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European Conference on Mobile Robots (ECMR)

作者： Ali Marjovi Lino Marques EPFL Distributed Intelligent Systems and Algorithms Laboratory Switzerland Institute of Systems and Robotics University of Coimbra Portugal

This paper presents a method for odor plume tracking by a swarm of robots in realistic conditions. In real world environments, the chemical concentration within an odor plume is patchy, intermittent and time-variant. This study shows that swarm robots can cooperatively track the odor plume towards its source by establishing a cohesive spatial sensor network to deal with the turbulences and patchy nature of odor plumes. The robots move together and maintain a distance margin between themselves in order to keep the cohesion of the constructed sensor network while the odor concentration and air-flow speed are considered in the equations of navigation of the robots in the network to more efficiently track the plume. The method is evaluated in simulation against various number of robots, the emission rate of the odor source, the number of obstacles in the environment and the size of the testing environment. The emergent behavior of the swarm proves the functionality, robustness and scalability of the system in different conditions.

关键词： Robot sensing systems Equations Mathematical model Force Olfactory Testing

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Distributed Formation Control of Quadrotors under Limited Sensor Field of View 16

Distributed Formation Control of Quadrotors under Limited Se...

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International Conference on Autonomous Agents and Multiagent systems

作者： Duarte Dias Pedro U. Lima Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory EPFL Institute for Systems and Robotics IST Universidade de Lisboa

ISBN: (纸本)9781510855083

This work tackles the problem of quadrotor formation control, using exclusively on-board resources. Local inter-robot localization systems are typically characterized by limited sensing capabilities, either in range or in the field of view. Most of the existing literature on the subject uses inter-robot communication to obtain the unavailable information, but problems such as communication delays and packet loss can seriously compromise the system stability, especially when the system shows fast dynamics such as that of quadrotors. This work focuses on the sensor field of view limitation: it proposes a formation control algorithm that extends the existing methods to allow each quadrotor to control the occupied area of its sensor field of view, while moving to the right place in the formation. This decreases the situations when necessary inter-robot information is unavailable through local sensing, thus reducing the communication requirements. The system is proven to be stable when this algorithm is applied. Results, both using simulated and real quadrotors, show the correct behavior of the algorithm without the use of communications, even when each robot can only sense a subset of the robots in the group.

关键词： algorithms Experimentation Performance Quadrotors Formation Control Limited Field of View field of view algorithms experimentation Computer algorithms Robot Communication Research Not Available System stability used area Sensors formation

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Predicting missing links using PyKEEN

Predicting missing links using PyKEEN

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2019 ISWC Satellite Tracks (Posters and Demonstrations, Industry, and Outrageous Ideas), ISWC 2019-Satellites

作者： Ali, Mehdi Hoyt, Charles Tapley Domingo-Fernández, Daniel Lehmann, Jens Smart Data Analytics Group University of Bonn Germany Department of Enterprise Information Systems Fraunhofer Institute for Intelligent Analysis and Information Systems Sankt Augustin and Dresden Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing Sankt Augustin Germany

PyKEEN is a framework, which integrates several approaches to compute knowledge graph embeddings (KGEs). We demonstrate the usage of PyKEEN in an biomedical use case, i.e. we trained and evaluated several KGE models on a biological knowledge graph containing genes’ annotations to pathways and pathway hierarchies from well-known databases. We used the best performing model to predict new links and present an evaluation in collaboration with a domain expert*. Copyright 2019 for this paper by its authors.

关键词： Knowledge graph

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Localizing an Odor Source and Avoiding Obstacles: Experiments in a Wind Tunnel using Real Robots

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AIP Conference Proceedings 2009年第1期1137卷 69-72页

作者： Thomas Lochmatter Nicolas Heiniger Alcherio Martinoli Ecole Polytechnique Fédérate de Lausanne (EPFL) Distributed Intelligent Systems and Algorithms Laboratory (DISAL)

We report on real‐robot odor source localization experiments carried out in an environment with obstacles in the odor plume. The robot was equipped with an ethanol sensor and a wind direction sensor, and the experiments were carried out in a wind tunnel (controlled environment). An enhanced version of the surge‐spiral algorithm was used, which was augmented with a dedicate behavior to manage obstacles (avoid them, or follow their contour). We compare the results in terms of distance overhead and success rate, and discuss the impact of obstacles on plume traversal.

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Towards optimally efficient field estimation with threshold-based pruning in real robotic sensor networks

Towards optimally efficient field estimation with threshold-...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Amanda Prorok Christopher M. Cianci Alcherio Martinoli Distributed Intelligent Systems and Algorithms Laboratory Ecole Polytechnique Federale de Lausanne Switzerland

The efficiency of distributed sensor networks depends on an optimal trade-off between the usage of resources and data quality. The work in this paper addresses the problem of optimizing this trade-off in a self-configured distributed robotic sensor network, with respect to a user-defined objective function. We investigate a quadtree network topology and implement a fully distributed threshold-based field estimation algorithm. Simulations with field data as well as real robot experiments are performed, validating our distributed control strategy and evaluating the threshold-based formula for real world scenarios. We propose a theoretical analysis that predicts the system's behavior in real world case studies. The experiments and this prediction show very good correspondence, enabling the accurate employment of the objective function, optimizing the trade-off based on user needs.

关键词： Robot sensing systems Routing Sensor phenomena and characterization Wireless sensor networks Signal processing algorithms Monitoring Robotics and automation Network topology Space technology Clustering algorithms

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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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A parallel multi-objective ab initio approach for protein structure prediction

A parallel multi-objective ab initio approach for protein st...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： David Becerra Angelica Sandoval Daniel Restrepo-Montoya F. Nino Luis Intelligent Systems Research Laboratory (LISI) and the Algorithms and Combinatorics Research Group (ALGOS) National University of Colombia Colombia Intelligent Systems Research Laboratory (LISI) and the Algorithms and Combinatorics Research Group (ALGOS) National University of Colombia

Protein structure prediction is one of the most important problems in bioinformatics and structural biology. This work proposes a novel and suitable methodology to model protein structure prediction with atomic-level detail by using a parallel multi-objective ab initio approach. In the proposed model, i) A trigonometric representation is used to compute backbone and side-chain torsion angles of protein atoms; ii) The Chemistry at HARvard Macromolecular Mechanics (CHARMm) function optimizes and evaluates the structures of the protein conformations; iii) The evolution of protein conformations is directed by optimization of protein energy contributions using the multi-objective genetic algorithm NSGA-II; and iv) The computation process is sped up and its effectiveness improved through the implementation of an island model of the evolutionary algorithm. The proposed model was validated on a set of benchmark proteins obtaining very promising results.

关键词： Proteins Computational modeling Protein engineering Optimization Prediction algorithms Predictive models

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Robust Localization for Multi-robot Formations: An Experimental Evaluation of an Extended GM-PHD Filter 1

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15th International Symposium on Distributed Autonomous Robotic systems, DARS 2021 and 4th International Symposium on Swarm Behavior and Bio-Inspired Robotics, SWARM 2021

作者： Hirayama, Michiaki Wasik, Alicja Kamezaki, Mitsuhiro Martinoli, Alcherio Department of Modern Mechanical Engineering Graduate School of Creative Science and Engineering Waseda University Tokyo Japan Distributed Intelligent Systems and Algorithms Laboratory École Polytechnique Fédérale de Lausanne Lausanne Switzerland

ISBN: (数字)9783030927905

ISBN: (纸本)9783030927899

This paper presents a thorough experimental evaluation of an extended Gaussian Mixture Probability Hypothesis Density filter which is able to provide state estimates for the maintenance of a multi-robot formation, even when the communication fails and the tracking data are insufficient for maintaining a stable formation. The filter incorporates, firstly, absolute poses exchanged by the robots, and secondly, the geometry of the desired formation. By combining communicated data, information about the formation, and sensory detections, the resulting algorithm preserves accuracy in the state estimates despite frequent occurrences of long-duration sensing occlusions, and provides the necessary state information when the communication is sporadic or suffers from short-term outage. Differently from our previous contributions, in which the tracking strategy has only been tested in simulation, in this paper we present the results of experiments with a real multi-robot system. The results confirm that the algorithm enables robust formation maintenance in cluttered environments, under conditions affected by sporadic communication and high measurement uncertainty. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multipurpose robots

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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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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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