Search

Your search keyword '"Khaluf, Yara"' showing total 122 results
Start Over Author "Khaluf, Yara"
122 results on '"Khaluf, Yara"'

2. Minimalistic Collective Perception with Imperfect Sensors

Author

Chin, Khai Yi, Khaluf, Yara, and Pinciroli, Carlo

Subjects
Computer Science - Robotics
Abstract

Collective perception is a foundational problem in swarm robotics, in which the swarm must reach consensus on a coherent representation of the environment. An important variant of collective perception casts it as a best-of-$n$ decision-making process, in which the swarm must identify the most likely representation out of a set of alternatives. Past work on this variant primarily focused on characterizing how different algorithms navigate the speed-vs-accuracy tradeoff in a scenario where the swarm must decide on the most frequent environmental feature. Crucially, past work on best-of-$n$ decision-making assumes the robot sensors to be perfect (noise- and fault-less), limiting the real-world applicability of these algorithms. In this paper, we derive from first principles an optimal, probabilistic framework for minimalistic swarm robots equipped with flawed sensors. Then, we validate our approach in a scenario where the swarm collectively decides the frequency of a certain environmental feature. We study the speed and accuracy of the decision-making process with respect to several parameters of interest. Our approach can provide timely and accurate frequency estimates even in presence of severe sensory noise., Comment: 7 pages, accepted into IROS2023. Current version incorporates minor updates from reviewer comments

Published
2022

3. An Adaptive Metric Model for Collective Motion Structures in Dynamic Environments

Author

Van Havermaet, Stef, Simoens, Pieter, Khaluf, Yara, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dorigo, Marco, editor, Hamann, Heiko, editor, López-Ibáñez, Manuel, editor, García-Nieto, José, editor, Engelbrecht, Andries, editor, Pinciroli, Carlo, editor, Strobel, Volker, editor, and Camacho-Villalón, Christian, editor

Published
2022
Full Text
View/download PDF

6. Collective Decision-Making on Triadic Graphs

Author

Rausch, Ilja, Khaluf, Yara, Simoens, Pieter, Barbosa, Hugo, editor, Gomez-Gardenes, Jesus, editor, Gonçalves, Bruno, editor, Mangioni, Giuseppe, editor, Menezes, Ronaldo, editor, and Oliveira, Marcos, editor

Published
2020
Full Text
View/download PDF

7. Construction Task Allocation Through the Collective Perception of a Dynamic Environment

Author

Khaluf, Yara, Allwright, Michael, Rausch, Ilja, Simoens, Pieter, Dorigo, Marco, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dorigo, Marco, editor, Stützle, Thomas, editor, Blesa, Maria J., editor, Blum, Christian, editor, Hamann, Heiko, editor, Heinrich, Mary Katherine, editor, and Strobel, Volker, editor

Published
2020
Full Text
View/download PDF

8. Memory Induced Aggregation in Collective Foraging

Author

Nauta, Johannes, Simoens, Pieter, Khaluf, Yara, Goos, Gerhard, Founding Editor, Hartmanis, Juris, Founding Editor, Bertino, Elisa, Editorial Board Member, Gao, Wen, Editorial Board Member, Steffen, Bernhard, Editorial Board Member, Woeginger, Gerhard, Editorial Board Member, Yung, Moti, Editorial Board Member, Dorigo, Marco, editor, Stützle, Thomas, editor, Blesa, Maria J., editor, Blum, Christian, editor, Hamann, Heiko, editor, Heinrich, Mary Katherine, editor, and Strobel, Volker, editor

Published
2020
Full Text
View/download PDF

9. On different communication modalities between foraging robot swarms

Author

Dorigo, Marco, Trianni, Vito, Bersini, Hugues, Birattari, Mauro, Ferrante, Eliseo, Khaluf, Yara, Miletitch, Roman, Dorigo, Marco, Trianni, Vito, Bersini, Hugues, Birattari, Mauro, Ferrante, Eliseo, Khaluf, Yara, and Miletitch, Roman

Abstract

Coordination in a group relies heavily on the type and quality of interactions and communication among individuals. In swarm robotics, communication can make the difference between a heap of isolated robots working independently of each other, and a connected swarm displaying self-organisation. Communication between robots in a swarm can be indirect, for instance through stigmergy whereby robots exploit the sign of previous actions to coordinate, or direct, by means of messages exchanged among robots for the purpose of influencing each other's behaviour. In the latter case, messages can consist either of simple signals, or more structured information, possibly encoding some concept representing features of the environment or of the desired coordination outcome. More complex communication can support more complex self-organising behaviors, deeply impacting on how the swarm tackles the task at hand. In this work, we consider different ways of exploiting communication in the context of a foraging task, in which robots search an open environment for resources to be exploited. Foraging requires abilities such as navigation, exploration and collective decision making. Coordination within a foraging context can lead to higher efficiency in exploiting resources, both in the short or in the long run, by avoiding over-exploitation. Throughout my thesis, foraging is used as a means to study the coupling between different communication processes and the undertaking of a meaningful task by the robots. Specifically, we study three different uses of communication during foraging. Firstly, we focus on simple aggregation of information, and study three parameter-free information processing mechanisms. These result in varying behavior, from the selection of a single resource by the whole swarm to the robots splitting among the resources present. This study is supported by an extensive analysis of navigation and congestion, helping to explain how swarm density can affect the perceived, Doctorat en Sciences de l'ingénieur et technologie, info:eu-repo/semantics/nonPublished

Published
2024

12. Collective Lévy Walk for Efficient Exploration in Unknown Environments

Author

Khaluf, Yara, Van Havermaet, Stef, Simoens, Pieter, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Agre, Gennady, editor, van Genabith, Josef, editor, and Declerck, Thierry, editor

Published
2018
Full Text
View/download PDF

13. The Impact of Interaction Models on the Coherence of Collective Decision-Making: A Case Study with Simulated Locusts

Author

Khaluf, Yara, Rausch, Ilja, Simoens, Pieter, Hutchison, David, Series Editor, Kanade, Takeo, Series Editor, Kittler, Josef, Series Editor, Kleinberg, Jon M., Series Editor, Mattern, Friedemann, Series Editor, Mitchell, John C., Series Editor, Naor, Moni, Series Editor, Pandu Rangan, C., Series Editor, Steffen, Bernhard, Series Editor, Terzopoulos, Demetri, Series Editor, Tygar, Doug, Series Editor, Weikum, Gerhard, Series Editor, Dorigo, Marco, editor, Birattari, Mauro, editor, Blum, Christian, editor, Christensen, Anders L., editor, Reina, Andreagiovanni, editor, and Trianni, Vito, editor

Published
2018
Full Text
View/download PDF

17. Scheduling Access to Shared Space in Multi-robot Systems

Author

Khaluf, Yara, Markarian, Christine, Simoens, Pieter, Reina, Andreagiovanni, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Demazeau, Yves, editor, Davidsson, Paul, editor, Bajo, Javier, editor, and Vale, Zita, editor

Published
2017
Full Text
View/download PDF

19. Minimalistic Collective Perception with Imperfect Sensors

Author

Chin, Khai Yi, Khaluf, Yara, Pinciroli, Carlo, Chin, Khai Yi, Khaluf, Yara, and Pinciroli, Carlo

Abstract

Collective perception is a foundational problem in swarm robotics, in which the swarm must reach consensus on a coherent representation of the environment. An important variant of collective perception casts it as a best-of-n decision-making process, in which the swarm must identify the most likely representation out of a set of alternatives. Past work on this variant primarily focused on characterizing how different algorithms navigate the speed-vs-accuracy tradeoff in a scenario where the swarm must decide on the most frequent environmental feature. Crucially, past work on best-of-n decision-making assumes the robot sensors to be perfect (noise- and fault-less), limiting the real-world applicability of these algorithms. In this paper, we apply optimal estimation techniques and a decentralized Kalman filter to derive, from first principles, a probabilistic framework for minimalistic swarm robots equipped with flawed sensors. Then, we validate our approach in a scenario where the swarm collectively decides the frequency of a certain environmental feature. We study the speed and accuracy of the decision-making process with respect to several parameters of interest. Our approach can provide timely and accurate frequency estimates even in presence of severe sensory noise.

Published
2023

20. Steering herds away from dangers in dynamic environments

Author

Van Havermaet, Stef, Simoens, Pieter, Landgraf, Tim, Khaluf, Yara, Van Havermaet, Stef, Simoens, Pieter, Landgraf, Tim, and Khaluf, Yara

Abstract

Shepherding, the task of guiding a herd of autonomous individuals in a desired direction, is an essential skill to herd animals, enable crowd control and rescue from danger. Equipping robots with the capability of shepherding would allow performing such tasks with increased efficiency and reduced labour costs. So far, only single-robot or centralized multi-robot solutions have been proposed. The former is unable to observe dangers at any place surrounding the herd, and the latter does not generalize to unconstrained environments. Therefore, we propose a decentralized control algorithm for multi-robot shepherding, where the robots maintain a caging pattern around the herd to detect potential nearby dangers. When danger is detected, part of the robot swarm positions itself in order to repel the herd towards a safer region. We study the performance of our algorithm for different collective motion models of the herd. We task the robots to shepherd a herd to safety in two dynamic scenarios: (i) to avoid dangerous patches appearing over time and (ii) to remain inside a safe circular enclosure. Simulations show that the robots are always successful in shepherding when the herd remains cohesive, and enough robots are deployed.

Published
2023

21. Derivation of a Micro-Macro Link for Collective Decision-Making Systems : Uncover Network Features Based on Drift Measurements

Author

Hamann, Heiko, Valentini, Gabriele, Khaluf, Yara, Dorigo, Marco, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Kobsa, Alfred, Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Bartz-Beielstein, Thomas, editor, Branke, Jürgen, editor, Filipič, Bogdan, editor, and Smith, Jim, editor

Published
2014
Full Text
View/download PDF

22. A Swarm Robotics Approach to Task Allocation under Soft Deadlines and Negligible Switching Costs

Author

Khaluf, Yara, Birattari, Mauro, Hamann, Heiko, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Kobsa, Alfred, editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Weikum, Gerhard, editor, Goebel, Randy, editor, Tanaka, Yuzuru, editor, Wahlster, Wolfgang, editor, Siekmann, Jörg, editor, del Pobil, Angel P., editor, Chinellato, Eris, editor, Martinez-Martin, Ester, editor, Hallam, John, editor, Cervera, Enric, editor, and Morales, Antonio, editor

Published
2014
Full Text
View/download PDF

26. Probabilistic Analysis of Long-Term Swarm Performance under Spatial Interferences

Author

Khaluf, Yara, Birattari, Mauro, Rammig, Franz, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Doug, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Dediu, Adrian-Horia, editor, Martín-Vide, Carlos, editor, Truthe, Bianca, editor, and Vega-Rodríguez, Miguel A., editor

Published
2013
Full Text
View/download PDF

33. Robot Swarms Decide under Perception Errors in Best-of-N Problems

Author

Khaluf, Yara and Khaluf, Yara

Abstract

Robot swarms have been used extensively to examine best-of-N decisions; however, most studies presume that robots can reliably estimate the quality values of the various options. In an attempt to bridge the gap to reality, in this study, we assume robots with low-quality sensors take inaccurate measurements in both directions of overestimating and underestimating the quality of available options. We propose the use of three algorithms for allowing robots to identify themselves individually based on both their own measurements and the measurements of their dynamic neighborhood. Within the decision-making process, we then weigh the opinions of robots who define themselves as inaccurately lower than others. Our research compares the classification accuracy of the three algorithms and looks into the swarm’s decision accuracy when the best algorithm for classification is used.

Published
2022

39. ChronoPilot – Modulating Time Perception

Author

European Commission - EC [sponsor], Botev, Jean, Drewing, Knut, Hamann, Heiko, Khaluf, Yara, Simoens, Pieter, Vatakis, Argiro, European Commission - EC [sponsor], Botev, Jean, Drewing, Knut, Hamann, Heiko, Khaluf, Yara, Simoens, Pieter, and Vatakis, Argiro

Published
2021

40. Enhanced foraging in robot swarms using collective levy walks

Author

Nauta, Johannes, Van Havermaet, Stef, Simoens, Pieter, Khaluf, Yara, De Giacomo, G., Catala, A., Dilkina, B., Milano, M., Barro, S., Bugarín, A., and Lang, J.

Subjects
Technology and Engineering, RANDOM SEARCH, STRATEGIES, Life Science
Abstract

A key aspect of foraging in robot swarms is optimizing the search efficiency when both the environment and target density are unknown. Hence, designing optimal exploration strategies is desirable. This paper proposes a novel approach that extends the individual Lévy walk to a collective one. To achieve this, we adjust the individual motion through applying an artificial potential field method originating from local communication. We demonstrate the effectiveness of the enhanced foraging by confirming that the collective trajectory follows a heavy-tailed distribution over a wide range of swarm sizes. Additionally, we study target search efficiency of the proposed algorithm in comparison with the individual Lévy walk for two different types of target distributions: Homogeneous and heterogeneous. Our results highlight the advantages of the proposed approach for both target distributions, while increasing the scalability to large swarm sizes. Finally, we further extend the individual exploration algorithm by adapting the Lévy walk parameter α, altering the motion pattern based on a local estimation of the target density. This adaptive behavior is particularly useful when targets are distributed in patches.

Published
2020

Catalog

Books, media, physical & digital resources
See catalog results