817 research outputs found

    Learning Ground Traversability from Simulations

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    Mobile ground robots operating on unstructured terrain must predict which areas of the environment they are able to pass in order to plan feasible paths. We address traversability estimation as a heightmap classification problem: we build a convolutional neural network that, given an image representing the heightmap of a terrain patch, predicts whether the robot will be able to traverse such patch from left to right. The classifier is trained for a specific robot model (wheeled, tracked, legged, snake-like) using simulation data on procedurally generated training terrains; the trained classifier can be applied to unseen large heightmaps to yield oriented traversability maps, and then plan traversable paths. We extensively evaluate the approach in simulation on six real-world elevation datasets, and run a real-robot validation in one indoor and one outdoor environment.Comment: Webpage: http://romarcg.xyz/traversability_estimation

    An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots

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    We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. However, in robotics applications, it is often the case that (potentially very few) examples of anomalies are available. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model, by minimizing, jointly with the Real-NVP loss, an auxiliary outlier exposure margin loss. We perform quantitative experiments on a novel dataset (which we publish as supplementary material) designed for anomaly detection in an indoor patrolling scenario. On a disjoint test set, our approach outperforms alternatives and shows that exposing even a small number of anomalous frames yields significant performance improvements

    Challenges in Visual Anomaly Detection for Mobile Robots

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    We consider the task of detecting anomalies for autonomous mobile robots based on vision. We categorize relevant types of visual anomalies and discuss how they can be detected by unsupervised deep learning methods. We propose a novel dataset built specifically for this task, on which we test a state-of-the-art approach; we finally discuss deployment in a real scenario.Comment: Workshop paper presented at the ICRA 2022 Workshop on Safe and Reliable Robot Autonomy under Uncertainty https://sites.google.com/umich.edu/saferobotautonomy/hom

    Path planning for mobile robots in the real world: handling multiple objectives, hierarchical structures and partial information

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    Autonomous robots in real-world environments face a number of challenges even to accomplish apparently simple tasks like moving to a given location. We present four realistic scenarios in which robot navigation takes into account partial information, hierarchical structures, and multiple objectives. We start by discussing navigation in indoor environments shared with people, where routes are characterized by effort, risk, and social impact. Next, we improve navigation by computing optimal trajectories and implementing human-friendly local navigation behaviors. Finally, we move to outdoor environments, where robots rely on uncertain traversability estimations and need to account for the risk of getting stuck or having to change route

    Charm in Deep-Inelastic Scattering

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    We show how to extend systematically the FONLL scheme for inclusion of heavy quark mass effects in DIS to account for the possible effects of an intrinsic charm component in the nucleon. We show that when there is no intrinsic charm, FONLL is equivalent to S-ACOT to any order in perturbation theory, while when an intrinsic charm component is included FONLL is identical to ACOT, again to all orders in perturbation theory. We discuss in detail the inclusion of top and bottom quarks to construct a variable flavour number scheme, and give explicit expressions for the construction of the structure functions F2cF^c_2, FLcF^c_L and F3cF^c_3 to NNLO