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International Conference on Distributed Computing in Sensor systems (DCOSS)

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

We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and measure multiple air quality parameters including the Lung Deposited Surface Area (LDSA), a state of the art metric for quantifying human exposure to ultra fine particles. In this paper, our focus is on generating LDSA maps. In particular, since the sensor network coverage is spatially and temporally dynamic, we leverage models to estimate the values for the locations and times where the data are not available. We first discretize the area topologically based on the street segments in the city and we then propose the following three prediction models: i) a log-linear regression model based on nine meteorological (e.g., Temperature and precipitations) and gaseous (e.g., NO 2 and CO) explanatory variables measured at two static stations in the city, ii) a novel network-based log-linear regression model that takes into account the LDSA values of the most correlated streets and also the nine explanatory variables mentioned above, iii) a novel Probabilistic Graphical Model (PGM) in which each street segment is considered as one node of the graph, and inference on conditional joint probability distributions of the nodes results in estimating the values in the nodes of interest. More than 44 millions of geo- and time-stamped LDSA measurements (i.e., More than 14 months of real data) are used in this paper to evaluate the proposed modeling approaches in various time resolutions (hourly, daily, weekly and monthly). The results show that the three approaches bring significant improvements in R2, RMSE and FAC metrics compared to a baseline K-Nearest Neighbor method.

关键词： Atmospheric modeling Pollution measurement Atmospheric measurements Mobile communication Monitoring Air pollution Cities and towns

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Sense in Motion with Belief Clustering: Efficient Gas Source Localization with Mobile Robots

Sense in Motion with Belief Clustering: Efficient Gas Source...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wanting Jin Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Given the patchy nature of gas plumes and the slow response of conventional gas sensors, the use of mobile robots for Gas Source Localization (GSL) tasks presents significant challenges. These aspects increase the difficulties in obtaining gas measurements, encompassing both qualitative and quantitative aspects. Most existing model-based GSL algorithms rely on lengthy stops at each sampling point to ensure accurate gas measurements. However, this approach not only prolongs the time required for a single measurement but also hinders sampling during robot motion, thus exacerbating the scarcity of available gas measurements. In this work, our goal is to push the boundaries in terms of continuity in sampling to enhance system efficiency. Firstly, we decouple and comprehensively evaluate the impact of both plume dynamics and gas sensor properties on the GSL performance. Secondly, we demonstrate that adopting a continuous sampling strategy, which has been generally overlooked in prior research, markedly enhances the system efficiency by obviating the prolonged measurement pauses and leveraging all the data gathered during the robot motion. Thirdly, we further expand the capabilities of the continuous sampling by introducing a novel informative path-planning strategy, which takes into account all the information gathered along the robot's movement. The proposed method is evaluated in both simulation and reality under different scenarios emulating indoor environmental conditions.

关键词： Location awareness Robot motion Robot sensing systems Time measurement Sensors Motion measurement Mobile robots

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Towards Efficient Gas Leak Detection in Built Environments: Data-Driven Plume Modeling for Gas Sensing Robots

Towards Efficient Gas Leak Detection in Built Environments: ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Wanting Jin Faezeh Rahbar Chiara Ercolani Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

The deployment of robots for Gas Source Localization (GSL) tasks in hazardous scenarios significantly reduces the risk to humans and animals. Gas sensing using mobile robots focuses primarily on simplified scenarios, due to the complexity of gas dispersion, with a current trend towards tackling more complex environments. However, most state-of-art GSL algorithms for environments with obstacles only depend on local information, leading to low efficiency in large and more structured spaces. The efficiency of GSL can be improved dramatically by coupling it with a global knowledge of gas distribution in the environment. However, since gas dispersion in a built environment is difficult to model analytically, most previous work incorporating a gas dispersion model was tested under simplified assumptions, which do not take into consideration the impact of the presence of obstacles to the airflow and gas plume. In this paper, we propose a probabilistic algorithm that enables a robot to efficiently localize gas sources in built environments, by combining a state-of-the-art probabilistic GSL algorithm, Source Term Estimation (STE) with a learned plume model. The pipeline of generating gas dispersion datasets from realistic simulations, the training and validation of the model, as well as the integration of the learned model with the STE framework are presented. The performance of the algorithm is validated both in high-fidelity simulations and real experiments, with promising results obtained under various obstacle configurations.

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Multi-Robot 3D Gas Distribution Mapping: Coordination, Information Sharing and Environmental Knowledge

Multi-Robot 3D Gas Distribution Mapping: Coordination, Infor...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Chiara Ercolani Shashank Mahendra Deshmukh Thomas Laurent Peeters Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

Environmental monitoring and mapping operations are an essential tool to combat climate change. An important branch of this domain concerns the construction of reliable gas maps. Adaptive navigation strategies coupled with multi-robot systems improve the outcome of an environmental mapping mission by focusing more efficiently on informative areas. This direction is yet to be explored in the context of gas mapping, which presents peculiar challenges due to the hard-to-sense and expensive-to-model nature of the underlying phenomenon. In this paper, we introduce the application of a multi-robot system to a gas mission with severe time constraints. We study the impact of information-based navigation strategies, coupled with increasing levels of coordination among the robots, on information gathering and consequent map reconstruction performance. We also focus on proposing solutions that inject additional knowledge into the system to enhance the final mapping outcome. We tested the strategies through extensive high-fidelity simulation experiments, and we compared the proposed approaches to three relevant baseline methods.

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Environmental Field Estimation with Hybrid-Mobility Sensor Networks

Environmental Field Estimation with Hybrid-Mobility Sensor N...

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IEEE International Conference on Robotics and Automation

作者： William C. Evans Duarte Dias Steven Roelofsen Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland

ISBN: (纸本)9781467380270

The remarkable accessibility of modern flying robots makes them an attractive platform for environmental sensing. However, low cost and ease of use are currently incompatible with large payloads, severely limiting the choice of sensor and ultimately modality. This paper describes the design of a system for using a small infrared thermometer to estimate the surface temperature over an area that is large compared to the area measured by the sensor, by mounting it on a flying robot. We leverage a priori knowledge about the spatial statistics of the phenomena under measure in order to plan an informative sampling path, fusing observations by Gaussian process regression. Our approach is designed to be evaluated in an indoor testbed, in which a quadrotor, in cooperation with simulated static sensing nodes, estimates the spatial distribution of surface temperature over a controlled thermal gradient. We perform extensive systematic experimentation both in simulation and our real-world testbed environment, with our algorithm estimating surface temperature to an accuracy of up to 2.1 °C over a 16 m~2 area ranging in value from 25-65 °C.

关键词： Estimation Hybrid-Mobility Sensor

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The Sliced Gaussian Mixture Filter with adaptive state decomposition depending on linearization error

The Sliced Gaussian Mixture Filter with adaptive state decom...

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International Conference on Information Fusion

作者： Vesa Klumpp Frederik Beutler Uwe D. Hanebeck Dietrich Franken Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute of Anthropomatics Karlsruhe Institute of Technology Germany Data Fusion Algorithms and Software EADS Deutschland GmbH Ulm Germany

In this paper, a novel nonlinear/nonlinear model decomposition for the Sliced Gaussian Mixture Filter is presented. Based on the level of nonlin-earity of the model, the overall estimation problem is decomposed into a "severely" nonlinear and a "slightly" nonlinear part, which are processed by different estimation techniques. To further improve the efficiency of the estimator, an adaptive state decomposition algorithm is introduced that allows decomposition according to the linearization error for nonlinear system and measurement models. Simulations show that this approach has orders of magnitude less complexity compared to other state of the art estimators, while maintaining comparable estimation errors.

关键词： Approximation methods Adaptation model Estimation Computational modeling Covariance matrix Kalman filters Visualization

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Automatic Multi-Robot Control Design and Optimization Leveraging Multi-Level Modeling: An Exploration Case Study

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IFAC-PapersOnLine 2023年第2期56卷 11462-11469页

作者： Wakana Endo Cyrill Baumann Hajime Asama Alcherio Martinoli Department of Precision Engineering School of Engineering The University of Tokyo Japan Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering École Polytechnique Fédérale de Lausanne (EPFL) Switzerland

In this work, we demonstrate the applicability of a recently proposed automatic synthesis approach for behavioral arbitrators based on Probabilistic Finite State Machines (PFSMs) for a multi-robot scenario. More specifically, a behavior-based controller for a multi-robot exploration scenario is automatically synthesized using a predefined set of basic behaviors and conditions. A key feature of the used synthesis approach is the tailored use of two modeling levels of the scenario, microscopic and submicroscopic, to significantly reduce the computational effort. Furthermore, the modeling is extended by a simplified macroscopic model of the scenario to analytically evaluate the best achievable performance given an ideal controller, taking into account real-world constraints such as limited speed and localization. Taking advantage of the interpretability of the synthesized PFSM-based arbitrators, individualistic and collaborative controllers are analyzed separately to provide insights into the theoretical and experimental effects of collaboration for the considered case study. The obtained results show that the PFSM-based synthesis approach is also suitable for multi-robot scenarios, and in particular that the collaborative solution can compete with a manually designed and fine-tuned solution.

关键词： Multi-Level Modeling Probabilistic Modeling Learning for Control Metaheuristic Optimization Multi-Robot systems Behavior-Based Control

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Flutter suppression of a bridge section model endowed with actively controlled flap arrays

Flutter suppression of a bridge section model endowed with a...

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IEEE/RSJ International Conference on intelligent Robots and systems

作者： M. Boberg G. Feltrin A. Martinoli Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Fédérale de Lausanne Switzerland Structural Engineering Laboratory Swiss Federal Laboratories for Materials Science and Technology Duebendorf Switzerland

In this work, we investigate active flutter control of a bridge section model equipped with arrays of flaps. We consider three simple control algorithms based on an amplitude-gain and a phase-shift for actuating the flaps and stabilizing the section model. We have leveraged a linear analytical model of the structural and aeroelastic forces during flutter in order to find efficient control parameters. The proposed solution was validated with wind tunnel experiments, where all the algorithms showed capable of suppressing flutter, and the most efficient one was using all of the flaps on the deck.

关键词： Bridges Solid modeling Aerodynamics Analytical models Wind speed Damping Mathematical model

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A realistic simulator for the design and evaluation of intelligent vehicles

A realistic simulator for the design and evaluation of intel...

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International Conference on intelligent Transportation

作者： Sven Gowal Yizhen Zhang Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Fédérale de Lausanne Switzerland Applied Materials Inc. Santa Clara CA USA

The number of vehicles hitting the road each day is rapidly increasing, and several problems, such as traffic congestion or driver safety, can no longer be solved in the same fashion as before. intelligent transportation systems could potentially solve part of these problems, but prototyping, designing and testing cooperative smart vehicles is a cumbersome task. This paper presents a realistic simulator, capable of operating both at microscopic and sub-microscopic level, where intelligent vehicles can be designed and analyzed with a pragmatic approach. A number of advances in robotics have already been transferred to vehicular technology, with a potential increase of this trend into the future. Here, we develop a plugin for a well-established robotics simulator (Webots), in order to reinforce at the virtual level this cross-fertilization between the two areas and create a baseline for realistic studies of future solutions in real intelligent vehicles.

关键词： Vehicles Sensors intelligent vehicles Biological system modeling Vehicle dynamics Wheels Engines

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Extension of the Sliced Gaussian Mixture Filter with application to cooperative passive target tracking

Extension of the Sliced Gaussian Mixture Filter with applica...

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International Conference on Information Fusion

作者： Julian Horst Felix Sawo Vesa Klumpp Uwe D. Hanebeck Dietrich Franken Intelligent Sensor-Actuator-Systems Laboratory (ISAS) Institute of Anthropomatics Universität Karlsruhe Germany Data Fusion Algorithms and Software EADS Deutschland GmbH Ulm Germany

ISBN: (纸本)9780982443804

This paper copes with the problem of nonlinear Bayesian state estimation. A nonlinear filter, the sliced Gaussian mixture filter (SGMF), employs linear substructures in the nonlinear measurement and prediction model in order to simplify the estimation process. Here, a special density representation, the sliced Gaussian mixture density, is used to derive an exact solution of the Chapman-Kolmogorov equation. The sliced Gaussian mixture density is obtained by a systematic and deterministic approximation of a continuous density minimizing a certain distance measure. In contrast to previous work, improvements of the SGMF presented here include an extended system model and the processing of multi-dimensional nonlinear subspaces. As an application for the SGMF, cooperative passive target tracking, where sensors take angular measurements from a target, is considered in this paper. Finally, the performance of the proposed estimator is compared to the marginalized particle filter (MPF) in simulations.

关键词： Passive filters Target tracking

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