@inproceedings{StrDorFri2024:neuripsworkshop,
	title = {LLM2Swarm: {R}obot Swarms that Responsively Reason, Plan, and Collaborate through LLMs},
	author = {Volker Strobel and Marco Dorigo and Mario Fritz},
	year = {2024},
	booktitle = {NeurIPS 2024 Workshop on Open-World Agents (OWA-2024)},
	url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/StrDorFri2024arxiv.pdf},
	url = {https://arxiv.org/abs/2410.11387},
}

@inproceedings{PaDeVDor-etal2024:dars,
   author = {Pacheco, Alexandre and De Vos, S\'{e}bastien and Dorigo, Marco and Reina, Andreagiovanni and Strobel, Volker},
   title = {Securing Federated Learning in Robot Swarms using Blockchain Technology},
   booktitle = {Distributed Autonomous Robotic Systems (DARS 2024): The 17th International Symposium},
   year = {2024},
   pages = {In press},
   series = {Springer Proceedings in Advanced Robotics},
   url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/PacDeVRei-etal2024dars.pdf},
   url = {https://arxiv.org/abs/2409.01900},
}

@inproceedings{GupStrPac-etal2024:ants,
   author = {Gupta, Himank and Strobel, Volker and Pacheco, Alexandre and Ferrante, Eliseo and Natalizio, Enrico and Dorigo, Marco},
   title = {Group-Level Behavioral Switch in a Robot Swarm Using Blockchain},
   booktitle = {Proceedings of the 14th International Conference on Swarm Intelligence (ANTS 2024)},
   year = {2024},
   publisher = {Springer},
   pages = {98--111},
   doi = {10.1007/978-3-031-70932-6_8},
   url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/GupStrPac-etal2024ants.pdf},   
   url = {https://link.springer.com/chapter/10.1007/978-3-031-70932-6_8}
}

@inproceedings{MorPacStr-etal2024:ants,
   author = {Moroncelli, Angelo and Pacheco, Alexandre and Strobel, Volker and Lajoie, Pierre-Yves and Dorigo, Marco and Reina, Andreagiovanni},
   title = {Byzantine Fault Detection in Swarm-{SLAM} using Blockchain and Geometric Constraints},
   pages = {42--56},
   series = {LNCS},
   volume = {14987},
   booktitle = {Proceedings of the 14th International Conference on Swarm Intelligence (ANTS 2024)},
   publisher = {Springer},
   year = {2024},
   doi = {10.1007/978-3-031-70932-6_4},
   url = {https://link.springer.com/chapter/10.1007/978-3-031-70932-6_4}
}

@misc{PacDenZak-etal2024:arxiv,
      title={Toychain: {A} Simple Blockchain for Research in Swarm Robotics}, 
      author={Alexandre Pacheco and Ulysse Denis and Raina Zakir and Volker Strobel and Andreagiovanni Reina and Marco Dorigo},
      year={2024},
      eprint={2407.06630},
      howpublished ={arXiv},
      primaryClass={cs.RO},
      doi={10.48550/arXiv.2407.06630},
      url={https://arxiv.org/abs/2407.06630},
      url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/PacDenZak-etal2024arxiv.pdf}
}

@article{DorPacReiStr2024:natrevee,
author = {M. Dorigo and A. Pacheco and A. Reina and V. Strobel},
journal = {Nature Reviews Electrical Engineering},
number = {4},
title = {Blockchain technology for mobile multi-robot systems},
volume = {1},
pages={264--274},
year = {2024},
doi={10.1038/s44287-024-00034-9},
url={https://www.nature.com/articles/s44287-024-00034-9}
}

@article{VanPacStr-etal2023:SciRep,
author = {Van Calck, Lud{\'e}ric and Pacheco, Alexandre and Strobel, Volker and Dorigo, Marco and Reina, Andreagiovanni},
journal = {Scientific Reports},
title = {A blockchain-based information market to incentivise cooperation in swarms of self-interested robots},
volume = {13},
pages = {20417},
year = {2023},
doi = {10.1038/s41598-023-46238-1},
url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/VanPacStr-etal2023scirep.pdf}
}

@inproceedings{ZhaPacStr-etal2023:iros,
author = {H. Zhao and A. Pacheco and V. Strobel and A. Reina and X. Liu and G. Dudek and M. Dorigo},
booktitle = {Proceedings of the 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2023)},
publisher = {IEEE Press},
title = {A Generic Framework for Byzantine-tolerant Consensus Achievement in Robot Swarms},
pages = {8839--8846},
year = {2023},
url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/ZhaPacStr-etal2023iros.pdf}
}

@article{StrPacDor2023:scirobotics,
author = {Volker Strobel and Alexandre Pacheco and Marco Dorigo},
title = {Robot swarms neutralize harmful {B}yzantine robots using a blockchain-based token economy},
journal = {Science Robotics},
volume = {8},
number = {79},
pages = {eabm4636},
year = {2023},
doi = {10.1126/scirobotics.abm4636},
url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/StrPacDor2023scirobotics.pdf},
abstract = {Through cooperation, robot swarms can perform tasks or solve problems that a single robot from the swarm could not perform/solve by itself. However, it has been shown that a single Byzantine robot (such as a malfunctioning or malicious robot) can disrupt the coordination strategy of the entire swarm. Therefore, a versatile swarm robotics framework that addresses security issues in inter-robot communication and coordination is urgently needed. Here, we show that security issues can be addressed by setting up a token economy between the robots. To create and maintain the token economy, we used blockchain technology, originally developed for the digital currency Bitcoin. The robots were given crypto tokens that allowed them to participate in the swarm’s security-critical activities. The token economy was regulated via a smart contract that decided how to distribute crypto tokens among the robots depending on their contributions. We designed the smart contract so that Byzantine robots soon ran out of crypto tokens and could therefore no longer influence the rest of the swarm. In experiments with up to 24 physical robots, we demonstrated that our smart contract approach worked: The robots could maintain blockchain networks, and a blockchain-based token economy could be used to neutralize the destructive actions of Byzantine robots in a collective-sensing scenario. In experiments with more than 100 simulated robots, we studied the scalability and long-term behavior of our approach. The obtained results demonstrate the feasibility and viability of blockchain-based swarm robotics. A token economy implemented via blockchain-based smart contracts allowed robot swarms to neutralize harmful Byzantine robots.}}

Through cooperation, robot swarms can perform tasks or solve problems that a single robot from the swarm could not perform/solve by itself. However, it has been shown that a single Byzantine robot (such as a malfunctioning or malicious robot) can disrupt the coordination strategy of the entire swarm. Therefore, a versatile swarm robotics framework that addresses security issues in inter-robot communication and coordination is urgently needed. Here, we show that security issues can be addressed by setting up a token economy between the robots. To create and maintain the token economy, we used blockchain technology, originally developed for the digital currency Bitcoin. The robots were given crypto tokens that allowed them to participate in the swarm’s security-critical activities. The token economy was regulated via a smart contract that decided how to distribute crypto tokens among the robots depending on their contributions. We designed the smart contract so that Byzantine robots soon ran out of crypto tokens and could therefore no longer influence the rest of the swarm. In experiments with up to 24 physical robots, we demonstrated that our smart contract approach worked: The robots could maintain blockchain networks, and a blockchain-based token economy could be used to neutralize the destructive actions of Byzantine robots in a collective-sensing scenario. In experiments with more than 100 simulated robots, we studied the scalability and long-term behavior of our approach. The obtained results demonstrate the feasibility and viability of blockchain-based swarm robotics. A token economy implemented via blockchain-based smart contracts allowed robot swarms to neutralize harmful Byzantine robots.

@inProceedings{PacStrReiDor2022:ants,
   author = {Pacheco, Alexandre and Strobel, Volker and Reina, Andreagiovanni and Dorigo, Marco},
   editor = {Dorigo, Marco and Hamann, Heiko and L\'{o}pez-Ib\'{a}\~{n}ez, Manuel and Garc\'{\i}a-Nieto, Jos\'{e} and Engelbrecht, Andries and Pinciroli, Carlo and Strobel, Volker and Camacho-Villal\'{o}n, Christian},   
   title = {Real-time coordination of a foraging robot swarm using blockchain smart contracts},
   booktitle = {Proceedings of the 13th International Conference on Swarm Intelligence (ANTS 2022)},   
   pages = {196--208},
   series = {LNCS},
   volume = {13491},
   publisher = {Springer},
   address = {Cham, Switzerland},
   year = {2022},
   doi = {10.1007/978-3-031-20176-9_16},
   url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/PacStrReiDor2022ants.pdf},   
}

@InProceedings{PacStrDor2020:ants,
  author={Pacheco, Alexandre and Strobel, Volker and Dorigo, Marco},
  title={A Blockchain-Controlled Physical Robot Swarm Communicating via an Ad-Hoc Network},
  booktitle={Proceedings of the 12th International Conference on Swarm Intelligence (ANTS 2020)},
  editor={Dorigo, Marco and St\"{u}tzle, Thomas and Blesa, Maria J. and Blum, Christian and Hamann, Heiko and Heinrich, Mary Katherine and Strobel, Volker},
  year={2020},
  pages={3--15},
  series={LNCS},
  volume={12421},
  publisher={Springer},  
  address = {Cham, Switzerland},  
  doi={10.1007/978-3-030-60376-2_1},
  url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/PacStrDor2020ants.pdf},     
}

@article{StrCasDor2020:frontiers,
  author={Strobel, Volker and Castell\'o Ferrer, Eduardo and Dorigo, Marco},
  title={Blockchain Technology Secures Robot Swarms: {A} Comparison of Consensus Protocols and Their Resilience to {B}yzantine Robots},
  journal={Frontiers in Robotics and AI},
  volume={7},
  pages={54},
  year={2020},
  url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/StrCasDor2020frontiers.pdf},
  url={https://www.frontiersin.org/article/10.3389/frobt.2020.00054},
  doi={10.3389/frobt.2020.00054},
  issn={2296-9144},
  abstract={Consensus achievement is a crucial capability for robot swarms, for example, for path selection, spatial aggregation, or collective sensing. However, the presence of malfunctioning and malicious robots (Byzantine robots) can make it impossible to achieve consensus using classical consensus protocols. In this work, we show how a swarm of robots can achieve consensus even in the presence of Byzantine robots by exploiting blockchain technology. Bitcoin and later blockchain frameworks, such as Ethereum, have revolutionized financial transactions. These frameworks are based on decentralized databases (blockchains) that can achieve secure consensus in peer-to-peer networks. We illustrate our approach in a collective sensing scenario where robots in a swarm are controlled via blockchain-based smart contracts (decentralized protocols executed via blockchain technology) that serve as “meta-controllers” and we compare it to state-of-the-art consensus protocols using a robot swarm simulator. Additionally, we show that our blockchain-based approach can prevent attacks where robots forge a large number of identities (Sybil attacks). The developed robot-blockchain interface is released as open-source software in order to facilitate future research in blockchain-controlled robot swarms. Besides increasing security, we expect the presented approach to be important for data analysis, digital forensics, and robot-to-robot financial transactions in robot swarms.}
}

Consensus achievement is a crucial capability for robot swarms, for example, for path selection, spatial aggregation, or collective sensing. However, the presence of malfunctioning and malicious robots (Byzantine robots) can make it impossible to achieve consensus using classical consensus protocols. In this work, we show how a swarm of robots can achieve consensus even in the presence of Byzantine robots by exploiting blockchain technology. Bitcoin and later blockchain frameworks, such as Ethereum, have revolutionized financial transactions. These frameworks are based on decentralized databases (blockchains) that can achieve secure consensus in peer-to-peer networks. We illustrate our approach in a collective sensing scenario where robots in a swarm are controlled via blockchain-based smart contracts (decentralized protocols executed via blockchain technology) that serve as “meta-controllers” and we compare it to state-of-the-art consensus protocols using a robot swarm simulator. Additionally, we show that our blockchain-based approach can prevent attacks where robots forge a large number of identities (Sybil attacks). The developed robot-blockchain interface is released as open-source software in order to facilitate future research in blockchain-controlled robot swarms. Besides increasing security, we expect the presented approach to be important for data analysis, digital forensics, and robot-to-robot financial transactions in robot swarms.

@incollection{StrKir2020:bookchapter,
  title="{MyPDDL}: Tools for Efficiently Creating {PDDL} Domains and Problems",
  author="Strobel, Volker and Kirsch, Alexandra",
  editor="Vallati, Mauro and Kitchin, Diane",
  booktitle="Knowledge Engineering Tools and Techniques for AI Planning",
  year="2020",
  publisher="Springer",
  address="Cham, Switzerland",
  pages="67--90",
  abstract="The Planning Domain Definition Language (PDDL) is the state-of-the-art language for specifying planning problems in artificial intelligence research. Writing and maintaining these planning problems, however, can be time-consuming and error- prone. To address this issue, we present myPDDL---a modular toolkit for developing and manipulating PDDL domains and problems. To evaluate myPDDL, we compare its features to existing knowledge engineering tools for PDDL. In a user test, we additionally assess two of its modules, namely the syntax highlighting feature and the type diagram generator. The users of syntax highlighting detected 36{\%} more errors than non-users in an erroneous domain file. The average time on task for questions on a PDDL type hierarchy was reduced by 48{\%} when making the type diagram generator available. This implies that myPDDL can support knowledge engineers well in the PDDL design and analysis process.",
  isbn="978-3-030-38561-3",
  url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/StrKir2020bookchapter.pdf},     
  url="https://doi.org/10.1007/978-3-030-38561-3_4",
  doi="10.1007/978-3-030-38561-3_4",

}

The Planning Domain Definition Language (PDDL) is the state-of-the-art language for specifying planning problems in artificial intelligence research. Writing and maintaining these planning problems, however, can be time-consuming and error- prone. To address this issue, we present myPDDL—a modular toolkit for developing and manipulating PDDL domains and problems. To evaluate myPDDL, we compare its features to existing knowledge engineering tools for PDDL. In a user test, we additionally assess two of its modules, namely the syntax highlighting feature and the type diagram generator. The users of syntax highlighting detected 36% more errors than non-users in an erroneous domain file. The average time on task for questions on a PDDL type hierarchy was reduced by 48% when making the type diagram generator available. This implies that myPDDL can support knowledge engineers well in the PDDL design and analysis process.

@techreport{PacStrDor2020:techreport-001,
  author={Alexandre Pacheco and Volker Strobel and Marco Dorigo},
  title={A Framework for Swarm Robotics Experimentation with Pi-puck Robots and an Ethereum-based Blockchain},
  institution={IRIDIA, Universit{\'e} Libre de Bruxelles},
  year={2020},
  number={TR/IRIDIA/2020-001},
  address={Brussels, Belgium},
  month={February},
  url_PDF={https://iridia.ulb.ac.be/IridiaTrSeries/link/IridiaTr2020-001.pdf},
  url={http://iridia.ulb.ac.be/IridiaTrSeries/history.php?tryear=2020&trsnum=001}  
}

@inproceedings{StrDor2018:ants,
  author = {Strobel, Volker and Dorigo, Marco},
  editor = {Dorigo, Marco and Birattari, Mauro and Blum, Christian and Christensen, Anders L. and Reina, Andreagiovanni and Trianni, Vito},
  title = {Blockchain Technology for Robot Swarms: {A} Shared Knowledge and Reputation Management System for Collective Estimation},
  year = {2018},
  booktitle = {Swarm Intelligence -- Proceedings of ANTS 2018 -- Eleventh International Conference},
  volume = {11172},
  series = {LNCS},
  pages = {425--426},
  publisher = {Springer},
  url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/StrDor2018ants.pdf},
  url = {https://link.springer.com/book/10.1007/978-3-030-00533-7},
  address = {Cham, Switzerland}
}

@inproceedings{StrCasDor2018:aamas,
  author = {Strobel, Volker and Castell{\'o} Ferrer, Eduardo and Dorigo, Marco},
  editor={Dastani, M. and Sukthankar, G. and Andr\'e, E. and Koenig, S.},
  title = {Managing {B}yzantine Robots via Blockchain Technology in a Swarm Robotics Collective Decision Making Scenario},
  year = {2018},
  booktitle= {Proceedings of 17th International Conference on Autonomous Agents and MultiAgent Systems},
  series = {AAMAS '18},
  pages={541--549},
  address = {Richland, SC},
  publisher = {International Foundation for Autonomous Agents and Multiagent Systems},
  url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/StrCasDor2018aamas.pdf},
  url = {https://dl.acm.org/citation.cfm?id=3237464},
  abstract = {While swarm robotics systems are often claimed to be highly fault-tolerant, so far research has limited its attention to safe laboratory settings and has virtually ignored security issues in the presence of Byzantine robots---i.e., robots with arbitrarily faulty or malicious behavior. However, in many applications one or more Byzantine robots may suffice to let current swarm coordination mechanisms fail with unpredictable or disastrous outcomes. In this paper, we provide a proof-of-concept for managing security issues in swarm robotics systems via blockchain technology. Our approach uses decentralized programs executed via blockchain technology (blockchain-based smart contracts) to establish secure swarm coordination mechanisms and to identify and exclude Byzantine swarm members. We studied the performance of our blockchain-based approach in a collective decision-making scenario both in the presence and absence of Byzantine robots and compared our results to those obtained with an existing collective decision approach. The results show a clear advantage of the blockchain approach when Byzantine robots are part of the swarm.}
}

While swarm robotics systems are often claimed to be highly fault-tolerant, so far research has limited its attention to safe laboratory settings and has virtually ignored security issues in the presence of Byzantine robots—i.e., robots with arbitrarily faulty or malicious behavior. However, in many applications one or more Byzantine robots may suffice to let current swarm coordination mechanisms fail with unpredictable or disastrous outcomes. In this paper, we provide a proof-of-concept for managing security issues in swarm robotics systems via blockchain technology. Our approach uses decentralized programs executed via blockchain technology (blockchain-based smart contracts) to establish secure swarm coordination mechanisms and to identify and exclude Byzantine swarm members. We studied the performance of our blockchain-based approach in a collective decision-making scenario both in the presence and absence of Byzantine robots and compared our results to those obtained with an existing collective decision approach. The results show a clear advantage of the blockchain approach when Byzantine robots are part of the swarm.

@inproceedings{StrMeedeC2017:imav,
  author = {Strobel, Volker and Meertens, Roland and de Croon, G. C. H. E.},
  editor = {Moschetta, J.-M. and Hattenberger, G and de Plinval, H.},
  title = {Efficient Global Indoor Localization for Micro Aerial Vehicles},
  year = {2017},
  booktitle = {The 9th International Micro Air Vehicle Conference and Flight Competition (IMAV 2017)},
  pages = {270--277},
  address = {Toulouse, France},
  url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/StrMeedeC2017imav.pdf},
  url = {http://www.imavs.org/imav2017-proceedings/},
  abstract = {Indoor localization for autonomous micro aerial vehicles (MAVs) requires specific localization techniques, since the Global Positioning System (GPS) is usually not available. We present an efficient onboard computer vision approach that estimates 2D positions of an MAV in real-time. This global localization system does not suffer from error accumulation over time and uses a k-Nearest Neighbors (k-NN) algorithm to predict positions based on textons---small characteristic image patches that capture the texture of an environment. A particle filter aggregates the estimates and resolves positional ambiguities. To predict the performance of the approach in a given setting, we developed an evaluation technique that compares environments and identifies critical areas within them. We conducted flight tests to demonstrate the applicability of our approach. The algorithm has a localization accuracy of approximately 0.6 m on a 5 m x 5 m area at a runtime of 32 ms on board of an MAV. Based on random sampling, its computational effort is scalable to different platforms, trading off speed and accuracy.}
}

Indoor localization for autonomous micro aerial vehicles (MAVs) requires specific localization techniques, since the Global Positioning System (GPS) is usually not available. We present an efficient onboard computer vision approach that estimates 2D positions of an MAV in real-time. This global localization system does not suffer from error accumulation over time and uses a k-Nearest Neighbors (k-NN) algorithm to predict positions based on textons—small characteristic image patches that capture the texture of an environment. A particle filter aggregates the estimates and resolves positional ambiguities. To predict the performance of the approach in a given setting, we developed an evaluation technique that compares environments and identifies critical areas within them. We conducted flight tests to demonstrate the applicability of our approach. The algorithm has a localization accuracy of approximately 0.6 m on a 5 m x 5 m area at a runtime of 32 ms on board of an MAV. Based on random sampling, its computational effort is scalable to different platforms, trading off speed and accuracy.

@incollection{StrKir2014:aai,
  author = {Strobel, Volker and Kirsch, Alexandra},
  editor = {Lutz, Carsten and Thielscher, Michael},
  title = {Planning in the Wild: {M}odeling Tools for {PDDL}},
  booktitle = {KI 2014: Advances in Artificial Intelligence},
  pages = {273--284},
  series = {LNCS},
  volume = {8736},
  publisher = {Springer},
  year = {2014},
  address = {Cham, Switzerland},
  url_PDF = {https://iridia.ulb.ac.be/~vstrobel/articles/StrKir2014aai.pdf},
  url = {https://link.springer.com/chapter/10.1007%2F978-3-319-11206-0_27},
  doi = {10.1007/978-3-319-11206-0_27},
  abstract = {Even though there are sophisticated AI planning algorithms, many integrated, large-scale projects do not use planning. One reason seems to be the missing support by engineering tools such as syntax highlighting and visualization. We propose myPDDL---a modular toolbox for efficiently creating PDDL domains and problems. To evaluate myPDDL, we compare it to existing knowledge engineering tools for PDDL and experimentally assess its usefulness for novice PDDL users.}
}

Even though there are sophisticated AI planning algorithms, many integrated, large-scale projects do not use planning. One reason seems to be the missing support by engineering tools such as syntax highlighting and visualization. We propose myPDDL—a modular toolbox for efficiently creating PDDL domains and problems. To evaluate myPDDL, we compare it to existing knowledge engineering tools for PDDL and experimentally assess its usefulness for novice PDDL users.