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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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Evolutionary Multi-Criterion Optimization 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Carlos M. Fonseca Peter J. Fleming Eckart Zitzler Lothar Thiele Kalyanmoy Deb

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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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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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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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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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Toward the Deployment of an Ultra-Wideband Localization Test Bed

Toward the Deployment of an Ultra-Wideband Localization Test...

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IEEE 74th Vehicular Technology Conference (VTC)

作者： Feldman, Alexander Bahr, Alexander Colli-Vignarelli, James Robert, Stephan Dehollain, Catherine Martinoli, Alcherio Institute for Information and Communication Technologies Haute Ecole d'Ingénierie et de Gestion du Canton de Vaud (HEIG-VD) Switzerland Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland Radio Frequency Integrated Circuit Group Ecole Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781424483273

The design, development, and deployment of Ultra-Wideband (UWB) localization systems involves digital and Radio-Frequency (RF) hardware, embedded software, localization algorithms, security and reliability aspects, electromagnetics, and others. Design and integration decisions affect the performance of an UWB system, in particular the most important metrics: localization accuracy and position update rate. To facilitate further development of UWB localization systems and to analyze some of the major trade-offs we share our experience in deploying the EPFL UWB-Lite test bed (U-LITE). We describe an approach to numerical simulation modeling that can help in the design and evaluation of UWB localization systems. To validate our approach we show experimental results with one transmitter and one receiver. Our UWB test bed includes a mobile robot platform, so we can study and evaluate the UWB performance trade-offs in real-world conditions.

关键词： Accuracy Demodulation Radio frequency Radio transmitters Receivers Robots electromagnetic waves electromagnetics embedded software embedded systems localization algorithms position update rate radiofrequency hardware real world conditions ultra wideband c

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The sliced Gaussian mixture filter for efficient nonlinear estimation

The sliced Gaussian mixture filter for efficient nonlinear e...

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International Conference on Information Fusion

作者： Vesa Klumpp Felix Sawo Uwe D. Hanebeck Dietrich Franken Intelligent Sensor-Actuator-Systems Laboratory Institute of Computer Science and Engineering Universität Karlsruhe Germany Data Fusion Algorithms and Software EADS Deutschland GmbH Ulm Germany

This paper addresses the efficient state estimation for mixed linear/nonlinear dynamic systems with noisy measurements. Based on a novel density representation - sliced Gaussian mixture density - the decomposition into a (conditionally) linear and nonlinear estimation problem is derived. The systematic approximation procedure minimizing a certain distance measure allows the derivation of (close to) optimal and deterministic estimation results. This leads to high-quality representations of the measurement-conditioned density of the states and, hence, to an overall more efficient estimation process. The performance of the proposed estimator is compared to state-of-the-art estimators, like the well-known marginalized particle filter.

关键词： Approximation methods Estimation Particle filters Function approximation Particle measurements Atmospheric measurements Nonlinear dynamical systems

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