8 research outputs found

    Improving perception and locomotion capabilities of mobile robots in urban search and rescue missions

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    Nasazení mobilních robotů během zásahů záchranných složek je způsob, jak učinit práci záchranářů bezpečnější a efektivnější. Na roboty jsou ale při takovém použití kladeny vyšší nároky kvůli podmínkám, které při těchto událostech panují. Roboty se musejí pohybovat po nestabilních površích, ve stísněných prostorech nebo v kouři a prachu, což ztěžuje použití některých senzorů. Lokalizace, v robotice běžná úloha spočívající v určení polohy robotu vůči danému souřadnému systému, musí spolehlivě fungovat i za těchto ztížených podmínek. V této dizertační práci popisujeme vývoj lokalizačního systému pásového mobilního robotu, který je určen pro nasazení v případě zemětřesení nebo průmyslové havárie. Nejprve je předveden lokalizační systém, který vychází pouze z měření proprioceptivních senzorů a který vyvstal jako nejlepší varianta při porovnání několika možných uspořádání takového systému. Lokalizace je poté zpřesněna přidáním měření exteroceptivních senzorů, které zpomalují kumulaci nejistoty určení polohy robotu. Zvláštní pozornost je věnována možným výpadkům jednotlivých senzorických modalit, prokluzům pásů, které u tohoto typu robotů nevyhnutelně nastávají, výpočetním nárokům lokalizačního systému a rozdílným vzorkovacím frekvencím jednotlivých senzorů. Dále se věnujeme problému kinematických modelů pro přejíždění vertikálních překážek, což je další zdroj nepřesnosti při lokalizaci pásového robotu. Díky účasti na výzkumných projektech, jejichž členy byly hasičské sbory Itálie, Německa a Nizozemska, jsme měli přístup na cvičiště určená pro přípravu na zásahy během zemětřesení, průmyslových a dopravních nehod. Přesnost našeho lokalizačního systému jsme tedy testovali v podmínkách, které věrně napodobují ty skutečné. Soubory senzorických měření a referenčních poloh, které jsme vytvořili pro testování přesnosti lokalizace, jsou veřejně dostupné a považujeme je za jeden z přínosů naší práce. Tato dizertační práce má podobu souboru tří časopiseckých publikací a jednoho článku, který je v době jejího podání v recenzním řízení.eployment of mobile robots in search and rescue missions is a way to make job of human rescuers safer and more efficient. Such missions, however, require robots to be resilient to harsh conditions of natural disasters or human-inflicted accidents. They have to operate on unstable rough terrain, in confined spaces or in sensory-deprived environments filled with smoke or dust. Localization, a common task in mobile robotics which involves determining position and orientation with respect to a given coordinate frame, faces these conditions as well. In this thesis, we describe development of a localization system for tracked mobile robot intended for search and rescue missions. We present a proprioceptive 6-degrees-of-freedom localization system, which arose from the experimental comparison of several possible sensor fusion architectures. The system was modified to incorporate exteroceptive velocity measurements, which significantly improve accuracy by reducing a localization drift. A special attention was given to potential sensor outages and failures, to track slippage that inevitably occurs with this type of robots, to computational demands of the system and to different sampling rates sensory data arrive with. Additionally, we addressed the problem of kinematic models for tracked odometry on rough terrains containing vertical obstacles. Thanks to research projects the robot was designed for, we had access to training facilities used by fire brigades of Italy, Germany and Netherlands. Accuracy and robustness of proposed localization systems was tested in conditions closely resembling those seen in earthquake aftermath and industrial accidents. Datasets used to test our algorithms are publicly available and they are one of the contributions of this thesis. We form this thesis as a compilation of three published papers and one paper in review process

    Enhanced Subsea Acoustically Aided Inertial Navigation

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    Integrierte Multi-Sensor-Fusion für die simultane Lokalisierung und Kartenerstellung für mobile Robotersysteme

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    In this work, probabilistic methods for combining multiple sensors utilizing multi-sensor fusion for robust and precise localization and mapping in heterogeneous outdoor environments are presented. Aspects of increasing the reliability of landmark recognition are highlighted, as well as the integration of additional absolute and relative sensors using advanced filtering techniques

    Online unscented Rauch-Tung-Striebel smoother for 6DOF vehicle localization

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    Localization and Mapping for Autonomous Driving: Fault Detection and Reliability Analysis

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    Autonomous driving has advanced rapidly during the past decades and has expanded its application for multiple fields, both indoor and outdoor. One of the significant issues associated with a highly automated vehicle (HAV) is how to increase the safety level. A key requirement to ensure the safety of automated driving is the ability of reliable localization and navigation, with which intelligent vehicle/robot systems could successfully make reliable decisions for the driving path or react to the sudden events occurring within the path. A map with rich environment information is essential to support autonomous driving system to meet these high requirements. Therefore, multi-sensor-based localization and mapping methods are studied in this Thesis. Although some studies have been conducted in this area, a full quality control scheme to guarantee the reliability and to detect outliers in localization and mapping systems is still lacking. The quality of the integration system has not been sufficiently evaluated. In this research, an extended Kalman filter and smoother based quality control (EKF/KS QC) scheme is investigated and has been successfully applied for different localization and mapping scenarios. An EKF/KS QC toolbox is developed in MATLAB, which can be easily embedded and applied into different localization and mapping scenarios. The major contributions of this research are: a) The equivalence between least squares and smoothing is discussed, and an extended Kalman filter-smoother quality control method is developed according to this equivalence, which can not only be used to deal with system model outlier with detection, and identification, can also be used to analyse, control and improve the system quality. Relevant mathematical models of this quality control method have been developed to deal with issues such as singular measurement covariance matrices, and numerical instability of smoothing. b) Quality control analysis is conducted for different positioning system, including Global Navigation Satellite System (GNSS) multi constellation integration for both Real Time Kinematic (RTK) and Post Processing Kinematic (PPK), and the integration of GNSS and Inertial Navigation System (INS). The results indicate PPK method can provide more reliable positioning results than RTK. With the proposed quality control method, the influence of the detected outlier can be mitigated by directly correcting the input measurement with the estimated outlier value, or by adapting the final estimation results with the estimated outlier’s influence value. c) Mathematical modelling and quality control aspects for online simultaneous localization and mapping (SLAM) are examined. A smoother based offline SLAM method is investigated with quality control. Both outdoor and indoor datasets have been tested with these SLAM methods. Geometry analysis for the SLAM system has been done according to the quality control results. The system reliability analysis is essential for the SLAM designer as it can be conducted at the early stage without real-world measurement. d) A least squares based localization method is proposed that treats the High-Definition (HD) map as a sensor source. This map-based sensor information is integrated with other perception sensors, which significantly improves localization efficiency and accuracy. Geometry analysis is undertaken with the quality measures to analyse the influence of the geometry upon the estimation solution and the system quality, which can be hints for future design of the localization system. e) A GNSS/INS aided LiDAR mapping and localization procedure is developed. A high-density map is generated offline, then, LiDAR-based localization can be undertaken online with this pre-generated map. Quality control is conducted for this system. The results demonstrate that the LiDAR based localization within map can effectively improve the accuracy and reliability compared to the GNSS/INS only system, especially during the period that GNSS signal is lost

    Contributions to Positioning Methods on Low-Cost Devices

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    Global Navigation Satellite System (GNSS) receivers are common in modern consumer devices that make use of position information, e.g., smartphones and personal navigation assistants. With a GNSS receiver, a position solution with an accuracy in the order of five meters is usually available if the reception conditions are benign, but the performance degrades rapidly in less favorable environments and, on the other hand, a better accuracy would be beneficial in some applications. This thesis studies advanced methods for processing the measurements of low-cost devices that can be used for improving the positioning performance. The focus is on GNSS receivers and microelectromechanical (MEMS) inertial sensors which have become common in mobile devices such as smartphones. First, methods to compensate for the additive bias of a MEMS gyroscope are investigated. Both physical slewing of the sensor and mathematical modeling of the bias instability process are considered. The use of MEMS inertial sensors for pedestrian navigation indoors is studied in the context of map matching using a particle filter. A high-sensitivity GNSS receiver is used to produce coarse initialization information for the filter to decrease the computational burden without the need to exploit local building infrastructure. Finally, a cycle slip detection scheme for stand-alone single-frequency GNSS receivers is proposed. Experimental results show that even a MEMS gyroscope can reach an accuracy suitable for North seeking if the measurement errors are carefully modeled and eliminated. Furthermore, it is seen that even a relatively coarse initialization can be adequate for long-term indoor navigation without an excessive computational burden if a detailed map is available. The cycle slip detection results suggest that even small cycle slips can be detected with mass-market GNSS receivers, but the detection rate needs to be improved

    Deep-Sea Model-Aided Navigation Accuracy for Autonomous Underwater Vehicles Using Online Calibrated Dynamic Models

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    In this work, the accuracy of inertial-based navigation systems for autonomous underwater vehicles (AUVs) in typical mapping and exploration missions up to 5000m depth is examined. The benefit of using an additional AUV motion model in the navigation is surveyed. Underwater navigation requires acoustic positioning sensors. In this work, so-called Ultra-Short-Baseline (USBL) devices were used allowing the AUV to localize itself relative to an opposite device attached to a (surface) vehicle. Despite their easy use, the devices\u27 absolute positioning accuracy decreases proportional to range. This makes underwater navigation a sophisticated estimation task requiring integration of multiple sensors for inertial, orientation, velocity and position measurements. First, error models for the necessary sensors are derived. The emphasis is on the USBL devices due to their key role in navigation - besides a velocity sensor based on the Doppler effect. The USBL model is based on theoretical considerations and conclusions from experimental data. The error models and the navigation algorithms are evaluated on real-world data collected during field experiments in shallow sea. The results of this evaluation are used to parametrize an AUV motion model. Usually, such a model is used only for model-based motion control and planning. In this work, however, besides serving as a simulation reference model, it is used as a tool to improve navigation accuracy by providing virtual measurements to the navigation algorithm (model-aided navigation). The benefit of model-aided navigation is evaluated through Monte Carlo simulation in a deep-sea exploration mission. The final and main contributions of this work are twofold. First, the basic expected navigation accuracy for a typical deep-sea mission with USBL and an ensemble of high-quality navigation sensors is evaluated. Secondly, the same setting is examined using model-aided navigation. The model-aiding is activated after the AUV gets close to sea-bottom. This reflects the case where the motion model is identified online which is only feasible if the velocity sensor is close to the ground (e.g. 100m or closer). The results indicate that, ideally, deep-sea navigation via USBL can be achieved with an accuracy in range of 3-15m w.r.t. the expected root-mean-square error. This also depends on the reference vehicle\u27s position at the surface. In case the actual estimation certainty is already below a certain threshold (ca. <4m), the simulations reveal that the model-aided scheme can improve the navigation accuracy w.r.t. position by 3-12%

    Underwater Vehicles

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    For the latest twenty to thirty years, a significant number of AUVs has been created for the solving of wide spectrum of scientific and applied tasks of ocean development and research. For the short time period the AUVs have shown the efficiency at performance of complex search and inspection works and opened a number of new important applications. Initially the information about AUVs had mainly review-advertising character but now more attention is paid to practical achievements, problems and systems technologies. AUVs are losing their prototype status and have become a fully operational, reliable and effective tool and modern multi-purpose AUVs represent the new class of underwater robotic objects with inherent tasks and practical applications, particular features of technology, systems structure and functional properties
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