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IEEE International Conference on Robotics and Automation

作者： Zeynab Talebpour Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory (DISAL) Ecole Polytechnique Federale de Lausanne (EPFL) Lausanne Switzerland

This work addresses multi-robot coordination in social human-populated environments using a market-based framework for solving the Multi-Robot Task Allocation (MRTA) problem. Humans are considered in the proposed coordination mechanism by means of accounting for social costs in bid evaluations and requesting collaboration in socially blocking situations. Initially, the effect of a realistic environment with varying number of static/moving humans on the behavior and performance of our method is studied through an extensive suite of experiments in a high-fidelity simulator. Results show that the total traveled distance and time are increased when humans are present in the environments. Localization noise is also increased particularly in the case of static people. In the second series of experiments, a number of problematic cases resulting in longer modified paths, blocked passages, and long waits have been investigated. A comparative study targeting human-agnostic navigation and planning, human-aware navigation and human-agnostic planning, and human-aware navigation and planning has been conducted. Both simulated and real robot experiments confirm the effectiveness of accounting for humans at both team and individual levels. This leads to respecting social constraints as well as achieving a better performance based on MRTA metrics.

关键词： Robot kinematics Task analysis Navigation Planning Resource management Measurement

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Informed machine learning - A taxonomy and survey of integrating prior knowledge into learning systems

arXiv

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arXiv 2019年

作者： von Rueden, Laura Mayer, Sebastian Beckh, Katharina Georgiev, Bogdan Giesselbach, Sven Heese, Raoul Kirsch, Birgit Pfrommer, Julius Pick, Annika Ramamurthy, Rajkumar Walczak, Michal Garcke, Jochen Bauckhage, Christian Schuecker, Jannis The Fraunhofer IAIS Institute for Intelligent Analysis and Information Systems Sankt Augustin53757 Germany The Fraunhofer SCAI Institute for Algorithms and Scientific Computing Sankt Augustin53757 Germany The Fraunhofer ITWM Institute for Industrial Mathematics Kaiserslautern67663 Germany The Fraunhofer IOSB Institute for Optronics System Technologies and Image Exploitation Karlsruhe76131 Germany

Despite its great success, machine learning can have its limits when dealing with insufficient training data. A potential solution is the additional integration of prior knowledge into the training process which leads to the notion of informed machine learning. In this paper, we present a structured overview of various approaches in this field. We provide a definition and propose a concept for informed machine learning which illustrates its building blocks and distinguishes it from conventional machine learning. We introduce a taxonomy that serves as a classification framework for informed machine learning approaches. It considers the source of knowledge, its representation, and its integration into the machine learning pipeline. Based on this taxonomy, we survey related research and describe how different knowledge representations such as algebraic equations, logic rules, or simulation results can be used in learning systems. This evaluation of numerous papers on the basis of our taxonomy uncovers key methods in the field of informed machine learning. © 2019, CC BY.

关键词： Surveys

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Optimal path planning and coverage control for multi-robot persistent coverage in environments with obstacles

Optimal path planning and coverage control for multi-robot p...

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2017 IEEE International Conference on Robotics and Automation, ICRA 2017

作者： Palacios-Gasos, Jose Manuel Talebpour, Zeynab Montijano, Eduardo Sagues, Carlos Martinoli, Alcherio Universidad de Zaragoza Zaragoza Spain Distributed Intelligent Systems and Algorithms Laboratory Ècole Polytechnique Fédrale de Lausanne Lausanne Switzerland

ISBN: (纸本)9781509046331

Persistent coverage aims to maintain a certain coverage level over time in an environment where such level deteriorates. This level can be associated to temperature, dust or sensor information. We propose an algorithmic solution in which each robot locally finds the best paths and coverage actions to keep the desired coverage level over the whole environment. Using Fast Marching Methods, optimal paths are computed in terms of coverage quality, while keeping a safety distance to obstacles. Additionally, our solution enables a computationally efficient evaluation of a list of potential trajectories, allowing us to choose the one that mostly improves the coverage along the whole path. The combination of this algorithm with a Dynamic Window navigation makes our approach competitive in terms of flexibility and robustness in changing environments with existing solutions. Finally, we also propose a coverage action controller, locally computed and optimal, that makes the robots maintain the coverage level of the environment significantly close to the objective. Simulations and real experiments validate the whole approach. © 2017 IEEE.

关键词： Motion planning

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Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

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Nature Communications 2020年第1期11卷 3966-3966页

作者： Al-Zaiti, Salah Besomi, Lucas Bouzid, Zeineb Faramand, Ziad Frisch, Stephanie Martin-Gill, Christian Gregg, Richard Saba, Samir Callaway, Clifton Sejdić, Ervin Department of Acute & Tertiary Care Nursing University of Pittsburgh Pittsburgh PA United States Department of Emergency Medicine University of Pittsburgh Pittsburgh PA United States Division of Cardiology School of Medicine University of Pittsburgh Pittsburgh PA United States Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA United States University of Pittsburgh Medical Center (UPMC) Pittsburgh PA United States Advanced Algorithms Development Research Center Philips Healthcare Andover MA United States Department of Biomedical Informatics University of Pittsburgh Pittsburgh PA United States Department of Intelligent Systems University of Pittsburgh Pittsburgh PA United States

Prompt identification of acute coronary syndrome is a challenge in clinical practice. The 12-lead electrocardiogram (ECG) is readily available during initial patient evaluation, but current rule-based interpretation approaches lack sufficient accuracy. Here we report machine learning-based methods for the prediction of underlying acute myocardial ischemia in patients with chest pain. Using 554 temporal-spatial features of the 12-lead ECG, we train and test multiple classifiers on two independent prospective patient cohorts (n = 1244). While maintaining higher negative predictive value, our final fusion model achieves 52% gain in sensitivity compared to commercial interpretation software and 37% gain in sensitivity compared to experienced clinicians. Such an ultra-early, ECG-based clinical decision support tool, when combined with the judgment of trained emergency personnel, would help to improve clinical outcomes and reduce unnecessary costs in patients with chest pain. © 2020, The Author(s).

关键词： Acute coronary syndromes Diagnostic markers

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Adaptive Lévy Taxis for odor source localization in realistic environmental conditions

Adaptive Lévy Taxis for odor source localization in realist...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Romain Emery Faezeh Rahbar Ali Marjovi Alcherio Martinoli Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

Odor source localization with mobile robots has recently been subject to many research works, but remains a challenging task mainly due to the large number of environmental parameters that make it hard to describe gas concentration fields. We designed a new algorithm called Adaptive Lévy Taxis (ALT) to achieve odor plume tracking through a correlated random walk. In order to compare its performances with well-established solutions, we have implemented three moth-inspired algorithms on the same robotic platform. To improve the performance of the latter algorithms, we developed a rigorous way to determine one of their key parameters, the odor concentration threshold at which the robot considers to be inside or outside the plume. The methods have been systematically evaluated in a large wind tunnel under various environmental conditions. Experiments revealed that the performance of ALT is consistently good in all environmental conditions (in particular when compared to the three reference algorithms) in terms of both distance traveled to find the source and success rate.

关键词： Robots Public transportation Algorithm design and analysis Hidden Markov models Probability distribution Complexity theory Classification algorithms

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Attentionxml: Label tree-based attention-aware deep model for high-performance extreme multi-label text classification

arXiv

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arXiv 2018年

作者： You, Ronghui Zhang, Zihan Wang, Ziye Dai, Suyang Mamitsuka, Hiroshi Zhu, Shanfeng Shanghai Key Lab of Intelligent Information Processing School of Computer Science Centre for Computational Systems Biology School of Mathematical Sciences Shanghai Institute of Artificial Intelligence Algorithms and ISTBI Fudan University Shanghai China Bioinformatics Center Institute for Chemical Research Kyoto University Japan Department of Computer Science Aalto University Helsinki Finland

Extreme multi-label text classification (XMTC) is an important problem in the era of big data, for tagging a given text with the most relevant multiple labels from an extremely large-scale label set. XMTC can be found in many applications, such as item categorization, web page tagging, and news annotation. Traditionally most methods used bag-of-words (BOW) as inputs, ignoring word context as well as deep semantic information. Recent attempts to overcome the problems of BOW by deep learning still suffer from 1) failing to capture the important subtext for each label and 2) lack of scalability against the huge number of labels. We propose a new label tree-based deep learning model for XMTC, called AttentionXML, with two unique features: 1) a multi-label attention mechanism with raw text as input, which allows to capture the most relevant part of text to each label;and 2) a shallow and wide probabilistic label tree (PLT), which allows to handle millions of labels, especially for "tail labels". We empirically compared the performance of AttentionXML with those of eight state-of-the-art methods over six benchmark datasets, including Amazon-3M with around 3 million labels. AttentionXML outperformed all competing methods under all experimental settings. Experimental results also show that AttentionXML achieved the best performance against tail labels among label tree-based methods. The code and datasets are available at http://***/yourh/AttentionXML . Copyright © 2018, The Authors. All rights reserved.

关键词： Benchmarking

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Collision avoidance with limited field of view sensing: A velocity obstacle approach

Collision avoidance with limited field of view sensing: A ve...

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2017 IEEE International Conference on Robotics and Automation, ICRA 2017

作者： Roelofsen, Steven Gillet, Denis Martinoli, Alcherio Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne United States Coordination and Interaction System Group School of Engineering École Polytechnique Fédérale de Lausanne United States

ISBN: (纸本)9781509046331

Collision avoidance, in particular between robots, is an important component for autonomous robots. It is a necessary component in numerous applications such as humanrobot interaction, automotive or unmanned aerial vehicles. While many collision avoidance algorithms take into account actuation constraints, only a few consider sensing limitations. In this paper, we present a reciprocal collision avoidance algorithm based on the velocity obstacle approach that guarantees collision-free maneuvers even when the robots are only capable to sense their environment within a limited Field Of View (FOV). We also present the challenges associated to sensors with limited FOV, show the conditions under which maneuvering can be safely done, and the modifications that a velocity obstacle approach requires to satisfy such conditions. We provide simulations and real robot experiments to validate our approach. © 2017 IEEE.

关键词： Collision avoidance

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Extending Urban Air Quality Maps Beyond the Coverage of a Mobile Sensor Network: Data Sources, Methods, and Performance Evaluation 17

Extending Urban Air Quality Maps Beyond the Coverage of a Mo...

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Proceedings of the 2017 International Conference on Embedded Wireless systems and Networks

作者： Ali Marjovi Adrian Arfire Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne Lausanne Switzerland

ISBN: (纸本)9780994988614

Targeting the problem of generating high-resolution air quality maps for cities, we leverage four different sources of data: (i) in-situ air quality measurements produced by our mobile sensor network deployed on public transportation vehicles, (ii) explanatory air-quality and meteorological variables obtained from two static monitoring stations, (iii) land-use data of the city, and (iv) traffic statistics. We propose two novel approaches for estimating the targeted pollutant level at desired time-location pairs, extending also to areas of the city that are beyond the coverage of our mobile sensor network. The first is a log-linear regression model which is built over a virtual dependency graph based on land-use data. The second is a deep learning framework that automatically captures the dependencies of the data based on autoencoders. We have evaluated the two proposed approaches against three canonical modeling techniques considering metrics of coefficient of determination (R²), root mean square error (RMSE), and the fraction of predictions within a factor of two of observations (FAC2). Using more than 45 million real measurements in the models, the results show consistently superior performance in respect to the canonical techniques.

关键词：

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Optimal path planning and coverage control for multi-robot persistent coverage in environments with obstacles

Optimal path planning and coverage control for multi-robot p...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： José Manuel Palacios-Gasós Zeynab Talebpour Eduardo Montijano Carlos Sagüés Alcherio Martinoli Instituto de Investigación en Ingeniería de Aragón (I3A) Universidad de Zaragoza Zaragoza Spain Ecole Polytechnique Fédrale de Lausanne Zeynab Talebpour and Alcherio Martinoli are with Distributed Intelligent Systems and Algorithms Laboratory Lausanne Switzerland

关键词： Robot sensing systems Planning Trajectory Collision avoidance Production

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Distributed Formation Control of Quadrotors under Limited Sensor Field of View 16

Distributed Formation Control of Quadrotors under Limited Se...

引用

International Conference on Autonomous Agents and Multiagent systems

作者： Duarte Dias Pedro U. Lima Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory EPFL Institute for Systems and Robotics IST Universidade de Lisboa

ISBN: (纸本)9781510855083

This work tackles the problem of quadrotor formation control, using exclusively on-board resources. Local inter-robot localization systems are typically characterized by limited sensing capabilities, either in range or in the field of view. Most of the existing literature on the subject uses inter-robot communication to obtain the unavailable information, but problems such as communication delays and packet loss can seriously compromise the system stability, especially when the system shows fast dynamics such as that of quadrotors. This work focuses on the sensor field of view limitation: it proposes a formation control algorithm that extends the existing methods to allow each quadrotor to control the occupied area of its sensor field of view, while moving to the right place in the formation. This decreases the situations when necessary inter-robot information is unavailable through local sensing, thus reducing the communication requirements. The system is proven to be stable when this algorithm is applied. Results, both using simulated and real quadrotors, show the correct behavior of the algorithm without the use of communications, even when each robot can only sense a subset of the robots in the group.

关键词： algorithms Experimentation Performance Quadrotors Formation Control Limited Field of View field of view algorithms experimentation Computer algorithms Robot Communication Research Not Available System stability used area Sensors formation

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