@article {yang2025real,
	title = {Real-world Cyber Security Demonstration for Networked Electric Drives},
	journal = {IEEE Journal of Emerging and Selected Topics in Power Electronics},
	year = {2025},
	publisher = {IEEE},
	author = {Yang, He and Yang, Bowen and Coshatt, Stephen and Li, Qi and Hu, Kun and Hammond, Bryan Cooper and Ye, Jin and Parasuraman, Ramviyas and Song, Wenzhan}
}
@conference {zhang2024map2schedule,
	title = {Map2Schedule: An End-to-End Link Scheduling Method for Urban V2V Communications},
	booktitle = {2024 IEEE International Conference on Communications (ICC)},
	year = {2024},
	author = {Zhang, Lihao and Sun, Haijian and Sun, Jin and Parasuraman, Ramviyas and Ye, Yinghui and Hu, Rose Qingyang}
}
@article {488,
	title = {Time-sensitive coverage control for non-holonomic and heterogeneous robots: An extremum seeking framework and application},
	journal = {IET Control Theory \&amp$\mathsemicolon$ Applications},
	year = {2023},
	month = {feb},
	doi = {10.1049/cth2.12434},
	url = {https://doi.org/10.1049\%2Fcth2.12434},
	author = {Liang Zhang and Jinghui Deng and Kun Zhou and Tao Yu and Jun Song and Shuping He}
}
@conference {Alhafnawi2022,
	title = {MOSAIX: a Swarm of Robot Tiles for Social Human-Swarm Interaction},
	booktitle = {2022 International Conference on Robotics and Automation (ICRA)},
	year = {2022},
	pages = {6882-6888},
	doi = {10.1109/ICRA46639.2022.9811723},
	author = {M Alhafnawi and E R Hunt and S Lemaignan and P O{\textquoteright}Dowd and S Hauert}
}
@article {Hunt2022,
	title = {What Could Models of Superorganismal Cognition Offer to Embodied AI?},
	journal = {IOP Conference Series: Materials Science and Engineering},
	volume = {1261},
	year = {2022},
	pages = {012023},
	publisher = {IOP Publishing},
	abstract = {Superorganisms such as ant or honeybee colonies exhibit extraordinary collective intelligence, such as an ability to identify and choose the best available nest site in an uncertain world. This collective cognition is inextricably reliant on the embodiment of individual agents, specifically their movement through space. We have recently developed models of superorganismal cognition based on a compelling analogy with techniques in Bayesian statistics, which are likewise aimed at grappling with the uncertainty and incompleteness of real data sources. These models foreground some potential lessons for the design of embodied artificial intelligences, such as robot swarms. For example, the spatial distribution of independently judging agents can convey valuable information, relaxing expectations that regular inter-agent ({\textquoteleft}inter-neuronal{\textquoteright}) communication is necessary for cognition, which points to the potential of minimal field swarm robotics. Meanwhile, the importance of individual heterogeneity to effective and resilient collective cognition in biology suggests great potential in this area for engineering.},
	issn = {1757-899X},
	doi = {10.1088/1757-899X/1261/1/012023},
	url = {https://dx.doi.org/10.1088/1757-899X/1261/1/012023},
	author = {Edmund R Hunt}
}
@article {Guo2021,
	title = {Semantic Histogram Based Graph Matching for Real-Time Multi-Robot Global Localization in Large Scale Environment},
	journal = {IEEE Robotics and Automation Letters},
	volume = {6},
	year = {2021},
	pages = {8349{\textendash}8356},
	publisher = {IEEE},
	abstract = {The core problem of visual multi-robot simultaneous localization and mapping (MR-SLAM) is how to efficiently and accurately perform multi-robot global localization (MR-GL). The difficulties are two-fold. The first is the difficulty of global localization for significant viewpoint difference. Appearance-based localization methods tend to fail under large viewpoint changes. Recently, semantic graphs have been utilized to overcome the viewpoint variation problem. However, the methods are highly time-consuming, especially in large-scale environments. This leads to the second difficulty, which is how to perform real-time global localization. In this letter, we propose a semantic histogram based graph matching method that is robust to viewpoint variation and can achieve real-time global localization. Based on that, we develop a system that can accurately and efficiently perform MR-GL for both homogeneous and heterogeneous robots. The experimental results show that our approach is about 30 times faster than Random Walk based semantic descriptors. Moreover, it achieves an accuracy of 95\% for global localization, while the accuracy of the state-of-the-art method is 85\%.},
	keywords = {Global localization, Histograms, large viewpoint difference, Location awareness, multi-robot map merging, Real-time systems, semantic SLAM, Semantics, Simultaneous localization and mapping, Three-dimensional displays, Visualization},
	issn = {2377-3774},
	doi = {10.1109/LRA.2021.3058935},
	author = {Xiyue Guo and Junjie Hu and Junfeng Chen and Fuqin Deng and Tin Lun Lam}
}
@article {Hunt2020b,
	title = {The Bayesian Superorganism: Collective Probability Estimation in Swarm Systems},
	journal = {Artificial Life Conference Proceedings},
	volume = {32},
	year = {2020},
	note = {doi: 10.1162/isal_a_00247},
	month = {7},
	pages = {315-323},
	publisher = {MIT Press},
	abstract = {Superorganisms such as social insect colonies are very successful relative to their non-social counterparts. Powerful emergent information processing capabilities would seem to contribute to the abundance of such ?swarm? systems, as they effectively explore and exploit their environment collectively. We develop a Bayesian model of collective information processing in a decision-making task: choosing a nest site (a ?multi-armed bandit? problem). House-hunting Temnothorax ants are adept at discovering and choosing the best available nest site for their colony: we propose that this is possible via rapid, decentralized estimation of the probability that each choice is best. Viewed this way, their behavioral algorithm can be understood as a statistical method that anticipates recent advances in mathematics. Our nest finding model incorporates insights from approximate Bayesian computation as a model of colony-level behavior; and particle filtering as a model of Temnothorax ?tandem running?. Our framework suggests that the mechanisms of complex collective behavior can sometimes be explained as a spatial enactment of Bayesian inference. It facilitates the generation of quantitative hypotheses regarding individual and collective movement behaviors when collective decisions must be made. It also points to the potential for bioinspired statistical techniques. Finally, it suggests simple mechanisms for collective decision-making in engineered systems, such as robot swarms.},
	doi = {10.1162/isal_a_00247},
	url = {https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_00247},
	author = {Edmund R Hunt and Nigel R Franks and Roland J Baddeley}
}
@article {Hunt2020,
	title = {A checklist for safe robot swarms},
	journal = {Nature Machine Intelligence},
	volume = {2},
	year = {2020},
	pages = {420-422},
	abstract = {As robot swarms move from the laboratory to real-world applications, a routine checklist of questions could help ensure their safe operation.},
	issn = {2522-5839},
	doi = {10.1038/s42256-020-0213-2},
	url = {https://doi.org/10.1038/s42256-020-0213-2},
	author = {Edmund R Hunt and Sabine Hauert}
}
@conference {10.1145/3377929.3398078,
	title = {Evolutionary Stress Factors for Adaptable Robot {\textquoteright}Personalities{\textquoteright}},
	booktitle = {Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion},
	year = {2020},
	pages = {1387{\textendash}1388},
	publisher = {Association for Computing Machinery},
	organization = {Association for Computing Machinery},
	abstract = {Many studies on real multi-robot systems are undertaken in laboratory environments, whereas the outside world is unstable and risky. I argue that the phenomenon of phenotypic plasticity provides a bioinspiration framework for making such systems more resilient in the field. Such plasticity can result in consistent individual behavioural differences among group members, which have been described as {\textquoteright}personalities{\textquoteright} in recent years. Personality axes such as shy-bold can be represented by single variables, and may be intuitive in the context of human-robot interaction. However, for plasticity to emerge in artificial evolution, simulated environments need to be more heterogeneous and unpredictable, as in real-world natural history. Behavioral Reaction Norms (BRNs) from developmental biology are a useful concept for the transparent mapping of relevant sensory inputs to robot behavioral change. In this position paper, I identify candidate stress factors in simulated environments for artificial evolution of adaptive BRNs. I also consider some potential benefits and costs of this approach.},
	keywords = {field robotics, personality, phenotypic plasticity, reaction norms},
	isbn = {9781450371278},
	doi = {10.1145/3377929.3398078},
	url = {https://doi.org/10.1145/3377929.3398078},
	author = {Edmund R Hunt}
}
@booklet {Hunt2020,
	title = {Phenotypic Plasticity Provides a Bioinspiration Framework for Minimal Field Swarm Robotics},
	howpublished = {Frontiers in Robotics and AI},
	volume = {7},
	year = {2020},
	pages = {23},
	abstract = {The real world is highly variable and unpredictable, and so fine-tuned robot controllers that successfully result in group-level {\textquotedblleft}emergence{\textquotedblright} of swarm capabilities indoors may quickly become inadequate outside. One response to unpredictability could be greater robot complexity and cost, but this seems counter to the {\textquotedblleft}swarm philosophy{\textquotedblright} of deploying (very) large numbers of simple agents. Instead, here I argue that bioinspiration in swarm robotics has considerable untapped potential in relation to the phenomenon of phenotypic plasticity: when a genotype can produce a range of distinctive changes in organismal behavior, physiology and morphology in response to different environments. This commonly arises following a natural history of variable conditions; implying the need for more diverse and hazardous simulated environments in offline, pre-deployment optimization of swarms. This will generate{\textemdash}indicate the need for{\textemdash}plasticity. Biological plasticity is sometimes irreversible; yet this characteristic remains relevant in the context of minimal swarms, where robots may become mass-producible. Plasticity can be introduced through the greater use of adaptive threshold-based behaviors; more fundamentally, it can link to emerging technologies such as smart materials, which can adapt form and function to environmental conditions. Moreover, in social animals, individual heterogeneity is increasingly recognized as functional for the group. Phenotypic plasticity can provide meaningful diversity {\textquotedblleft}for free{\textquotedblright} based on early, local sensory experience, contributing toward better collective decision-making and resistance against adversarial agents, for example. Nature has already solved the challenge of resilient self-organisation in the physical realm through phenotypic plasticity: swarm engineers can follow this lead.},
	isbn = {2296-9144},
	doi = {10.3389/frobt.2020.00023},
	url = {https://www.frontiersin.org/article/10.3389/frobt.2020.00023},
	author = {Edmund R Hunt}
}
@article {Hunt2020a,
	title = {SPIDER: a Bioinspired Swarm Algorithm for Adaptive Risk-Taking},
	journal = {Artificial Life Conference Proceedings},
	volume = {32},
	year = {2020},
	note = {doi: 10.1162/isal_a_00279},
	month = {7},
	pages = {44-51},
	publisher = {MIT Press},
	abstract = {Robot swarms can solve tasks that are impossible or too hazardous for single robots. For example, following a nuclear radiation leak, a user may wish to establish a distributed communication chain that partly extends into the most dangerous areas to gather new information. The challenge is to create long chains while maintaining chain connectivity (?connected reach?), where those at the distant end of the chain are more likely to be disconnected. Here we take the concept of dynamic ?boldness? levels from animal behavior (Stegodyphus social spiders) to explore such risky environments in a way that adapts to the size of the group. Boldness is implemented as a continuous variable associated with the risk appetite of individuals to explore regions more distant from a central base. We present a decentralized mechanism for robots, based on the frequency of their social interactions, to adaptively take on ?bold? and ?shy? behaviors. Using this new bioinspired algorithm, which we call SPIDER, swarms are shown to adapt rapidly to the loss of bold individuals by regenerating a suitable shy?bold distribution, with fewer bolder individuals in smaller groups. This allows them to dynamically trade-off the benefits and costs of long chains (information retrieval versus loss of robots) and demonstrates the particular advantage of this approach in hazardous or adversarial environments.},
	doi = {10.1162/isal_a_00279},
	url = {https://www.mitpressjournals.org/doi/abs/10.1162/isal_a_00279},
	author = {Edmund R Hunt and George Jenkinson and Michael Wilsher and Carl P Dettmann and Sabine Hauert}
}
@article {350,
	title = {Persistent and Robust Execution of MAPF Schedules in Warehouses},
	journal = {IEEE Robotics and Automation Letters},
	volume = {4},
	year = {2019},
	month = {Jan-04-2019},
	pages = {1125 - 1131},
	doi = {10.1109/LRA.2019.2894217},
	url = {https://ieeexplore.ieee.org/document/8620328/https://ieeexplore.ieee.org/ielam/7083369/8581687/8620328-aam.pdfhttp://xplorestaging.ieee.org/ielx7/7083369/8581687/08620328.pdf?arnumber=8620328},
	author = {Honig, Wolfgang and Kiesel, Scott and Tinka, Andrew and Durham, Joseph W. and Ayanian, Nora}
}
@article {Hunt2019,
	title = {Testing the limits of pheromone stigmergy in high-density robot swarms},
	journal = {Royal Society Open Science},
	volume = {6},
	year = {2019},
	pages = {190225},
	doi = {10.1098/rsos.190225},
	author = {Edmund R Hunt and Simon Jones and Sabine Hauert}
}
@conference {419,
	title = {Autonomous Marine Sampling Enhanced by Strategically Deployed Drifters in Marine Flow Fields},
	booktitle = {OCEANS 2018 MTS/IEEE Charleston},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Charleston, SC},
	doi = {10.1109/OCEANS.2018.8604873},
	url = {https://ieeexplore.ieee.org/document/8604873/},
	author = {Hansen, Johanna and Manjanna, Sandeep and Quattrini~Li, Alberto and Rekleitis, Ioannis and Dudek, Gregory}
}
@conference {363,
	title = {Trajectory Planning for Heterogeneous Robot Teams},
	booktitle = {2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
	year = {2018},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Madrid},
	doi = {10.1109/IROS.2018.8593876},
	url = {https://ieeexplore.ieee.org/document/8593876/http://xplorestaging.ieee.org/ielx7/8574473/8593358/08593876.pdf?arnumber=8593876},
	author = {Debord, Mark and Honig, Wolfgang and Ayanian, Nora}
}
@article {351,
	title = {Trajectory Planning for Quadrotor Swarms},
	journal = {IEEE Transactions on Robotics},
	volume = {34},
	year = {2018},
	month = {Jan-08-2018},
	pages = {856 - 869},
	issn = {1552-3098},
	doi = {10.1109/TRO.2018.2853613},
	url = {https://ieeexplore.ieee.org/document/8424034/http://xplorestaging.ieee.org/ielx7/8860/8437110/08424034.pdf?arnumber=8424034},
	author = {Honig, Wolfgang and Preiss, James A. and Kumar, T. K. Satish and Sukhatme, Gaurav S. and Ayanian, Nora}
}
@article {haseeb2018wisture,
	title = {Wisture: Touch-less hand gesture classification in unmodified smartphones using Wi-Fi signals},
	journal = {IEEE Sensors Journal},
	volume = {19},
	number = {1},
	year = {2018},
	pages = {257{\textendash}267},
	publisher = {IEEE},
	author = {Haseeb, Mohamed Abudulaziz Ali and Parasuraman, Ramviyas}
}
@conference {334,
	title = {Collaborative Sampling Using Heterogeneous Marine Robots Driven by Visual Cues},
	booktitle = {Proceedings of the Conference on Computer and Robot Vision (CRV)},
	year = {2017},
	abstract = {This paper addresses distributed data sampling in marine environments using robotic devices. We present a method to strategically sample locally observable features using two classes of sensor platforms. Our system consists of a sophisticated autonomous surface vehicle (ASV) which strategically samples based on information provided by a team of inexpensive sensor nodes. The sensor nodes effectively extend the observational capabilities of the vehicle by capturing georeferenced samples from disparate and moving points across the region. The ASV uses this information, along with its own observations, to plan a path so as to sample points which it expects to be particularly informative. We compare our approach to a traditional exhaustive survey approach and show that we are able to effectively represent a region with less energy expenditure. We validate our approach through simulations and test the system on real robots in field.},
	doi = {10.1109/CRV.2017.49},
	author = {Sandeep Manjanna and Johanna Hansen and Quattrini~Li, Alberto and Ioannis Rekleitis and Gregory Dudek}
}
@conference {357,
	title = {Crazyswarm: A large nano-quadcopter swarm},
	booktitle = {2017 IEEE International Conference on Robotics and Automation (ICRA)2017 IEEE International Conference on Robotics and Automation (ICRA)},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Singapore, Singapore},
	doi = {10.1109/ICRA.2017.7989376},
	url = {http://ieeexplore.ieee.org/document/7989376/http://xplorestaging.ieee.org/ielx7/7960754/7988677/07989376.pdf?arnumber=7989376},
	author = {Preiss, James A. and Honig, Wolfgang and Sukhatme, Gaurav S. and Ayanian, Nora}
}
@conference {355,
	title = {Downwash-aware trajectory planning for large quadrotor teams},
	booktitle = {2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
	year = {2017},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Vancouver, BC},
	doi = {10.1109/IROS.2017.8202165},
	url = {http://ieeexplore.ieee.org/document/8202165/http://xplorestaging.ieee.org/ielx7/8119304/8202121/08202165.pdf?arnumber=8202165},
	author = {Preiss, James A. and Honig, Wolfgang and Ayanian, Nora and Sukhatme, Gaurav S.}
}
@inbook {354,
	title = {Flying Multiple UAVs Using ROS},
	volume = {707},
	year = {2017},
	pages = {83 - 118},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	address = {Cham},
	isbn = {978-3-319-54926-2},
	issn = {1860-949X},
	doi = {10.1007/978-3-319-54927-910.1007/978-3-319-54927-9_3},
	author = {Honig, Wolfgang and Ayanian, Nora},
	editor = {Koubaa, Anis}
}
@article {353,
	title = {Overview: A Hierarchical Framework for Plan Generation and Execution in Multirobot Systems},
	journal = {IEEE Intelligent Systems},
	volume = {32},
	year = {2017},
	month = {Jan-11-2017},
	pages = {6 - 12},
	issn = {1541-1672},
	doi = {10.1109/MIS.2017.4531217},
	url = {http://ieeexplore.ieee.org/document/8268002/http://xplorestaging.ieee.org/ielx7/9670/8267591/08268002.pdf?arnumber=8268002},
	author = {Ma, Hang and Honig, Wolfgang and Cohen, Liron and Uras, Tansel and Xu, Hong and Kumar, T.K. Satish and Ayanian, Nora and Koenig, Sven}
}
@booklet {abdulaziz2017passive,
	title = {Passive gesture recognition on unmodified smartphones using Wi-Fi RSSI},
	year = {2017},
	author = {Abdulaziz Ali Haseeb, Mohamed}
}
@conference {356,
	title = {Summary: Multi-Agent Path Finding with Kinematic Constraints},
	booktitle = {Twenty-Sixth International Joint Conference on Artificial IntelligenceProceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence},
	year = {2017},
	publisher = {International Joint Conferences on Artificial Intelligence Organization},
	organization = {International Joint Conferences on Artificial Intelligence Organization},
	address = {Melbourne, AustraliaCalifornia},
	doi = {10.24963/ijcai.201710.24963/ijcai.2017/684},
	url = {https://www.ijcai.org/proceedings/2017https://www.ijcai.org/proceedings/2017/684},
	author = {Honig, Wolfgang and Kumar, T. K. Satish and Cohen, Liron and Ma, Hang and Xu, Hong and Ayanian, Nora and Koenig, Sven}
}
@article {haseeb2017wisture,
	title = {Wisture: RNN-based learning of wireless signals for gesture recognition in unmodified smartphones},
	journal = {arXiv preprint arXiv:1707.08569},
	year = {2017},
	author = {Haseeb, Mohamed Abudulaziz Ali and Parasuraman, Ramviyas}
}
@conference {335,
	title = {Data Correlation and Comparison from Multiple Sensors over a Coral Reef with a Team of Heterogeneous Aquatic Robots},
	booktitle = {International Symposium on Experimental Robotics (ISER)},
	year = {2016},
	abstract = {This paper presents experimental insights from the deployment of an ensemble of heterogeneous autonomous sensor systems over a shallow coral reef. Visual, inertial, GPS, and ultrasonic data collected are compared and correlated to produce a comprehensive view of the health of the coral reef. Coverage strategies are discussed with a focus on the use of informed decisions to maximize the information collected during a fixed period of time.},
	doi = {10.1007/978-3-319-50115-4_62},
	author = {Quattrini~Li, Alberto and Ioannis Rekleitis and Sandeep Manjanna and Nikhil Kakodkar and Johanna Hansen and Gregory Dudek and Leonardo Bobadilla and Jacob Anderson and Ryan N. Smith}
}
@conference {358,
	title = {Formation change for robot groups in occluded environments},
	booktitle = {2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
	year = {2016},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Daejeon, South Korea},
	doi = {10.1109/IROS.2016.7759710},
	url = {http://ieeexplore.ieee.org/document/7759710/http://xplorestaging.ieee.org/ielx7/7743711/7758082/07759710.pdf?arnumber=7759710},
	author = {Honig, Wolfgang and Kumar, T. K. Satish and Ma, Hang and Koenig, Sven and Ayanian, Nora}
}
@article {165,
	title = {Model and analysis of the interaction dynamics in cooperative manipulation tasks},
	journal = {IEEE Transactions on Robotics (T-RO), accepted},
	year = {2016},
	url = {https://mediatum.ub.tum.de/doc/1276284/132134.pdf},
	author = {S. Erhart and S. Hirche}
}
@conference {188,
	title = {Multi-Agent Path Finding with Kinematic Constraints},
	booktitle = {International Conference on Automated Planning and Scheduling},
	year = {2016},
	month = {Jun},
	address = {London, UK},
	keywords = {Multi-Agent Path Finding with Kinematic Contraints},
	author = {Wolfgang H{\"o}nig and T. K. Satish Kumar and Liron Cohen and Hang Ma and Hong Xu and Nora Ayanian and Sven Koenig}
}
@conference {384,
	title = {Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios},
	booktitle = {IJCAI-16 Workshop on Multi-Agent Path Finding (WOMPF)},
	year = {2016},
	keywords = {workshop},
	author = {Hang Ma and Sven Koenig and Nora Ayanian and Liron Cohen and Wolfgang H{\"o}nig and T. K. Satish Kumar and Tansel Uras and Hong Xu and C. Tovey and G. Sharon}
}
@conference {264,
	title = {Scalable value approximation for multiple target tail-chase with collision avoidance},
	booktitle = {2016 IEEE 55th Conference on Decision and Control (CDC)},
	year = {2016},
	month = {Dec},
	keywords = {Cost function, Dynamic programming, Games, Hazards, Markov processes, Mathematical model, Optimal control},
	doi = {10.1109/CDC.2016.7798645},
	author = {A. Hashemi and D. Milutinovi{\'c} and D. W. Casbeer}
}
@article {189,
	title = {Cooperative Control for Target Tracking with Onboard Sensing},
	journal = {International Journal of Robotics Research},
	volume = {34},
	year = {2015},
	month = {November},
	pages = {1660-1677},
	author = {Karol Hausman and Joerg Mueller and Abishek Hariharan and Nora Ayanian and Gaurav Sukhatme}
}
@conference {158,
	title = {Dynamic Load Distribution in Cooperative Manipulation Tasks},
	booktitle = {Proceedings of the 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2015)},
	year = {2015},
	address = {Hamburg, Germany},
	doi = {http://dx.doi.org/10.1109/IROS.2015.7353699},
	author = {A. Zambelli and S. Erhart and L. Zaccarian and S. Hirche}
}
@article {157,
	title = {Internal force analysis and load distribution for cooperative multi-robot manipulation},
	journal = {IEEE Transactions on Robotics (T-RO)},
	volume = {31},
	year = {2015},
	pages = {1238 - 1243},
	doi = {10.1109/TRO.2015.2459412},
	author = {S. Erhart and S. Hirche}
}
@conference {192,
	title = {Mixed Reality for Robotics},
	booktitle = {IEEE/RSJ Intl Conf. Intelligent Robots and Systems},
	year = {2015},
	month = {Sept},
	address = {Hamburg, Germany},
	author = {Wolfgang Hoenig and Christina Milanes and Lisa Scaria and Thai Phan and Mark Bolas and Nora Ayanian}
}
@conference {360,
	title = {Mixed reality for robotics},
	booktitle = {2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
	year = {2015},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Hamburg, Germany},
	doi = {10.1109/IROS.2015.7354138},
	url = {http://ieeexplore.ieee.org/document/7354138/http://xplorestaging.ieee.org/ielx7/7347169/7353104/07354138.pdf?arnumber=7354138},
	author = {Honig, Wolfgang and Milanes, Christina and Scaria, Lisa and Phan, Thai and Bolas, Mark and Ayanian, Nora}
}
@conference {159,
	title = {Multi-robot manipulation controlled by a human with haptic feedback},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
	year = {2015},
	abstract = {The interaction of a single human with a team of cooperative robots, which collaboratively manipulate an object, poses a great challenge by means of the numerous possibilities of issuing commands to the team or providing appropriate feedback to the human. In this paper we propose a formationbased approach in order to avoid deformations of the object and to virtually couple the human to the formation. Here the human can be interpreted as a leader in a leader-follower formation with the robotic manipulators being the followers. The results of a controllability analysis in such a leader-follower formation suggest that it is beneficial to measure the state of the human (leader) by all physically cooperating manipulators (followers). The proposed approach is evaluated in a full-scale multi-robot cooperative manipulation experiment with humans.},
	author = {D. Sieber and S. Music and S. Hirche}
}
@conference {151,
	title = {Network Entropy: A System-Theoretic Perspective},
	booktitle = {Conference on Decision and Control},
	year = {2015},
	author = {Mathias Hudoba de Badyn and Chapman, Airlie and Mesbahi, Mehran}
}
@conference {HausmanCooperative2014,
	title = {Cooperative Control for Target Tracking with Onboard Sensing},
	booktitle = {International Symposium of Robotics Research},
	year = {2014},
	month = {Jun},
	address = {Morocco},
	author = {Karol Hausman and Joerg Mueller and Abishek Hariharan and Nora Ayanian and Gaurav Sukhatme}
}
@conference {160,
	title = {Dynamic Movement Primitives for Cooperative Manipulation and Synchronized Motions},
	booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
	year = {2014},
	author = {J. Umlauft and D. Sieber and S. Hirche}
}
@conference {164,
	title = {Adaptive Force/Velocity Control for Multi-Robot Cooperative Manipulation under Uncertain Kinematic Parameters},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
	year = {2013},
	doi = {10.1109/IROS.2013.6696369},
	url = {;},
	author = {S. Erhart and S. Hirche}
}
@conference {162,
	title = {Formation-based approach for multi-robot cooperative manipulation based on optimal control design},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
	year = {2013},
	abstract = {Cooperative manipulation, where several robots collaboratively transport an object, poses a great challenge in robotics. In order to avoid object deformations in cooperative manipulation, formation rigidity of the robots is desired. This work proposes a novel linear state feedback controller that combines both optimal goal regulation and a relaxed form of the formation rigidity constraint, exploiting an underlying distributed impedance control scheme. Since the presented control design problem is in a biquadratic LQR-like form, we present an iterative design algorithm to compute the controller. As an intermediate result, an approximated state-space model of an interconnected robot system is derived. The controller design approach is evaluated in a full-scale multi-robot experiment.},
	url = {;},
	author = {D. Sieber and F. Deroo and S. Hirche}
}
@conference {163,
	title = {An impedance-based control architecture for multi-robot cooperative dual-arm mobile manipulation},
	booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems},
	year = {2013},
	address = {Tokyo},
	doi = {10.1109/IROS.2013.6696370},
	url = {;},
	author = {S. Erhart and D. Sieber and S. Hirche}
}
@conference {161,
	title = {Iterative optimal feedback control design under relaxed rigidity constraints for multi-robot cooperative manipulation},
	booktitle = {Proceedings of the 52nd IEEE Conference on Decision and Control (CDC)},
	year = {2013},
	address = {Florenz, Italien},
	abstract = {Cooperative manipulation of multiple robots presents an interesting control application scenario of coupled dynamical systems with a common goal. Here, we treat the problem of moving a formation of physically interconnected robots to a desired goal while maintaining the formation. This control problen is for example relevant in cooperative transport of an object from an initial to a final configuration by mobile robotic manipulators. To achieve the control goal we formulate an LQR-like optimal control problem that, in addition to goal regulation and minimization of input energy, includes the formation rigidity constraint in a relaxed form expressed as a biquadratic penalty term. The control problem is solved by two different iterative algorithms, a gradient descent using adjoint states and a quasi-Newton method, that determine a static linear state-feedback matrix. The proposed control design and the iterative algorithms are validated and compared in numerical simulations showing the efficacy of both approaches.},
	url = {;},
	author = {D. Sieber and F. Deroo and S. Hirche}
}
@inbook {NA:VK:DK:ISRR09,
	title = {Synthesis of Controllers to Create, Maintain, and Reconfigure Robot Formations with Communication Constraints},
	booktitle = {Robotics Research},
	series = {Springer Tracts in Advanced Robotics},
	volume = {70},
	year = {2011},
	pages = {625-642},
	publisher = {Springer Berlin / Heidelberg},
	organization = {Springer Berlin / Heidelberg},
	author = {Nora Ayanian and Kumar, Vijay and Koditschek, Daniel},
	editor = {Pradalier, C{\'e}dric and Siegwart, Roland and Hirzinger, Gerhard}
}
@conference {NA:PW:AH:MR08,
	title = {Stochastic Control for Self-Assembly of {XB}ots},
	booktitle = {ASME Mechanisms and Robotics Conference},
	volume = {2},
	year = {2008},
	month = {August},
	pages = {1169-1176},
	address = {New York, NY},
	author = {Nora Ayanian and Paul White and Adam Hal{\'a}sz and Mark Yim and Vijay Kumar}
}
