection is still limited to conventional approaches that use cable-tethered robots. Such commercially available tethered robots lack autonomy, and their operation must be manually controlled via their tethered cables. Consequently, they can only travel to a certain distance in pipe, cannot access small-diameter pipes, and their deployment incurs high costs for highly skilled operators. In this paper, we introduce a miniaturised mobile robot for pipe inspection. We present an autonomous control strategy for this robot that is effective, stable, and requires only low-computational resources. The robots used here can access pipes as small as 75 mm in diameter. Due to their small size, low carrying capacity, and limited battery supply, our robots can only carry simple sensors, a small processor, and miniature wheel-legs for locomotion. Yet, our control method is able to compensate for these limitations. We demonstrate fully autonomous robot mobility in a sewer pipe network, without any visual aid or power-hungry image processing. The control algorithm allows the robot to correctly recognise each local network configuration, and to make appropriate decisions accordingly. The control strategy was tested using the physical micro robot in a laboratory pipe network. In both simulation and experiment, the robot autonomously and exhaustively explored an unknown pipe network without missing any pipe section while avoiding obstacles. This is a significant advance towards fully autonomous inspection robot systems for sewer pipe networks

    Simultaneous localization and mapping for inspection robots in water and sewer pipe networks: a review

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    At the present time, water and sewer pipe networks are predominantly inspected manually. In the near future, smart cities will perform intelligent autonomous monitoring of buried pipe networks, using teams of small robots. These robots, equipped with all necessary computational facilities and sensors (optical, acoustic, inertial, thermal, pressure and others) will be able to inspect pipes whilst navigating, selflocalising and communicating information about the pipe condition and faults such as leaks or blockages to human operators for monitoring and decision support. The predominantly manual inspection of pipe networks will be replaced with teams of autonomous inspection robots that can operate for long periods of time over a large spatial scale. Reliable autonomous navigation and reporting of faults at this scale requires effective localization and mapping, which is the estimation of the robot’s position and its surrounding environment. This survey presents an overview of state-of-the-art works on robot simultaneous localization and mapping (SLAM) with a focus on water and sewer pipe networks. It considers various aspects of the SLAM problem in pipes, from the motivation, to the water industry requirements, modern SLAM methods, map-types and sensors suited to pipes. Future challenges such as robustness for long term robot operation in pipes are discussed, including how making use of prior knowledge, e.g. geographic information systems (GIS) can be used to build map estimates, and improve the multi-robot SLAM in the pipe environmen

    Topological robot localization in a large-scale water pipe network

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    Topological localization is well suited to robots operating in water pipe networks because the environment is well defined as a set of discrete connected places like junctions, customer connections, and access points. Topological methods are more computationally efficient than metric methods, which is important for robots operating in pipes as they will be small with limited computational power. A Hidden Markov Model (HMM) based localization method is presented here, with novel incorporation of measured distance travelled. Improvements to the method are presented which use a reduced definition of the robot state to improve computational efficiency and an alternative motion model where the probability of transitioning to each other state is uniform. Simulation in a large realistic map shows that the use of measured distance travelled improves the localization accuracy by around 70%, that the reduction of the state definition gives an reduction in computational requirement by 75% with only a small loss to accuracy dependant on the robot parameters, and that the alternative motion model gives a further improvement to accuracy
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