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检索条件"机构=Intelligent Systems and Algorithms Laboratory"

共 98 条记录，以下是31-40 订阅

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A distributed noise-resistant Particle Swarm Optimization algorithm for high-dimensional multi-robot learning

A distributed noise-resistant Particle Swarm Optimization al...

引用

IEEE International Conference on Robotics and Automation (ICRA)

作者： Ezequiel Di Mario Iñaki Navarro Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering

ISBN: (纸本)9781479969241

Population-based learning techniques have been proven to be effective in dealing with noise in numerical benchmark functions and are thus promising tools for the high-dimensional optimization of controllers for multiple robots with limited sensing capabilities, which have inherently noisy performance evaluations. In this article, we apply a statistical technique called Optimal Computing Budget Allocation to improve the performance of Particle Swarm Optimization in the presence of noise for a multi-robot obstacle avoidance benchmark task. We present a new distributed PSO OCBA algorithm suitable for resource-constrained mobile robots due to its low requirements in terms of memory and limited local communication. Our results from simulation show that PSO OCBA outperforms other techniques for dealing with noise, achieving a more consistent progress and a better estimate of the ground-truth performance of candidate solutions. We then validate our simulations with real robot experiments where we compare the controller learned with our proposed algorithm to a potential field controller for obstacle avoidance in a cluttered environment. We show that they both achieve a high performance through different avoidance behaviors.

关键词： Robot sensing systems Noise Collision avoidance Benchmark testing Resource management Mobile robots

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Lily: A miniature floating robotic platform for programmable stochastic self-assembly

Lily: A miniature floating robotic platform for programmable...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Bahar Haghighat Emmanuel Droz Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Federale de Lausanne Switzerland

ISBN: (纸本)9781479969241

Fluid-mediated programmable stochastic self-assembly offers promising means to formation of target structures capable of a variety of functionalities. While miniaturized building blocks allow for finer resolutions in such structures, as well as access to unconventional environments, they can only be endowed with very limited on-board resources. In this paper we present the design, fabrication, and experimental results validating the key functionalities of the Lily robot as the building block in a programmable stochastic fluidic self-assembly system, capable of forming 2D structures. In particular, we aim at driving a system including an arbitrary number of Lilies to form target structures through parallel self-assembly, using exclusively local information and communication. While capable of wireless communication to a base station, Lilies are endowed with custom-designed electropermanent magnets to latch and also to communicate locally with their neighbors. Several experiments validate the reliability of the radio channel as well as the robustness of the local induction-based communication which allows for data transfer at 9600 bps with a success rate of 92.8% without repetition. The latches are shown to hold four times the weight of a single robot and to drag in another Lily from a distance of 4 mm in water.

关键词： Robot sensing systems Robot kinematics Latches Base stations Batteries Self-assembly

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SwarmViz: An open-source visualization tool for Particle Swarm Optimization

SwarmViz: An open-source visualization tool for Particle Swa...

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Congress on Evolutionary Computation

作者： Guillaume Jornod Ezequiel Di Mario Iñaki Navarro Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Fédérale de Lausanne

Particle Swarm Optimization (PSO) is a metaheuristic for solving high dimensional optimization problems. Due to the large number of dimensions usually employed with PSO, it is not trivial to visualize and monitor the progress of the algorithm. Because of this, adjusting the parameters that govern the dynamics of the swarm for a specific problem becomes challenging. In this article, we present SwarmViz, an open-source visualization tool for PSO. Through SwarmViz, users are able to set up PSO experiments on canonical benchmark functions or input data from external experiments (e.g., learning robotic controllers), and to visualize the optimization process with state-of-the-art visualization tools. SwarmViz has two main goals. First, to enable researchers to monitor the progress of their specific optimization problem and adjust the relevant PSO parameters. Second, to give a visual insight about PSO to students in the scope of teaching optimization techniques. We demonstrate the features of the software through examples on well-known numerical benchmark functions and a case study on the optimization of a robotic controller.

关键词： Optimization Visualization

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Distributed graph-based convoy control for networked intelligent vehicles

Distributed graph-based convoy control for networked intelli...

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IEEE Symposium on intelligent Vehicle

作者： Ali Marjovi Milos Vasic Joseph Lemaitre Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Lausanne Switzerland

This paper presents an approach for formation control of multi-lane vehicular convoys in highways. We extend a Laplacian graph-based, distributed control law such that networked intelligent vehicles can join or leave the formation dynamically without jeopardizing the ensemble's stability. Additionally, we integrate two essential control behaviors for lane-keeping and obstacle avoidance into the controller. To increase the performance of the convoy controller in terms of formation maintenance and fuel economy, the parameters of the controller are optimized in realistic scenarios using Particle Swarm Optimization (PSO), a powerful metaheuristic optimization method well-suited for large parameter spaces. The performances of the optimized controllers are evaluated in high-fidelity multi-vehicle simulations outlining the efficiency and robustness of the proposed strategy.

关键词： Vehicles Vehicle dynamics Collision avoidance Roads intelligent vehicles Laplace equations

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A Collaborative Sensor Fusion Algorithm for Multi-object Tracking Using a Gaussian Mixture Probability Hypothesis Density Filter

A Collaborative Sensor Fusion Algorithm for Multi-object Tra...

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International Conference on intelligent Transportation

作者： Milos Vasic Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

This paper presents a method for collaborative tracking of multiple vehicles that extends a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter with a collaborative fusion algorithm. Measurements are preprocessed in a detect-before-track fashion, and cars are tracked using a rectangular shape model. The proposed method successfully mitigates clutter and occlusion problems. In order to extend the field of view of individual vehicles and increase the estimation confidence in the areas where a target is observable by multiple vehicles, PHD intensities are exchanged between vehicles and fused in the Collaborative GM-PHD filter using a novel algorithm based on the Generalized Covariance Intersection. The method is extensively evaluated using a calibrated, high-fidelity simulator in scenarios where vehicles exhibit both straight and curved motion at different speeds.

关键词： Collaboration Target tracking Robot sensing systems Automobiles Approximation methods Shape

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Distributed Particle Swarm Optimization - particle allocation and neighborhood topologies for the learning of cooperative robotic behaviors

Distributed Particle Swarm Optimization - particle allocatio...

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IEEE/RSJ International Conference on intelligent Robots and systems

作者： I. Navarro E. Di Mario A. Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Poly technique Federale de Lausanne

In this article we address the automatic synthesis of controllers for the coordinated movement of multiple mobile robots, as a canonical example of cooperative robotic behavior. We use five distributed noise-resistant variations of Particle Swarm Optimization (PSO) to learn in simulation a set of 50 weights of an artificial neural network. They differ on the way the particles are allocated and evaluated on the robots, and on how the PSO neighborhood is implemented. In addition, we use a centralized approach that allows for benchmarking with the distributed versions. Regardless of the learning approach, each robot measures locally and individually the performance of the group using exclusively on-board resources. Results show that four of the distributed variations obtain similar fitnesses as the centralized version, and are always able to learn. The other distributed variation fails to properly learn on some of the runs, and results in lower fitness when it succeeds. We test systematically the controllers learned in simulation in real robot experiments.

关键词： Robot kinematics Mobile robots Robot sensing systems Collision avoidance Measurement Particle swarm optimization

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High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

High Resolution Air Pollution Maps in Urban Environments Usi...

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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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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 distributed formation-based odor source localization algorithm - design, implementation, and wind tunnel evaluation

A distributed formation-based odor source localization algor...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jorge M. Soares A. Pedro Aguiar António M. Pascoal Alcherio Martinoli Laboratory of Robotics and Systems in Engineering and Science University of Lisbon Lisboa Portugal Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Faculty of Engineering University of Porto Research Center for Systems and Technology Porto Portugal

ISBN: (纸本)9781479969241

Robotic odor source localization is a promising tool with numerous applications in safety, search and rescue, and environmental science. In this paper, we present an algorithm for odor source localization using multiple cooperating robots equipped with chemical sensors. Laplacian feedback is employed to maintain the robots in a formation, introducing spatial diversity that is used to better establish the position of the flock relative to the plume and its source. Robots primarily move upwind but use odor information to adjust their position and spacing so that they are centered on the plume and trace its structure. Real-world experiments were performed with an ethanol plume inside a wind tunnel, and used to both validate the algorithm and assess the impact of different formation shapes.

关键词： Robot sensing systems Robot kinematics Algorithm design and analysis Shape Hardware

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Distributed Deconfliction Algorithm for Unmanned Aerial Vehicles with Limited Range and Field of View Sensors

Distributed Deconfliction Algorithm for Unmanned Aerial Vehi...

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American Control Conference

作者： Steven Roelofsen Alcherio Martinoli Denis Gillet Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Federale de Lausanne Coordination and Interaction System Group School of Engineering Ecole Polytechnique Federale de Lausanne

ISBN: (纸本)9781479917730

This paper proposes a novel approach for collision avoidance between Unmanned Aerial Vehicles (UAVs) with limited range and field of view sensors. The algorithm is designed for unicycle vehicles that need to fly above a specific minimal speed to maintain flight. It uses a navigation function approach when the UAV is clear from conflict and smoothly switches to a collision avoidance maneuver when other UAVs are encountered. We show that the proposed avoidance algorithm can ensure a collision-free path. We also carry out a throughout quantitative analysis of the algorithm singularities and propose heuristic recipes for avoiding deadlock situations. Simulations are performed to show the effectiveness of the algorithm.

关键词： Collision avoidance Sensors Aircraft Turning Aircraft navigation Mathematical model

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