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检索条件"机构=Distributed Algorithms and Systems"

共 99 条记录，以下是21-30 订阅

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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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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

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.

关键词： Robot sensing systems Planning Trajectory Collision avoidance Production

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Noise-resistant particle swarm optimization for the learning of robust obstacle avoidance controllers using a depth camera

Noise-resistant particle swarm optimization for the learning...

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Congress on Evolutionary Computation

作者： Inaki Navarro Ezequiel Di Mario Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory École Polytechnique Fédérale de Lausanne

The Ranger robot was designed to interact with children in order to motivate them to tidy up their room. Its mechanical configuration, together with the limited field of view of its depth camera, make the learning of obstacle avoidance behaviors a hard problem. In this article we introduce two new Particle Swarm Optimization (PSO) algorithms designed to address this noisy, high-dimensional optimization problem. Their aim is to increase the robustness of the generated robotic controllers, as compared to previous PSO algorithms. We show that we can successfully apply this set of PSO algorithms to learn 166 parameters of a robotic controller for the obstacle avoidance task. We also study the impact that an increased evaluation budget has on the robustness and average performance of the optimized controllers. Finally, we validate the control solutions learned in simulation by testing the most robust controller in three different real arenas.

关键词： Robot sensing systems Robustness Collision avoidance Mobile robots Wheels Cameras

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Graph-based distributed control for adaptive multi-robot patrolling through local formation transformation

Graph-based distributed control for adaptive multi-robot pat...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Alicja Wasik Jose N. Pereira Rodrigo Ventura Pedro U. Lima Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering école Polytechnique Fédérale de Lausanne Switzerland Institute for Systems and Robotics Instituto Superior Técnico Universidade de Lisboa Portugal

ISBN: (纸本)9781509037636

Multi-robot cooperative navigation in real-world environments is essential in many applications, including surveillance and search-and-rescue missions. State-of-the-art methods for cooperative navigation are often tested in ideal laboratory conditions and not ready to be deployed in real-world environments, which are often cluttered with static and dynamic obstacles. In this work, we explore a graph-based framework to achieve control of real robot formations moving in a world cluttered with a variety of obstacles by introducing a new distributed algorithm for reconfiguring the formation shape. We systematically validate the reconfiguration algorithm using three real robots in scenarios of increasing complexity.

关键词： Navigation Robot kinematics Collision avoidance Shape Robot sensing systems Laplace equations

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An overtaking decision algorithm for networked intelligent vehicles based on cooperative perception

An overtaking decision algorithm for networked intelligent v...

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IEEE Symposium on Intelligent Vehicle

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

ISBN: (纸本)9781509018222

This paper presents an overtaking decision algorithm for networked intelligent vehicles. The algorithm is based on a cooperative tracking and sensor fusion algorithm that we previously developed. The ego vehicle is equipped with lane keeping and lane changing capabilities, as well as a forward-looking lidar sensor. The lidar data are fed to the tracking module which detects other vehicles, such as the vehicle that is to be overtaken (leading) and the oncoming traffic. Based on the estimated distances to the leading and the oncoming vehicles and their speeds, a risk is calculated and a corresponding overtaking decision is made. We compare the performance of the overtaking algorithm between the case when the ego vehicle only relies on its lidar sensor, and the case in which it fuses object estimates received from the leading car which also has a forward-looking lidar. Systematic evaluations are performed in Webots, a calibrated high-fidelity simulator.

关键词： Automobiles Target tracking Laser radar Intelligent vehicles Clustering algorithms Fuses

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Enhancing measurement quality through active sampling in mobile air quality monitoring sensor networks

Enhancing measurement quality through active sampling in mob...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Adrian Arfire Ali Marjovi Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory École Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

ISBN: (纸本)9781509020669

In recent years, a growing number of research groups have targeted the development and deployment of networks using low-cost chemical sensors for monitoring air quality. Due to economical reasoning, most of these systems make use of some sort of mobility to increase spatial coverage. The effect of mobility on measurement quality has, however, been largely neglected. The long response time of the chemical sensors typically used for this type of application, in conjunction with platform mobility, leads to significant signal distortion. While this problem can be addressed through signal deconvolution techniques, their effectiveness is limited by the typical poor Signal-to-Noise Ratio (SNR) of the measured signal. In this paper, we study the possibility of enhancing the measurement quality of chemical sensors through the use of active sampling (or sniffing). We propose different sniffer designs, employing both fans and pumps as actuators. Using a rigorous experimental framework, inside a wind tunnel, we study the ability of active samplers to increase measurement SNR, and thus indirectly to improve sensor dynamic response. We obtain a significant and consistent improvement in SNR for one of our pump-based sniffer designs. Finally, we validate the robustness of this signal enhancement in real-world conditions through an outdoor car-based experiment.

关键词： Robot sensing systems Atmospheric measurements Pollution measurement Fans Mobile communication Signal to noise ratio Air quality

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Characterization and validation of a novel robotic system for fluid-mediated programmable stochastic self-assembly

Characterization and validation of a novel robotic system fo...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Bahar Haghighat Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering école Polytechnique Fédérale de Lausanne Switzerland

ISBN: (纸本)9781509037636

Several self-assembly systems have been developed in recent years, where depending on the capabilities of the building blocks and the controlability of the environment, the assembly process is guided typically through either a fully centralized or a fully distributed control approach. In this work, we present a novel experimental system for studying the range of fully centralized to fully distributed control strategies. The system is built around the floating 3-cm-sized Lily robots, and comprises a water-filled tank with peripheral pumps, an overhead camera, an overhead projector, and a workstation capable of controlling the fluidic flow field, setting the ambient luminosity, communicating with the robots over radio, and visually tracking their trajectories. We carry out several experiments to characterize the system and validate its capabilities. First, a statistical analysis is conducted to show that the system is governed by reaction diffusion dynamics, and validate the applicability of the standard chemical kinetics modeling. Additionally, the natural tendency of the system for structure formation subject to different flow fields is investigated and corresponding implications on guiding the self-assembly process are discussed. Finally, two control approaches are studied: 1) a fully distributed control approach and 2) a distributed approach with additional central supervision exhibiting an improved performance. The formation time statistics are compared and a discussion on the generalization of the method is provided.

关键词： Stochastic processes Wireless communication Robot sensing systems Process control Decentralized control Trajectory

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On-Board Vision-Based 3D Relative Localization System for Multiple Quadrotors

On-Board Vision-Based 3D Relative Localization System for Mu...

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IEEE International Conference on Robotics and Automation

作者： Duarte Dias Rodrigo Ventura Pedro Lima Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering école Polytechnique Fédérale de Lausanne Switzerland

ISBN: (纸本)9781467380270

This work proposes a novel relative localization system, based on active markers and an on-board camera, for tracking multiple quadrotors in a limited field of view. The system extracts the 3D poses of the markers including one that, by pulsating at a predefined frequency, provides an unique platform ID. We discuss how the camera field of view can be explored in presence of multiple targets, and what are the conditions on the system visibility that lead to the establishment of bidirectional sensing between robots with similar sensing capabilities. A visibility analysis is conducted to show that the developed relative localization system meets such requirements, and a closed-loop experiment is used to validate its performance under these conditions. Finally, its performance is compared with other results from the literature, and a metric is established with the intent of mapping different design solutions, facilitating design choices in presence of different requirements.

关键词： Multiple Quadrotors presence different requirements

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A system implementation and evaluation of a cooperative fusion and tracking algorithm based on a Gaussian Mixture PHD filter

A system implementation and evaluation of a cooperative fusi...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： Milos Vasic David Mansolino Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering école Polytechnique Fédérale de Lausanne (EPFL) Switzerland

ISBN: (纸本)9781509037636

This paper focuses on a real system implementation, analysis, and evaluation of a cooperative sensor fusion algorithm based on a Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter, using simulated and real vehicles endowed with automotive-grade sensors. We have extended our previously presented cooperative sensor fusion algorithm with a fusion weight optimization method and implemented it on a vehicle that we denote as the ego vehicle. The algorithm fuses information obtained from one or more vehicles located within a certain range (that we call cooperative), which are running a multi-object tracking PHD filter, and which are sharing their object estimates. The algorithm is evaluated on two Citroen C-ZERO prototype vehicles equipped with Mobileye cameras for object tracking and lidar sensors from which the ground truth positions of the tracked objects are extracted. Moreover, the algorithm is evaluated in simulation using simulated C-ZERO vehicles and simulated Mobileye cameras. The ground truth positions of tracked objects are in this case provided by the simulator. Multiple experimental runs are conducted in both simulated and real-world conditions in which a few legacy vehicles were tracked. Results show that the cooperative fusion algorithm allows for extending the sensing field of view, while keeping the tracking accuracy and errors similar to the case in which the vehicles act alone.

关键词： Vehicles Radar tracking Cameras Sensor fusion Laser radar Algorithm design and analysis Robot sensing systems

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

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

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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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