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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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Online model estimation of ultra-wideband TDOA measurements for mobile robot localization

Online model estimation of ultra-wideband TDOA measurements ...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Amanda Prorok Lukas Gonon Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédéral de Lausanne Switzerland

Ultra-wideband (UWB) localization is a recent technology that promises to outperform many indoor localization methods currently available. Yet, non-line-of-sight (NLOS) positioning scenarios can create large biases in the time-difference-of-arrival (TDOA) measurements, and must be addressed with accurate measurement models in order to avoid significant localization errors. In this work, we first develop an efficient, closed-form TDOA error model and analyze its estimation characteristics by calculating the Cramér-Rao lower bound (CRLB). We subsequently detail how an online Expectation Maximization (EM) algorithm is adopted to find an elegant formalism for the maximum likelihood estimate of the model parameters. We perform real experiments on a mobile robot equipped with an UWB emitter, and show that the online estimation algorithm leads to excellent localization performance due to its ability to adapt to the varying NLOS path conditions over time.

关键词： Mathematical model Base stations Equations Estimation Measurement uncertainty Robots Position measurement

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Low-cost collaborative localization for large-scale multi-robot systems

Low-cost collaborative localization for large-scale multi-ro...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Amanda Prorok Alexander Bahr Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédéral de Lausanne Switzerland

Large numbers of collaborating robots are advantageous for solving distributed problems. In order to efficiently solve the task at hand, the robots often need accurate localization. In this work, we address the localization problem by developing a solution that has low computational and sensing requirements, and that is easily deployed on large robot teams composed of cheap robots. We build upon a real-time, particle-filter based localization algorithm that is completely decentralized and scalable, and accommodates realistic robot assumptions including noisy sensors, and asynchronous and lossy communication. In order to further reduce this algorithm's overall complexity, we propose a low-cost particle clustering method, which is particularly well suited to the collaborative localization problem. Our approach is experimentally validated on a team of ten real robots.

关键词： Robot kinematics Robot sensing systems Clustering algorithms Complexity theory Collaboration Partitioning algorithms

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Two-phase online calibration for infrared-based inter-robot positioning modules

Two-phase online calibration for infrared-based inter-robot ...

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2011 IEEE/RSJ International Conference on intelligent Robots and systems

作者： Sven Gowal Amanda Prorok Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne Switzerland

Multi-robot systems can solve complex tasks that require the coordination of the team-member positions with respect to each other. While the development of ad-hoc relative positioning platforms embedding cheap off-the-shelf components is a practical choice, it leads not only to differences between the platforms themselves, but also to a high sensitivity to external factors. In this paper, we present a novel lightweight online calibration method composed of two phases, capable of running on miniature robots with limited computational capabilities. Furthermore, by exploiting a Gaussian process regression in its second phase, the proposed calibration approach is able to capture deviations from an assumed underlying physical model. We compare the performance of our approach with the theoretical Cramér-Rao lower bound and test its efficiency on real robots equipped with range and bearing modules.

关键词： Calibration Receivers Mathematical model Robot sensing systems Least squares approximation Ground penetrating radar

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A new collision warning system for lead vehicles in rear-end collisions

A new collision warning system for lead vehicles in rear-end...

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IEEE Symposium on intelligent Vehicle

作者： Adrian Cabrera Sven Gowal Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne Switzerland

Collision Warning systems (CWS) are safety systems designed to warn the driver about an imminent collision. A CWS monitors the dynamic state of the traffic in realtime by processing information from various proprioceptive and exteroceptive sensors. It assesses the potential threat level and decides whether a warning should be issued to the driver through auditory and/or visual signals. Several measures have already been defined for threat assessment and various CWS have been proposed in literature. In this paper, we will focus on two time-based measures that assess both front and rear collision threats. In particular, a new threat metric, the time-to-last-second-acceleration (T lsa ), for lead vehicles in rear-end collision is proposed and compared with its counterpart, the time-to-last-second-braking (T lsb ) [18]. The T lsa is a novel time-based approach that focuses on the lead vehicle (as opposed to the following vehicle). It inherits the properties of the T lsb and, as such, is coherent with the human judgement of urgency and severity of threats. It directly quantifies the threat level of the current dynamic situation before a required evasive action (i.e. maximum acceleration) needs to be applied. Furthermore, different warning thresholds are proposed by considering the average driver reaction time. Its effect on decreasing the severity of a rear-end collision is studied and its reliability is tested using a well-established physics-based robotics simulator, namely Webots [13].

关键词： Vehicles Acceleration Visualization Vehicle dynamics Sensors Equations Mathematical model

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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 reciprocal sampling algorithm for lightweight distributed multi-robot localization

A reciprocal sampling algorithm for lightweight distributed ...

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2011 IEEE/RSJ International Conference on intelligent Robots and systems

作者： Amanda Prorok Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne Switzerland

This work is situated in the context of collaboratively solving the localization problem for unknown initial conditions. We address this problem with a novel, fully decentralized, real-time particle filter algorithm, designed to accommodate realistic robotic assumptions including noisy sensors, and asynchronous and lossy communication. In particular, we introduce a collaborative reciprocal sampling algorithm which allows a drastic reduction in the number of particles needed to achieve localization. We elaborate an analysis of our reciprocal sampling method and support our conclusions with simulation results. Finally, we validate our approach on a team of four real robots within a controlled experimental setup.

关键词： Robot kinematics Manganese Robot sensing systems Mathematical model Collaboration Equations

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Bayesian rendezvous for distributed robotic systems

Bayesian rendezvous for distributed robotic systems

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2011 IEEE/RSJ International Conference on intelligent Robots and systems

作者： Sven Gowal Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory School of Architecture Civil and Environmental Engineering Ecole Polytechnique Fédérale de Lausanne Switzerland

In this paper, we state, using thorough mathematical analysis, sufficient conditions to perform a rendezvous maneuver with a group of differential-wheeled robots endowed with an on-board, noisy, local positioning system. In particular, we extend the existing framework of noise-free, graph-based distributed control with a layer of Bayesian reasoning allowing to solve the rendezvous problem more efficiently in presence of uncertainties and in a probabilistically sound way. Finally we perform extensive experiments with a team of four real robots, and simulation with their corresponding simulated counterpart, to confirm the benefits of our Bayesian approach.

关键词： Nickel Equations Robot sensing systems Noise Noise measurement Mathematical model

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Lumped Drag Model Identification and Real-Time External Force Detection for Rotary-Wing Micro Aerial Vehicles

Lumped Drag Model Identification and Real-Time External Forc...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Lucas Wälti Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory (DISAL) School of Architecture Civil and Environmental Engineering (ENAC) Swiss Federal Institute of Technology (EPFL) Lausanne Switzerland

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

This work focuses on understanding and identifying the drag forces applied to a rotary-wing Micro Aerial Vehicle (MAV). We propose a lumped drag model that concisely describes the aerodynamical forces the MAV is subject to, with a minimal set of parameters. We only rely on commonly available sensor information onboard a MAV, such as accelerometer data, pose estimate, and throttle commands, which makes our method generally applicable. The identification uses an offline gradient-based method on flight data collected over specially designed trajectories. The identified model allows us to predict the aerodynamical forces experienced by the aircraft due to its own motion in real-time and, therefore, will be useful to distinguish them from external perturbations, such as wind or physical contact with the environment. The results show that we are able to identify the drag coefficients of a rotary-wing MAV through onboard flight data and observe the close correlation between the motion of the MAV, the measured external forces, and the predicted drag forces.

关键词： Accelerometers Force measurement Drag Perturbation methods Atmospheric modeling Wind tunnels Real-time systems

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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.

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